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  <div>In today's rapidly evolving AI landscape, AI systems often produce inconsistent, unpredictable outputs when multiple agents need to communicate with each other. When one agent's output becomes another's input, the lack of structure leads to cascading errors, misinterpretations, and unusable results. This guided project addresses this fundamental challenge by showing you how to enforce structured data exchanges between AI agents.<br /><br /></div><div>You'll learn how to ensure consistent, structured outputs from AI agents - a common challenge in AI development. Working with a research report scenario, you'll build a system that transforms unstructured AI responses into professional documents.<br /><br /></div><div>You'll design hierarchical data models with Pydantic as blueprints for your AI outputs, then create specialized CrewAI agents with distinct roles: a researcher, an analyst, and a formatter. You'll coordinate these agents in a workflow where each agent's output becomes structured input for the next, using data models to eliminate inconsistencies. By the end, you'll have a reusable framework for generating well-structured reports on any subject, automating what would normally take days of manual work into a process that produces professional results within hours.</div><div><br /><br /></div><h1>What You'll Learn</h1><div>By completing this lab, you will:</div><ul><li>Create structured data models using Pydantic to enforce consistency in LLM outputs</li><li>Design specialized AI agents with specific roles using CrewAI</li><li>Implement multi-stage workflows that transform research into organized documents</li><li>Build hierarchical data structures for proper validation and formatting</li><li>Generate professional reports with consistent formatting and citations</li><li>Apply data validation techniques to prevent incomplete LLM outputs</li></ul><div><br /></div><h1>Who should enroll</h1><div>This project is perfect for:</div><ul><li>AI developers looking to build more reliable agent-based systems</li><li>Data scientists who need to generate consistent reports from AI research</li><li>Software engineers interested in structured data handling with AI</li><li>Business analysts wanting to automate research and reporting workflows</li></ul><div><br /></div><h1>What you'll need</h1><div>Before beginning this guided project, you should have:</div><ul><li>Basic understanding of Python programming</li><li>Access to a modern web browser for the IBM Skills Network Labs environment</li></ul><div><br /></div><h1>Why enroll</h1><div>The ability to create AI systems that produce structured, consistent outputs is increasingly critical as organizations move AI from experimentation to production. By learning to enforce data models and orchestrate specialized agents, you'll gain valuable skills for building reliable AI applications that deliver professional results in research, content generation, and data analysis.</div><div><br /></div>
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Structuring LLM Outputs with CrewAI and Pydantic for Agents

Transform chaotic LLM outputs into consistent, professional documents by implementing structured data models with Pydantic and multi-agent workflows in CrewAI. This project teaches you to design specialized AI agents that collaborate to research topics and generate formatted reports, ensuring reliable outputs regardless of the subject matter. By creating a hierarchical data architecture that enforces validation and formatting rules, you'll learn to build AI systems that consistently deliver client-ready documents with proper structure, citations, and visual formatting.

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  <div>What separates sophisticated AI from basic models? The ability to critique and improve its own work. In this guided project, you'll learn how to implement reflection agents using LangGraph—AI systems that evaluate their outputs, identify weaknesses, and iteratively enhance their responses without human intervention.<br /><br /></div><div>You'll construct a complete workflow that mimics the human creative process: generate content, step back to evaluate it, then revise based on deliberate feedback. Working with a practical tweet generation example, you'll witness how reflection transforms basic AI responses into polished, engaging content through multiple refinement cycles.<br /><br /></div><div>This project reveals the architecture behind self-improving agentic AI systems, giving you hands-on experience with one of the most important advancements in modern AI development—the ability to implement artificial "System 2" thinking where deliberate reflection leads to superior results.<br /><br /></div><h1><strong>What you'll learn</strong></h1><div>After completing this project, you will be able to:</div><ul><li><strong>Design reflection workflows</strong> with LangGraph that enable AI to critique and improve its own outputs</li><li><strong>Implement generation, evaluation, and refinement stages</strong> in a connected agentic system</li><li><strong>Create feedback mechanisms</strong> that allow AI to objectively assess its content quality</li><li><strong>Build conditional logic</strong> to control when reflection cycles should continue or end</li><li><strong>Apply reflection principles</strong> to various AI applications beyond the project examples</li></ul><div><br /></div><h1><strong>Who this project is for</strong></h1><div>This project is perfect for:</div><ul><li><strong>Software developers</strong> seeking to build more sophisticated and reliable systems</li><li><strong>Data scientists</strong> working with large language models</li><li><strong>ML engineers</strong> interested in self-improving agentic AI applications</li><li><strong>Content creators</strong> looking to leverage AI with refinement capabilities</li></ul><div><br /></div><h1><strong>What you'll need</strong></h1><div>Before beginning this guided project, you should have:</div><ul><li>Basic understanding of Python programming</li><li>Familiarity with LLM concepts and applications</li><li>Access to a modern web browser for the IBM Skills Network Labs environment</li></ul><div><br /></div><h1><strong>Why enroll</strong></h1><div>By the end of this project, you'll possess the skills to build AI systems that think twice—generating content and then critically reviewing it before delivering final results. This reflection capability is the key difference between basic AI applications and truly intelligent systems. Whether you're developing content generators, virtual assistants, or analytical tools, incorporating reflection mechanisms with LangGraph dramatically improves output quality and reliability. Join this project to learn how to transform one-shot AI responses into thoughtful, refined content through the power of self-critique and iteration.</div>
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Make Your AI Agents Smarter with Reflection in LangGraph

Build AI Agents that reason, critique their own work, and iteratively improve their responses just like a human. This project teaches you to build a tweet optimization workflow where content improves with each iteration, just like human editing. In just 45 minutes, master the principles of reflection agents that can recognize their own limitations and actively improve their responses—an essential skill for creating more intelligent, agentic AI systems.

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  <div>Language models become dramatically more powerful when they can interact with external services and data. In this hands-on guided project, you'll learn how to build this critical bridge between AI and the digital world through custom tool development.<br /><br /></div><div>Using YouTube content analysis as our example, you'll create specialized tools that enable language models to perform actions they couldn't accomplish alone. You'll build functions that extract video IDs, fetch transcripts, search for content, retrieve metadata, and access trending videos—giving your AI assistant comprehensive media analysis capabilities.<br /><br /></div><div>The project focuses on manual tool calling—controlling how LLMs select and execute tools. You'll learn how language models decide which tools to use, how to extract their requests, execute the appropriate functions, and return results in the expected format. This understanding is essential for debugging, customization, and building reliable AI systems.</div><div><br /></div><h1><strong>What you'll learn</strong></h1><div>After completing this project, you will be able to:</div><ul><li>Design custom tools with proper documentation, type hints, and error handling</li><li>Understand how LLMs select appropriate tools based on user requests</li><li>Extract tool call information from LLM responses</li><li>Execute tool calls manually with correct parameter formatting</li><li>Structure and manage conversations that include multiple tool calls</li><li>Build organized tool mappings for complex applications</li><li>Implement chains that handle multi-step tool execution workflows</li></ul><div><br /></div><h1><strong>Who should enroll</strong></h1><div>This project is perfect for:</div><ul><li>AI developers seeking to extend language model capabilities</li><li>Software engineers building LLM-powered applications</li><li>Data scientists interested in programmatic content analysis</li><li>Technology professionals implementing practical AI solutions</li><li>Content creators looking to automate YouTube data collection</li></ul><div><br /></div><h1><strong>What you'll need</strong></h1><div>Before beginning this guided project, you should have:</div><ul><li>Basic understanding of Python programming</li><li>Familiarity with language models and their capabilities</li><li>Access to a modern web browser for the IBM Skills Network Labs environment</li><li>Optional: Prior experience with LangChain is helpful but not required</li></ul><div><br /></div><h1><strong>Why enroll</strong></h1><div>By the end of this project, you'll understand the mechanics behind today's most advanced AI assistants. The ability to create custom tools and control their execution is what separates basic chatbots from powerful AI systems that can search databases, analyze content, and interact with external services.<br /><br /></div><div>As organizations increasingly adopt AI solutions, the demand for developers who understand tool integration continues to grow. Whether you're building research assistants, content analysis tools, or customer service agents, mastering custom tool development will enable you to create AI applications that deliver real-world value by combining the reasoning capabilities of language models with access to specialized functions and external data sources.</div>
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Build and Execute Your Own Tools for LLMs

Connect language models to the digital world by building tools that serve as their hands and eyes. This project shows you how to create specialized functions that let LLMs interact with YouTube videos and external services. You'll master the process of designing tools and controlling their execution through manual tool calling. Learn to build functions that extract video IDs, fetch transcripts, and analyze trending content. In just 1 hour, gain the expertise to create AI systems that search and analyze video content and understand the mechanics behind today's most powerful AI assistants.

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  <div>Learn to implement agentic workflows with LangGraph and discover how to build intelligent AI agents capable of making dynamic, context-aware decisions. This guided project will walk you through the core components of LangGraph, including nodes, states, edges, and conditional edges, to help you create flexible and autonomous systems. You’ll learn how to design and implement sophisticated workflows, such as user authentication systems, that adapt to different scenarios based on success, failure, or conditional logic.  <br /><br /></div><div>Through hands-on exercises, you’ll gain a deep understanding of how each component works and how to connect them to design seamless decision-making workflows. Whether you’re managing state transitions, setting up dynamic conditions, or creating failure-handling mechanisms, this project will empower you to create advanced AI systems with high levels of flexibility and autonomy.<br /><br /></div><div><strong>What You’ll Learn<br /></strong><br /></div><ul><li><strong>LangGraph Essentials</strong>: Understand the key components—nodes, states, edges, and conditional edges—that power decision-making in AI systems.</li><li><strong>Dynamic Workflows</strong>: Learn how to build user authentication systems and other workflows with flexible decision logic, tailored to different user states and conditions.</li><li><strong>State-driven Logic</strong>: Explore how to manage state transitions and conditional logic to ensure smooth execution of tasks, handling both success and failure scenarios.</li><li><strong>Building Autonomous Systems</strong>: Develop skills to create autonomous agents that adapt based on real-time inputs, making intelligent decisions that reflect changing conditions.</li></ul><div><br /></div><div><strong>Why It Matters</strong><br /> LangGraph represents a powerful framework for building AI agents with robust decision-making capabilities. By mastering LangGraph, you will:</div><ul><li><strong>Design Flexible Workflows</strong>: Create intelligent systems that react and adapt to user input and evolving scenarios.</li><li><strong>Enable Autonomous Systems</strong>: Build systems capable of acting independently, managing success, failure, and conditions based on real-time data.</li><li><strong>Enhance Decision-making</strong>: Make data-driven decisions by integrating dynamic decision logic and understanding the flow of information between nodes, states, and edges.</li></ul><div><br /></div><div><strong>Who Should Register</strong><br /> This guided project is ideal for:</div><ul><li><strong>Developers</strong>: Those interested in building intelligent AI systems that require dynamic decision-making.</li><li><strong>AI Enthusiasts</strong>: Anyone looking to deepen their understanding of AI workflows and how to build flexible, autonomous systems.</li><li><strong>Software Engineers</strong>: Developers working on user authentication or similar workflows that require decision-based logic and state management.</li></ul><div><br /></div><div><strong>What You’ll Need</strong><br /> Before starting this project, ensure you have the following:</div><ul><li><strong>Basic Programming Knowledge</strong>: Familiarity with basic programming concepts will be helpful, especially in Python.</li><li><strong>Understanding of Decision-making</strong>: While prior experience with decision-making workflows is a plus, it’s not required.</li><li><strong>A Computer with a Modern Browser</strong>: Chrome, Edge, Firefox, or Safari to run and interact with the project tools.</li></ul><div><br /></div><div><strong>What You’ll Build</strong><br /> By the end of this guided project, you will have:</div><ul><li><strong>Mastered LangGraph</strong>: A deep understanding of how to build and connect the essential components of LangGraph for creating AI workflows.</li><li><strong>Built Dynamic Workflows</strong>: Created user authentication workflows that handle success and failure scenarios and adapt to changing conditions.</li><li><strong>Developed Autonomous Systems</strong>: Gained hands-on experience in designing systems that make real-time decisions based on data inputs and conditions.</li></ul><div><br /></div><div>Embark on this exciting journey with LangGraph to build intelligent AI agents that think, adapt, and make decisions in real-time. Elevate your AI development skills and start building dynamic decision-making systems today!</div>
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LangGraph 101: Learn to Build Agentic Workflows

Create agentic workflows with LangGraph by learning key components like nodes, states, edges, and conditional edges through a user authentication use case. Learn how to design AI workflows that adapt to real-time inputs and manage state-driven logic effectively. You’ll explore how to code success and failure scenarios, build dynamic decision-making systems, and create workflows tailored to diverse conditions. Perfect for developers and AI enthusiasts, this project focuses on equipping you with practical skills to design flexible, autonomous AI systems for real-world applications. 

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  <div><strong>Imagine having a data analyst at your fingertips</strong> who understands natural language and can perform complex analyses without requiring you to write a single line of code. What if you could simply ask questions about your data and receive immediate insights, visualizations, and even predictive models? This is the power of combining <strong>language models with specialized data science tools</strong>.</div><div><br />In this hands-on lab, you'll build a conversational AI system that makes data science accessible to everyone in your organization—from marketing specialists analyzing customer segments to executives exploring sales trends, all without technical barriers.<br /><br /></div><h1>Project Overview</h1><div>This lab teaches you to create an intelligent assistant that handles the entire data workflow through natural language requests:</div><div>1️⃣ <strong>Data Discovery &amp; Exploration</strong> - Automatically identify available datasets, summarize their structure, and provide quick statistics<br />2️⃣ <strong>Analysis &amp; Visualization</strong> - Transform requests like "Show the relationship between customer age and spending" into visualizations<br />3️⃣ <strong>Predictive Modeling</strong> - Train and evaluate machine learning models through conversation<br />4️⃣ <strong>Context Management</strong> - Maintain conversation history to enable follow-up questions and complex analyses</div><div><br />By connecting specialized LangChain tools through an agent executor, you'll create a seamless experience where users can move from raw data to actionable insights using only conversation.<br /><br /></div><h1>What You'll Learn</h1><div>By completing this lab, you will:</div><ul><li>Design effective <strong>tool-based agents</strong> that reason about when to use specific data functions</li><li>Build a collection of <strong>specialized tools</strong> for different data science tasks</li><li>Create <strong>multi-step workflows</strong> that maintain context across analyses</li><li>Implement <strong>natural language interfaces</strong> to technical data processes</li><li>Learn how to handle ambiguous user requests with <strong>clarification techniques</strong></li></ul><div><br /></div><h1><strong>Who Should Do This Lab</strong></h1><div>This project is ideal for:</div><ul><li><strong>Developers</strong> looking to build practical AI applications with immediate business value</li><li><strong>Data scientists</strong> wanting to make their expertise available to non-technical colleagues</li><li><strong>Technical leaders</strong> exploring how AI can democratize data-driven decision making</li><li><strong>AI enthusiasts</strong> interested in creating assistants that solve real-world problems</li></ul><div>No advanced ML expertise required—basic Python knowledge and curiosity about AI applications are all you need.<br /><br /></div><h1>What You Need</h1><div>✅ <strong>A browser</strong> to access the lab environment<br />✅ <strong>Basic Python knowledge</strong> (understanding functions and data structures)<br />✅ <strong>Familiarity with data concepts</strong> (CSV files, simple statistics, basic ML terms)</div><div><br />By the end of this project, you'll have built an AI assistant that transforms how people interact with data—enabling anyone to ask questions and receive insights without writing code.</div>
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Build a LangChain-based AI-powered Data Analysis Assistant

Learn LangChain tools and agents while developing a data science assistant to automate tasks from exploring CSVs to training ML models. This project guides you through creating a natural language interface that makes data analysis accessible to non-technical users.  Build specialized tools that handle everything from listing available datasets to generating statistics and evaluating machine learning models. Learn how AI agents interpret user requests, select appropriate tools, and execute complex data workflows through conversation. 

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  <div>As businesses are increasingly moving toward data-driven decision-making, the ability to derive meaningful insights from raw data is becoming all the more essential. The IBM Data Analyst Capstone Project gives you the opportunity to apply the skills and techniques learned throughout the IBM Data Analyst Professional Certificate. You will be working on a project simulating a real-life scenario with actual datasets. The project would involve carrying out tasks commonly performed by professional data analysts, such as data collection from multiple sources, data wrangling, exploratory analysis, statistical analysis, data visualization, and creating interactive dashboards.  <br /><br />Throughout the project, you will demonstrate your proficiency in tools such as Jupyter Notebooks, SQL, and Relational Databases Management System (RDBMS), and Business Intelligence (BI) tools like IBM Cognos Analytics. You will also apply Python libraries, including Pandas, Numpy, Scikit-learn, Scipy, Matplotlib, and Seaborn. <br /><br />Your final deliverable will be a comprehensive data analysis report that includes an executive summary, detailed insights, and a conclusion for organizational stakeholders. <br /><br />We recommend completing the previous courses in the Professional Certificate before starting this capstone project, as it integrates all key concepts and techniques into a single, real-world scenario. <br /><br /></div><h1>Course Learning Objectives</h1><ul><li>Apply techniques to gather and wrangle data from multiple sources. </li><li>Analyze data to identify patterns, trends, and insights through exploratory techniques. </li><li>Create visual representations of data using Python libraries to communicate findings effectively. </li><li>Construct interactive dashboards with BI tools to present and explore data dynamically. </li></ul><h1>Course Syllabus</h1><div><strong>Welcome to the Course</strong></div><ul><li>Video: Course Introduction</li><li>Reading: General Information: About the course</li><li>Reading: Course Learning Objectives and Course Syllabus</li><li>Video: Project Overview</li><li>Reading: Project Scenario</li><li>Reading: Grading Scheme</li></ul><div><strong>Module 1: Data Collection<br />Collecting Data Using APIs</strong></div><ul><li>Reading: Module 1 Introduction and Learning Objectives</li><li>(Optional) Lab 1: Review Of Accessing APIs</li><li>Reading: API Access Details </li><li>Lab 2:  Collecting Data Using APIs</li><li>Checklist: Collecting Data Using APIs</li></ul><div><strong>Collecting Data Using Webscraping</strong></div><ul><li>Lab 3: Review Of Web Scraping</li><li>Lab 4: Collecting Data Using Web Scraping</li><li>Checklist: Collecting Data Using Webscraping</li></ul><div><strong>Exploring Data</strong></div><ul><li>Reading: About the Dataset</li><li>Lab 5: Exploring the Dataset</li><li>Checklist:  Exploring Data</li><li>Graded Quiz: Data Collection</li></ul><div><strong>Module 2: Data Wrangling</strong></div><div><strong>Finding Duplicates</strong></div><ul><li>Reading: Module 2 Introduction and Learning Objectives</li><li>Reading: Assignment Overview</li><li>Lab 6: Finding Duplicates</li><li>Checklist: Finding Duplicates</li></ul><div><strong>Removing Duplicates</strong></div><ul><li>Lab 7: Removing Duplicates</li><li>Checklist: Removing Duplicates</li></ul><div><strong>Finding Missing Values</strong></div><ul><li>Lab 8: Finding Missing Values</li><li>Checklist: Finding Missing Values</li></ul><div><strong>Imputing Missing Values</strong></div><ul><li>Lab 9: Impute Missing Values</li><li>Checklist: Imputing Missing Values</li></ul><div><strong>Normalizing Data</strong></div><ul><li>Lab 10: Normalizing Data</li><li>Checklist: Normalizing Data</li></ul><div><strong>Assignment Overview</strong></div><ul><li>Lab 11: Data Wrangling</li><li>Checklist: Data Wrangling</li><li>Graded Quiz: Data Wrangling</li></ul><div><strong>Module 3: Exploratory Data Analysis</strong></div><div><strong>Assignment Overview</strong></div><ul><li>Reading: Module 2 Introduction and Learning Objectives</li><li>Reading: Assignment Overview</li><li>Lab 12: Exploratory Data Analysis</li><li>Checklist: Exploratory Data Analysis</li></ul><div><strong>Analyzing the data distribution</strong></div><ul><li>Lab 13: Finding How The Data Is Distributed</li><li>Checklist: Analyzing the data distribution</li></ul><div><strong>Handling Outliers</strong></div><ul><li>Lab 14: Finding Outliers</li><li>Checklist: Handling Outliers</li></ul><div><strong>Correlation</strong></div><ul><li>Lab 15: Finding Correlation</li><li>Checklist: Correlation</li><li>Graded Quiz: Exploratory Data Analysis</li></ul><div><strong>Module 4: Data Visualization</strong></div><div><strong>Assignment Overview</strong></div><ul><li>Reading: Module 4 Introduction and Learning Objectives</li><li>ReadIng: Assignment Overview</li><li>Lab 16: Data Visualization</li><li>Checklist: Data Visualization</li></ul><div><strong>Visualizing Distribution Of Data</strong></div><ul><li>Lab 17: Histograms</li><li>Lab 18: Box Plots</li><li>Checklist: Visualizing Distribution Of Data</li></ul><div><strong>Visualizing Relationship</strong></div><ul><li>Lab 19: Scatter Plot</li><li>Lab 20: Bubble Plots</li><li>Checklist: Visualizing Relationship</li></ul><div><strong>Visualizing Composition of Data</strong></div><ul><li>Lab 21: Pie Charts</li><li>Lab 22: Stacked Charts</li><li>Checklist: Visualizing Composition of Data</li></ul><div><strong>Visualizing Comparison of Data</strong></div><ul><li>Lab 23: Line Charts</li><li>Lab 24: Bar Charts</li><li>Checklist:  Visualizing Comparison of Data</li><li>Graded Quiz: Data Visualization</li></ul><div><strong>Module 5: Building A Dashboard</strong></div><div><strong>Assignment Overview</strong></div><ul><li>Reading: Module 5 Introduction and Learning Objectives</li><li>Reading: Assignment Overview</li></ul><div><strong>Dashboards</strong></div><ul><li>Lab 25: Option A - Building A Dashboard With IBM Cognos Analytics</li><li>Lab 26: Option B - Building A Dashboard With Google Looker Studio</li><li>Checklist: Dashboards</li><li>Graded Quiz: Building a Dashboard</li></ul><div><strong>Module 6: Final Assignment: Present Your Findings</strong></div><div><strong>How To Present Your Findings</strong></div><ul><li>Reading: Module 6 Introduction and Learning Objectives</li><li>Video: Elements Of A Successful Data Findings Report</li><li>Lab: Structure Of A Report</li><li>Video: Best Practices For Presenting Your Findings</li><li>(Optional)  Lab 27: Getting Started With PowerPoint For The Web</li><li>(Optional)  Lab 28: Basics of PowerPoint</li><li>(Optional) Lab 29: Save your PowerPoint Presentation as PDF</li></ul><div><strong>Final Presentation</strong></div><ul><li>Reading: Assignment Overview</li><li>Exercise: Preparing Your Presentation</li><li>Peer-graded Assignment</li></ul><div><strong>Course Wrap Up</strong></div><ul><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li><li>Copyrights and Trademark</li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>This course requires you to be proficient in data analysis and have experience using SQL, Relational Databases, performing data collection, data wrangling, data analysis, &amp; data visualization with Python libraries, and using a BI tool like IBM Cognos Analytics or Google Looker. I</div>
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IBM Data Analytics and Visualization Capstone Project

Showcase your data analysis skills to prospective employers by carrying out common tasks performed by data analysts on a data set simulating a real-life business scenario and creating a comprehensive data analysis report.  

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  <div>"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large data sets. Data visualization plays an essential role in the representation of both small and large scale data.</div><div>One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions.</div><div>The main goal of this course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using the open source language R. </div>
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Data visualization with R

Data visualization is the presentation of data with graphics. It's a way to summarize your findings and display it in a form that facilitates interpretation and can help in identifying patterns or trends. In this course you will learn how to create beautiful graphics and charts, customizing the look and feel of them as you wish.

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  <div>​Selecting the appropriate AI model for your tasks can be challenging, especially when deciding between standard and reasoning modes. Anthropic's Claude 3.7 Sonnet offers a unique solution with its hybrid reasoning capabilities, allowing users to tailor the model's behavior to specific needs.<br /><br />By integrating Claude into your development environment, you can create a range of applications, including <strong>creative content generation, coding assistance, and analytical tasks</strong>. Claude's adaptability ensures that responses are both contextually appropriate and efficient, enhancing user engagement across various platforms.</div><div><br />Furthermore, Claude's "extended thinking" mode enables detailed, step-by-step reasoning, making it particularly effective for tasks requiring in-depth analysis. This feature enhances Claude's utility in solving complex problems and performing intricate tasks, providing users with a versatile and powerful AI tool.</div><div><br />By the end of this lab, you will have a solid understanding of how to effectively use Claude's API, design prompts that maximize AI performance, and choose between reasoning and non-reasoning models based on task requirements.</div><h1>A Look at the Project Ahead</h1><div>After completing this lab you will be able to:</div><ul><li>Learn how to set up and use Anthropic's Claude API. ​</li><li>Develop skills in prompt engineering to elicit high-quality outputs from AI models.</li><li>Understand the differences between Claude's reasoning capabilities and standard models, and learn to select the appropriate model based on specific task requirements. ​</li><li>Understand how to adjust Claude's "thinking budget" and other parameters to balance response quality and processing efficiency according to your application's needs.</li></ul><h1>What You'll Need</h1><div>To get started with this guided project, you should have:</div><ul><li>Access to modern web browsers like Chrome, Edge, Firefox, Internet Explorer, or Safari.</li></ul>
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Explore Reasoning v/s Non-Reasoning Models with Claude API

Use Anthropic's API to experiment with Claude Sonnet's reasoning and non-reasoning capabilities. By utilizing Anthropic's API, you can tailor Claude's behavior to align with your specific needs, whether it's for generating creative content, solving complex coding challenges, or engaging in detailed analytical tasks. This adaptability makes Claude a powerful tool for a wide range of applications, enhancing both productivity and the quality of AI-generated outputs. By experimenting with both models, we aim to identify key differences and determine which model is best suited for specific tasks.

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  <div>Imagine a world where patients can receive <strong>instant, AI-powered medical assistance</strong> anytime, anywhere. Whether it’s a late-night health concern, a follow-up on medication, or general health advice, <strong>AutoMed</strong> steps in as your intelligent virtual healthcare assistant. No more long waiting times, delayed responses, or unreliable online searches—AutoMed provides <strong>real-time, accurate, and personalized</strong> medical support tailored to each individual’s needs.</div><div><br />AutoMed is not just an ordinary chatbot—it’s a <strong>multi-agent AI system powered by AG2 (AutoGen)</strong>, designed to <strong>simulate expert medical consultation</strong> through intelligent collaboration. Instead of relying on a single AI model, <strong>AutoMed orchestrates multiple specialized agents</strong>, each dedicated to a specific task, ensuring <strong>comprehensive, accurate, and real-time</strong> medical guidance. With its <strong>adaptive intelligence and multi-agent communication</strong>, AutoMed delivers <strong>human-like, context-aware conversations</strong> that go beyond basic symptom checkers.<br /><br /><strong>Disclaimer</strong>: This guided project is designed to introduce learners to AG2 (AutoGen). The medical advice provided should not be considered a substitute for professional medical consultation, diagnosis, or treatment. Always seek guidance from a qualified healthcare professional.</div><h1>A Look at the Project Ahead</h1><div>After completing this lab you will be able to:</div><ul><li>Learn how <strong>AG2</strong> enables <strong>multi-agent AI systems</strong> for complex workflows.</li><li>Explore how <strong>AG2 integrates with LLMs</strong> like GPT-4 for dynamic AI-driven conversations.</li><li>Implement <strong>agent-to-agent communication</strong> for <strong>intelligent medical decision-making</strong>.</li><li>Develop <strong>multiple AI agents</strong> that interact and collaborate to handle different healthcare tasks.</li></ul><h1>What You'll Need</h1><div>The IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save the  hassle of setting everything up. This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.</div>
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Build MultiAgent Chatbot with AG2 (AutoGen) for Healthcare

Learn to build AI agents using AG2 (AutoGen) multi-agent framework. In this project you will create AutoMed Multi LLM Agent for health treatment recommendations. It leverages Large Language Models (LLMs) to analyze user symptoms, suggest treatments, and access medical databases for accurate insights. You will build multiple AI agents that interact with each other, collaborate on complex medical queries, and automate healthcare assistance. These agents can coordinate tasks, fetch real-time medical data, and provide context-aware responses for better decision-making.

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  <div>Discover how generative AI can help you create engaging, personalized learning content and improve your productivity so you can enhance student outcomes. <br /><br /></div><div>During this course, you’ll explore gen AI methodologies and best practices for implementing AI in education. You’ll look at how it can be used to create content, enhance personalized learning, special education, and adaptive learning. You’ll get hands-on with popular gen AI tools, including IBM watsonx, ChatGPT, Claude, and Khanmigo in education. Plus, you’ll investigate the ethical implementation of gen AI, considering privacy protection, bias mitigation, and the creation of inclusive learning environments.   <br /><br /></div><div>Throughout, you’ll hear from industry experts, who share insights on the future of gen AI in education. You’ll build your gen AI expertise in hands-on labs and interactive activities. Plus, you’ll complete a final project where you’ll apply all that you’ve learned to a practical, real-world scenario.  <br /><br /></div><div>Whether you’re an educator looking to revolutionize your classroom practices or simply want to streamline ad<br />ministrative tasks, this course will give you the gen AI skills you need to excel. Enroll today! <br /><br /><strong>Prerequisites</strong> </div><div> </div><div>This course is designed for educators who already have an educational or teaching background and want to enhance their teaching capabilities using gen AI. You should have a fundamental understanding of gen AI and instructional design. The following courses in the <strong>Generative AI for Educators</strong> specialization can help you become familiar with gen AI basics and prompt engineering techniques: </div><div> </div><ul><li><a href="https://www.coursera.org/learn/generative-ai-introduction-and-applications?specialization=generative-ai-for-everyone">Course 1: Generative AI: Introduction and Applications</a> </li><li><a href="https://www.coursera.org/learn/generative-ai-prompt-engineering-for-everyone?specialization=generative-ai-for-everyone">Course 2: Generative AI: Prompt Engineering Basics</a> </li></ul><div> </div><div><strong>Objectives</strong> </div><div>After completing this course, you will be able to: </div><div> </div><ul><li>Identify appropriate gen AI tools for education, considering pedagogy, integration ease, and cost, to address specific teaching needs </li><li>Align the content with learners’ abilities, preferences, and engagement goals to create personalized learning content </li><li>Leverage gen AI tools to design lesson plans and assessments to encourage dynamic and interactive learning </li><li>Apply ethical AI practices by safeguarding data privacy, mitigating bias, and promoting an inclusive learning environment </li></ul><div> </div><div> </div><div><strong>Course Outline</strong> </div><div> </div><div>This course is divided into three modules. You can complete each module at your own pace, but it’s recommended to set a goal of completing at least one module per week.   </div><div> </div><div><strong>Module 1: Gen AI Tools and Personalized Learning </strong> <br /> </div><div>In this module, you will explore the landscape of educational gen AI tools and select them based on pedagogical needs, integration capabilities, and cost considerations. You will also learn to evaluate the effectiveness of AI tools for different teaching requirements and identify principles for AI-enhanced personalized learning and advanced content customization techniques. Finally, you will be able to leverage AI-powered tools for content creation and implement strategies for differentiating instruction using AI-powered resources.  </div><div> </div><div> </div><div> </div><div><strong>Module 2: AI Integration and Performance Assessment</strong> <br /> </div><div>This module introduces you to how to integrate AI tools seamlessly into teaching practices and leverage them for learners’ evaluation. You will gain insight into designing AI-enhanced lesson planning aligning them with learning objectives. Additionally, you will learn to create assessment strategies using gen AI tools to gauge learners’ mastery of concepts and analyze AI-generated performance data. Finally, you will be able to leverage AI-powered feedback mechanisms to provide timely, data-driven insights that support learners’ growth and continuous improvement.  </div><div> </div><div> <br /><strong>Module 3: Ethical Implementation and Applied Project</strong> </div><div> </div><div>This module helps address ethical considerations while implementing gen AI in education, recognize bias, and develop its mitigation strategies. You will also learn to design data privacy protection strategies for AI integration. </div><div> </div><div>This module includes storyline interactivity based on the ethical guidelines while implementing gen AI in education.  </div><div> </div><div>The module includes a final project based on the concepts covered in the course. In this module, you will attempt a graded quiz to test and reinforce your understanding of concepts. The module also includes a glossary to enhance comprehension of generative AI-related terms.   <br /> <br /> </div><div> </div><div><strong>Tools/Software</strong> </div><div>In this course, you will explore no-cost versions of various gen AI tools and platforms, including: </div><div> </div><ul><li>Microsoft’s Copilot </li><li>OpenAI’s ChatGPT – A versatile AI language model for creating lesson plans, answering learners’ queries, and personalized tutoring  </li><li>Anthropic Claude – An advanced conversational AI model for generating insightful educational content and assisting in curriculum design </li><li>Khanmigo Tools for Teachers – A suite of AI-powered tools designed to assist teachers in lesson planning, curriculum alignment, and classroom management </li><li>TeachFlow – A tool for streamlining teaching workflows, providing AI-driven assistance for lesson planning and classroom management </li><li>Synthesia – A tool for creating engaging animated videos  </li></ul><div> </div><div>These tools are accessible via modern web browsers such as Chrome, Firefox, and Edge. Learners are not required to install these tools on their devices. </div><div> </div><div><strong>Tips for Success</strong> </div><ul><li>Stay organized and keep track of teaching strategies and learning requirements </li><li>Consistently practice using the gen AI tools and technologies covered in the course </li><li>Experiment with the examples provided to enhance your understanding and application of AI in education </li></ul><div> </div><div>Congratulations on taking this step to elevate your teaching career with gen AI! Enjoy your learning journey. </div>
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Generative AI: Empowering Modern Education

This course is for education professionals, including teachers, professors, instructional designers, education administrators, EdTech professionals, corporate trainers, education policymakers, online course creators, and consultants who want to advance their careers by leveraging the power of generative AI.  

<div>Imagine you're applying for a mortgage to buy your dream home. After weeks of gathering documents and waiting anxiously, you receive a simple rejection letter with no explanation. You're left wondering: <strong>What went wrong? Was it your income, your down payment, or something in your credit history?</strong> Without clear reasons, you have no idea what to fix before trying again. This is where <strong>Explainable AI (XAI)</strong> makes a difference—transforming vague rejections into specific, actionable feedback that gives you a clear path forward.<br><br>As AI becomes increasingly embedded in critical decision-making processes across industries, the ability to understand why and how models reach specific conclusions is essential for building trust, ensuring fairness, and meeting regulatory requirements.<br><br>Here's a glimpse into the <strong>IBM's AI Explainability 360 (AIX360)</strong> decision framework you'll master in this learning path—helping you navigate which explainability techniques work best for different scenarios<br><br><figure data-trix-attachment="{&quot;contentType&quot;:&quot;image/png&quot;,&quot;filename&quot;:&quot;methods-choice.png&quot;,&quot;filesize&quot;:164704,&quot;height&quot;:1362,&quot;sgid&quot;:&quot;eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJalJuYVdRNkx5OXdiM0owWVd4ekwwRmpkR2wyWlZOMGIzSmhaMlU2T2tKc2IySXZOemdfWlhod2FYSmxjMTlwYmdZNkJrVlUiLCJleHAiOm51bGwsInB1ciI6ImF0dGFjaGFibGUifX0=--c4a675b2462a5482e72c3ee025f577e1a73da307&quot;,&quot;url&quot;:&quot;https://latest-catalog.skillsnetwork.site/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBVdz09IiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--a99a9c540b33c9edec72f28d5d853169efc695ae/methods-choice.png&quot;,&quot;width&quot;:2048}" data-trix-content-type="image/png" data-trix-attributes="{&quot;presentation&quot;:&quot;gallery&quot;}" class="attachment attachment--preview attachment--png"><img src="https://latest-catalog.skillsnetwork.site/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBVdz09IiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--a99a9c540b33c9edec72f28d5d853169efc695ae/methods-choice.png" width="2048" height="1362"><figcaption class="attachment__caption"><span class="attachment__name">methods-choice.png</span> <span class="attachment__size">160.84 KB</span></figcaption></figure><br><br>The XAI methodology flowchart illustrates how to choose the right explainability approach based on your specific needs. Don't worry if you find this confusing at first—you'll understand these concepts step by step as you progress through the learning path. The framework starts with a fundamental decision: Do you need to understand the data or the model? Then it guides you through choosing between sample-based or feature-based explanations, local explanations (for individual predictions) or global explanations (for the entire model). The flowchart shows various techniques available in the AIX360 toolkit, including ProtoDash for prototype-based explanations, SHAP for feature importance, TED for user-friendly explanations, and BRCG for directly interpretable models.<br><br>By completing this learning path, you'll gain practical experience with diverse explainable AI techniques using <strong>IBM's AI Explainability 360 (AIX360) </strong>open-source toolkit. Through four guided projects across different domains, you'll develop skills to create AI systems that are not only accurate but also transparent and trustworthy—expertise that's increasingly valuable as organizations face growing demands for responsible AI from regulators, customers, and internal stakeholders.</div><div><br></div><ul><li><strong>Employee Retention Analysis</strong> - Identify factors influencing whether employees stay or leave</li><li><strong>Credit Approval Decisions</strong> - Create transparent explanations for financial decisions</li><li><strong>Housing Market Predictions </strong>- Build interpretable rule-based models for property valuations</li><li><strong>Student Performance Prediction</strong> - Generate representative profiles explaining academic outcomes</li></ul><div><br>This expertise is increasingly valuable as organizations face growing demands for responsible AI. Whether enhancing existing models or building new systems with explainability by design, this learning path provides the foundation to implement XAI effectively in your work.</div>

Master Explainable AI using AIX360

Learn practical techniques on how to make AI systems transparent, explainable, and trustworthy with IBM's AIX360 library. 

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  <div>This comprehensive course is designed for aspiring and current Product Owners aiming to deepen their understanding of Scrum practices and enhance their Agile expertise. It discusses the Product Owner's role, collaborating effectively with project sponsors, and creating key Scrum artifacts. Through hands-on activities, you will develop initial artifacts, such as user stories and product backlogs, while mastering the essential Agile ceremonies, including kickoffs and sprint planning.<br /><br /></div><div>The course then explores the ways Product Owners manage sprints, prioritize product backlogs, and resolve roadblocks. You will learn about the creation of Sprint goals and acceptance criteria, as well as how to ensure value delivery and stakeholder alignment. You’ll learn about Agile retrospectives and continuous improvement strategies. You’ll also explore the ways Product Owners use tools and methods to balance Agile principles with practical documentation, including innovations such as AI in product management.<br /><br /></div><div>This course includes hands-on projects that allow you to apply your learning to a real-world scenario. You will calculate performance metrics, complete a sprint retrospective, and present findings to stakeholders. </div><div>Additionally, you will be guided through building an Agile artifact portfolio, preparing for certification, and optimizing your job search strategies.</div><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Product Owner and Scrum: The Kickoff</strong>: </div><ul><li><strong>Welcome to the Course</strong></li><li> <strong>Product Owner Role Fundamentals</strong></li><li> <strong>Module Summary and Assessment</strong></li></ul><div><strong>Module 2: Delivering Value to the Customer</strong></div><ul><li>Managing Sprints</li><li>Overcoming Issues and Roadblocks</li><li>Module Summary and Assessment</li></ul><div><strong>Module 3: Continuous Improvement</strong></div><ul><li>MVP for AI Features</li><li>Managing continuous improvement across the Agile process</li><li>Module Summary and Assessment</li></ul><div><strong>Module 4: Final Project and Assessment</strong></div><ul><li>Final Project Submission</li><li>Final Graded Quiz</li><li>Course Wrap-Up</li></ul><div><strong>Module 5: Certifications, Portfolio Development, and Job Readiness</strong></div><ul><li>Product Owner Certifications</li><li>Your Personal Portfolio and Job Search Guidance</li></ul><h1><strong>Course Learning Objective</strong></h1><ul><li>Apply product owner skills learned from all courses in the Product Owner professional certificate</li><li>Analyze a series of Agile scenarios and create key scrum artifacts and deliverables</li><li>Demonstrate the product owner’s role in each of the Agile ceremonies by working through a simulated Scrum project</li><li>Identify the Product Owner’s role in communication and collaboration among all stakeholders throughout the simulated Scrum project</li><li>Create summary documentation for the Sprint Retrospective based on the scenarios provided</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Prior knowledge of Product Owner skills, tools, communication, stakeholder management, Agile, Scrum, and IT fundamentals is essential.</div>
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Product Owner Capstone

This course provides an opportunity for you to apply the Product Owner knowledge and skills learned throughout all prior courses in the Product Owner Professional Certificate (PC) program. This course will reinforce your preparation for the Professional Scrum Product Owner (PSPO) or other Product Owner certifications.  We encourage you to complete all courses in this Professional Certificate series before beginning this final course. You will be provided with a real-world Agile project scenario and numerous hands-on activities to further develop your Product Owner skills and experience. 

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  <div><br />Course Introduction <br /><br /></div><div>Elevate your career prospects with this capstone project, designed to help you showcase job-ready business analysis skills in a real-world scenario. <br /><br /></div><div>This hands-on experience allows you to apply business analysis concepts and techniques through a fictitious case study, Enhancing Operational Efficiency in a Multi-Specialty<strong> </strong>Hospital<strong>. </strong>While working on this project, you’ll apply key business analysis<strong> </strong>skills<strong>, including requirements gathering</strong>, <strong>stakeholder analysis</strong>, <strong>project management</strong>, <strong>process modeling</strong>, and <strong>risk assessment</strong>. Additionally, you’ll use tools like <strong>Excel</strong> for <strong>data analysis</strong> and <strong>Cognos</strong> or <strong>Google Looker</strong> for creating impactful <strong>dashboards</strong>. </div><div> </div><div>This project offers an opportunity to design key artifacts integral to a business analysis project, including a <strong>Business Requirements Document (BRD)</strong>, <strong>Requirements Traceability Matrix (RTM)</strong>, <strong>Stakeholder Analysis and Engagement Plan</strong>, <strong>Process Model Diagram</strong>, <strong>Scope Management Plan</strong> and <strong>Risk Mitigation Plan</strong>. <br /><br /></div><div>To conclude the project, you'll design a professional PowerPoint presentation summarizing your findings, sharpening your analytical and presentation skills. <br /><br /></div><div>By completing this project, you’ll refine your critical thinking and problem-solving abilities and business analysis skills that stand on your resume. Be prepared to power up your business analysis career! <br /><br /></div><div> <br /><br /></div><div><br />Course Objectives  <br /><br /></div><ul><li>Gain practical, in-depth experience that builds confidence and readiness for entry-level roles in business analysis </li><li>Apply the concepts and skills acquired to execute a practical business analysis project </li><li>Showcase proficiency in core business analysis skills, including requirements gathering, stakeholder analysis, process modeling, and risk assessment,  </li><li>Demonstrate proficiency in using relevant business analysis tools and technologies, such as Excel and Lucidchart </li></ul><div> </div>
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Capstone Project: Applying Business Analysis Skills

This project offers an opportunity to design key artifacts integral to a business analysis project, including a Business Requirements Document (BRD), Requirements Traceability Matrix (RTM), Stakeholder Analysis and Engagement Plan, Process Model Diagram, Scope Management Plan and Risk Mitigation Plan. To conclude the project, you'll design a professional PowerPoint presentation summarizing your findings, sharpening your analytical and presentation skills. 

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  <div>This Object-Oriented Programming (OOP) course is designed to empower beginner and experienced software developers with the skills needed to build robust and scalable Java applications. It provides an in-depth exploration of OOP principles, including encapsulation, inheritance, polymorphism, and abstraction, enabling you to create modular and maintainable code.<br /><br /></div><div>You will gain hands-on experience designing classes, implementing constructors, and mastering advanced OOP concepts such as interfaces, abstract classes, and method overloading. The course also covers collections, including lists, sets, queues, and maps, ensuring efficient data management in complex applications.<br /><br /></div><div>Additionally, you will learn essential file-handling techniques, work with dates and times, and integrate external services to enhance application functionality. Throughout the course, best practices in software design, such as modularity, code readability, and exception handling, are emphasized, preparing you for real-world software development challenges.</div><div>This course is part of the Java Development from IBM Professional Certificate, a structured learning path designed to provide practical programming experience. It is an ideal starting point for aspiring Java developers.<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Object-Oriented Programming in Java</strong></div><ul><li>Learn core OOP principles: classes, objects, encapsulation, constructors</li><li>Explore inheritance, polymorphism, interfaces, abstract classes</li><li>Hands-on labs to apply OOP concepts in Java</li></ul><div><strong>Module 2: Advanced Object-Oriented Programming Concepts</strong></div><ul><li>Delve into inheritance, polymorphism, interfaces, inner classes</li><li>Apply concepts through real-world examples and practical labs</li></ul><div><strong>Module 3: The Java Collections Framework</strong></div><ul><li>Study lists, sets, queues, and maps for data management</li><li>Hands-on practice selecting the right collection type for different challenges</li></ul><div><strong>Module 4: File Management</strong></div><ul><li>Learn file handling: creating, reading, writing files, managing directories</li><li>Hands-on with file streams for input/output operations</li></ul><div><strong>Module 5: Handling Dates and Times and Service Integration</strong></div><ul><li>Work with Java’s date and time classes, including time zones and date calculations</li><li>Master time-based functionality for real-world applications</li></ul><div><strong>Module 6: Final Project and Course Summary</strong></div><ul><li>Apply concepts learned in a final project</li><li>Course review and consolidation of Java programming skills</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Let your learners know what technology and skills they'll need prior to starting this guided project. Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save them the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.<br /><br /></div><h1><strong>Course objectives</strong></h1><div>After completing this course, you will be able to:</div><ul><li>Apply Object-Oriented Programming (OOP) concepts</li><li>Define and implement classes and objects, demonstrating encapsulation and abstraction</li><li>Use advanced OOP concepts such as inheritance, polymorphism, interfaces, and method overloading</li><li>Employ the Java collections framework to manage data using lists, sets, queues, and maps</li><li>Read and write to files using Java streams</li><li>Handle directories effectively</li><li>Work with Java date and time classes for effective date manipulation and formatting</li><li>Integrate external services and call external APIs</li><li>Demonstrate your knowledge with the completion of a comprehensive Java programming project</li></ul><h1><strong>Prerequisites and Hardware Requirements</strong></h1><div>This course is recommended for budding and experienced software developers who want to understand object-oriented programming (OOP) in Java. To access the course materials, you will need a laptop or desktop with internet access.</div>
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Object Oriented Programming in Java

Java developers with object-oriented programming skills in the United States earn an average annual salary of approximately USD 91,024 (Indeed.com).In this course, you’ll gain hands-on experience with key OOP concepts such as encapsulation, abstraction, inheritance, and polymorphism. Learn to define and implement classes and objects. You’ll explore advanced techniques, including interfaces, method overloading, and the Java collections framework, managing data with lists, sets, queues, and maps, and handling file input/output operations.

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  <div>Over 90% of Fortune 500 companies use Java (Netguru) and entry-level Java developers can earn USD100,000+ per year with outstanding opportunities for swift salary growth. This course gives you the job-ready, fundamental skills to develop Java applications employers are looking for, and enables you to add this powerful, versatile language to your resume in just 4-5 weeks!  </div><div>During the course, you’ll learn to set up the Java environment and install and configure the Java Development Kit (JDK), Java Runtime Environment (JRE), and an Integrated Development Environment (IDE). You’ll explore key Java concepts, such as data types, variables, operators, and control flow statements. Plus, you’ll discover how arrays, strings, methods, and effective exception handling are used to create efficient programs.  <br /><br />Throughout, you’ll engage in hands-on exercises and, at the end, you’ll complete a final project covering a real-world scenario that reinforces your practical understanding.  <br /><br />If you’re keen to add Java to your resume and have job-ready experience you can talk about in interviews, enroll today and power up new career opportunities in just one month!<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1:</strong> Introduction to the Java Environment and Ecosystem</div><div><strong>Module 2:</strong> Basic Programming Constructs in Java</div><div><strong>Module 3:</strong> Control Structures and String Handling</div><div><strong>Module 4:</strong> Robust Exception Handling</div><div><strong>Module 5:</strong> Final Project and Course Wrap-Up</div><div><br /></div><h1>Learning Objectives</h1><ul><li>Set up the Java environment</li><li>Install and configure the Java Development Kit (JDK), Java Runtime Environment (JRE), and Integrated Development Environment (IDE)</li><li>Describe the Java ecosystem and its applications across web, desktop, embedded, and mobile platforms</li><li>Explain Java fundamental coding concepts</li><li>Identify and utilize various programming constructs, including data types, variables, operators, and control flow statements</li><li>Work with arrays, strings, and methods to create efficient Java programs</li><li>Handle exceptions and manage data structures</li><li>Implement exception-handling techniques using try-catch blocks</li><li>Discuss the differences between checked and runtime exceptions</li><li>Synthesize knowledge gained throughout the course to complete a comprehensive project</li><li>Prepare for technical interviews through practice questions related to Java programming</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>This course is recommended for budding and experienced software developers who want to understand Java programming.  If you are new to software development, <a href="https://www.coursera.org/learn/introduction-to-software-engineering?">Introduction to Software Engineering </a>is a recommended prerequisite. You will need a laptop or desktop with internet access to access the course materials.   </div>
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Java Programming for Beginners

This course is designed to equip beginner and experienced software developers and programmers with the essential skills to write and understand Java programs. It comprehensively explores fundamental programming concepts such as variables, data types, and control structures.You will gain hands-on experience writing functions and methods, understanding class structures, and implementing encapsulation, inheritance, and polymorphism. The course also delves into core technical practices and exception handling, enabling you to write efficient and error-free code.

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  <div>The global recommendation engine market is predicted to grow 37% annually through 2030 (Straits Times). The expertise to predict user preferences and drive engagement using AI recommendation system skills has become an essential business need and a highly sought-after skill using vector databases. </div><div>In this IBM mini-course, you’ll create two shareable projects that demonstrate your proficiency and readiness to develop AI-powered recommendation systems.  <br /> <br />You’ll get step-by-step instructions to create a real-life inspired food ordering recommendation system using Chroma DB and Hugging Face models. For your final project, you’ll use Chroma DB or your choice of PostgreSQL, Cassandra, or MongoDB to create a real-life job search recommendation system. This will demonstrate your ability to generate embeddings and implement similarity searches using Hugging Face natural language processing (NLP) algorithms. <br /> <br />Ready to start? Bring your vector, NoSQL, or relational database vector search skills to this course. If you don't already have these skills, you can attain these skills in other Vector Databases Fundamentals Specialization courses.  <br /> <br />Enroll today in this mini-course to advance your AI career!</div><h1>Course Syllabus</h1><ul><li>Welcome</li><li>Introduction to Hugging Face</li><li>Practice project</li><li>Final project</li></ul><h1>Learning Objectives</h1><ul><li>Develop a recommendation system using the ChromaDB vector database and Hugging Face tools </li><li>Generate vector embeddings using natural language processing algorithms</li><li>Perform similarity searches using vector embeddings </li><li>Search a database using a PDF as the input query</li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>This course is the culminating project course in the series linked above. We highly recommend that you take the previous courses before this one.</div><div>To get the most out of this course, you should be comfortable with the following:</div><div>Technologies:</div><ul><li>JavaScript</li><li>ChromaDB</li><li>Git and GitHub</li></ul><div>Concepts:</div><ul><li>Vector embeddings</li><li>Natural language processing concepts</li><li>Recommendation systems</li><li>Similarity searches</li></ul><div>If you take the previous courses in the series, you will learn what you need for success in this course.</div>
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Vector Database Projects: AI Recommendation Systems

In this course, you will apply your knowledge of vector databases, natural language processing, embeddings, and similarity searches to create database-backed recommendation systems in a practice project and a final capstone project using JavaScript and ChromaDB.This course is the last in a series of microcourses. Follow the link below to read about the series and see how this program can benefit you and advance your career.

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  <div>In this course, you will learn how to design, deploy, load, manage, and query data warehouses and data marts. You will begin by understanding different analytics repositories including data marts, data warehouses, data lakes, data lakehouses and their functions and uses. They are designed to enable rapid business decision-making through accurate and flexible reporting and data analysis. <br /> </div><div>A data warehouse is one of the most fundamental business intelligence tools in use today, and one that successful Data Engineers must understand. Additionally, you will learn to design, model, and implement data warehouses and explore data warehousing architectures such as Star and Snowflake schemas. Finally, you will learn how to populate data warehouses using Extract, Transform, and Load (ETL) and Extract, Load, Transform (ELT) processes, verify data, query data, and use Cubes, Rollups, and materialized views/tables.  <br /><br /></div><div>The hands-on labs in this course will enable you to apply what you learn and gain a practical knowledge of data warehousing. You will work with repositories such as PostgreSQL and IBM Db2.</div><div><br /></div><h1>Course Syllabus</h1><ul><li>Module 1: Data Warehouses, Data Marts, and Data Lakes</li><li>Module 2: Designing, Modeling, and Implementing Data Warehouses</li><li>Module 3: Final Assignment and Final Quiz</li><li>Module 4 (Optional): IBM Db2</li></ul><div><br /></div><h1>What you'll learn</h1><ul><li>Discuss the fundamentals of data warehouses, data marts, and data lakes by analyzing their features and advantages.</li><li>Examine organizations' decision-making processes to select specific data warehouse systems and vendors.    </li><li>Design and populate a data warehouse, and model and query data using Cubes, Rollups, and materialized views.</li><li>Design and load data into a data warehouse, write aggregation queries, create materialized query tables, and create an analytics dashboard.</li></ul><h1><strong>Prerequisites</strong></h1><div>To succeed in this course, it is recommended that you should have a fundamental knowledge of Relational Database Management Systems (RDBMS) and a working knowledge of Structured Query Language (SQL).</div><div><br /></div>
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Data Warehouse Fundamentals

This course introduces you to designing, implementing and populating a data warehouse and analyzing its data using SQL & Business Intelligence (BI) tools. This course also covers data repositories including data marts, data lakes, data reservoirs, and Cognos Analytics for visualization. 

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  <div>This course is designed for everyone, including professionals, executives, students, and enthusiasts, interested in learning about generative AI and leveraging its capabilities in their work and lives.   <br /><br /></div><div>This course is your first step toward understanding the capabilities of generative AI, powered by different models, including large language models (LLMs).   <br /><br />In this course, you will learn about the fundamentals and evolution of generative AI. You will explore the capabilities of generative AI in different domains, including text, image, audio, video, virtual worlds, code, and data. You will understand the applications of generative AI across different sectors and industries. You will learn about the capabilities and features of common generative AI models and tools, such as GPT, DALL-E, Stable Diffusion, and Synthesia.    <br /><br />Hands-on labs, included in the course, provide an opportunity to explore the use cases of generative AI through IBM Generative AI Classroom and popular tools like ChatGPT. You will also hear from the practitioners about the capabilities, applications, and tools of generative AI.</div>
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Introduction to Generative AI

Dive into the new and exciting world of Generative AI and Large Language Models (LLM). Understand the capabilities of generative AI and its use cases in the real world. Identify the applications of generative AI in different sectors and industries. Explore common generative AI models and tools for text, code, image, audio, and video generation.

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  <div>This course is designed for everyone, including professionals, executives, students, and enthusiasts interested in leveraging effective prompt engineering techniques to unlock the full potential of generative artificial intelligence (AI) tools like ChatGPT and other LLMs. <br /><br /></div><div>Prompt engineering is a process to effectively guide generative AI models and control their output to produce desired results. In this course, you will learn the techniques, approaches, and best practices for writing effective prompts. <br /><br />You will learn about prompt techniques like zero-shot and few-shot, which can improve the reliability and quality of large language models (LLMs). You will also explore various prompt engineering approaches like Interview Pattern, Chain-of-Thought, and Tree-of-Thought, which aim at generating precise and relevant responses. <br /><br />You will be introduced to commonly used prompt engineering tools like IBM watsonx Prompt Lab, Spellbook, and Dust.  <br /><br />The hands-on labs included in the course offer an opportunity to optimize results by creating effective prompts in the IBM Skills Network AI Classroom. You will also hear from practitioners about the tools and approaches used in prompt engineering and the art of writing effective prompts.<br /><br /></div>
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Introduction to Prompt Engineering

Learn how to improve the effectiveness of your AI by using the latest prompt engineering techniques. Apply best practices for creating prompts and explore examples of impactful prompts. Practice common prompt engineering techniques and approaches for writing effective prompts for different LLMs.

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  <div>Complete the IBM Cybersecurity Analyst Professional Certificate with this final assessment!<br /><br /></div><div>In taking this final course, you will be showing your accumulated knowledge of cybersecurity skills. You will demonstrate your proficiency in cybersecurity tools and concepts including:<br /><br /></div><ul><li>Roles and responsibilities</li><li>System administration</li><li>Network security and vulnerabilities</li><li>Penetration testing and incident response</li><li>Threat intelligence</li></ul><div><br /></div><div>Before completing this final assessment, it is highly recommended that you complete the previous seven courses in the IBM Cybersecurity Analyst Professional Certificate.</div><div><br /><br /></div><ul><li><strong>Prerequisites:</strong><br />It is recommended to take the previous courses in the IBM Cybersecurity Fundamentals Professional Certificate before completing this course.</li></ul><div><strong><br />What you'll learn<br /></strong><br /></div><ul><li>Apply fundamental cybersecurity skills for a final assessment</li><li>Demonstrate proficiency in identifying cyber threats and risks</li><li>Showcase your knowledge of network security and testing for vulnerabilities</li><li>Exhibit an understanding of important concepts and tools utilized by successful cybersecurity analysts</li></ul><div><strong><br />Syllabus<br /></strong><br /></div><div>Module 1: IBM Cybersecurity Fundamentals Professional Certificate Assessment Exam</div>
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IBM Cybersecurity Assessment & CompTIA Security+ Practice Ex

Showcase your expertise in Cybersecurity fundamentals in this final assessment of the IBM Cybersecurity Analyst Professional Certificate!

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  <div>Develop a plausible genAI use case suited to your organization using your industry expertise and gain experience with AI tools and best practices by doing so.<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Develop an AI Use Case for Your Organization</strong></div><ul><li>Prompt Writing</li><li>Formulate A Business Goal with AI Assistance</li><li>Finalize and Evaluate Your Feasible Use Case</li></ul><div><br /></div><h1>About this Course</h1><div>As an executive or business leader, integrating genAI effectively into your organization will be a top priority. Navigating how to strategically use this tool is both critical and pressing. This short course guides you through formulating effective genAI use cases specific to your business circumstances.<br /><br /></div><div>Through a series of hands-on activities, you will apply genAI best practices while using IBM’s watsonx AI engine to both understand its nature and to brainstorm ways you can leverage this powerful tool in your organization. You will then construct a potential use case yourself. You will:</div><ul><li>Write effective prompts</li><li>Develop a business goal suited to your organization’s pain points</li><li>Choose an appropriate genAI application that addresses your goal</li><li>Critically examine the feasibility of your use case by considering common risks and reflect on ways to mitigate them</li></ul><div>Throughout the course, you will draw on your own industry expertise and leverage that knowledge to refine your ideas for your organization with genAI assistance.<br /><br /></div><div>To get the most from this course, we recommend you take the first 2 courses in the Generative AI for Executives and Business Leaders series as well. You should also be familiar with basic AI terminology and common AI business concepts.</div><div><br /></div>
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GenAI for Execs & Business Leaders: Formulate Your Use Case

Explore generative AI applications to enhance your business strategy. This beginner course empowers executives and leaders with fundamental skills in AI governance and response evaluation. Master prompt engineering for effective AI communication and introduce AI solutions tailored for business needs. Elevate decision-making and drive innovation in your organization through practical, AI-driven insights.

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  <div>Explain to a potential learner why this course is important/interesting and what they can gain from taking your course.<br /><br /></div><h1>Course Syllabus</h1><div><strong>Welcome</strong></div><ul><li>Course Preview</li><li>Course Overview</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li></ul><div><strong>Generative AI for Business Leaders</strong></div><ul><li>Video: From Pilot to Production: Driving ROI with GenAI </li><li>Video: Select the Right AI Use Case for your Business </li><li> Video: How Responsible AI Can Prepare You for Regulations </li><li>Video: Achieving AI-Readiness with Hybrid Cloud </li><li>Video: Is Data Management the Secret to Generative AI? </li><li>Video: Choose the Right AI Model for Your Use Case </li><li>Video: Putting AI to Work for Marketing </li><li>Video: Putting AI to Work for Talent </li><li>Video: Putting AI to Work for ITOps </li><li>Video: Putting AI to Work for FinOps </li><li>Video: Putting AI to Work in Finance</li><li>Reading: Course Summary: Putting AI to Work</li></ul><div><strong>Final Quiz and Course Wrap Up</strong></div><ul><li>Graded Quiz: Final Quiz</li><li>Reading: Congratulations and Next Steps</li><li>Reading: [Optional] Video Companion Guidebooks</li><li>Reading: Thanks from the Course Team</li></ul><div><strong>Course Rating and Feedback</strong></div><ul><li>Course Rating and Feedback</li></ul><div><strong>Completion Certificates and Badges</strong></div><ul><li>Claim your Badge Here and FAQ</li></ul><div><br /></div><h1>About this Course</h1><div>As an executive or business leader, determining how to integrate AI well is likely a top priority for your organization. In this short course, IBM thought leaders share their perspectives about effectively integrating generative AI at the enterprise level. Listen to their advice and gain insights into these key areas for your own organization:</div><ul><li>Strategies</li><li>Elements of AI in enterprise</li><li>Putting AI to work</li></ul><div>In this short, self-paced course, you’ll learn from these leaders' experiences and expertise cross-functionally and within specific domains regarding generative AI potential. Join us in helping you get started with practical guidance on AI adoption! No prior AI background required.</div><div><br /></div>
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GenAI for Execs & Business Leaders: Integration Strategy

Learn how executives and business leaders can strategically integrate Generative AI. Discover practical AI use cases and applications to enhance your enterprise. Understand governance and compliance to ensure responsible AI deployment. Gain beginner-level expertise in leveraging cutting-edge Artificial Intelligence solutions.

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  <div>Much of the world's data lives in databases. SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and manipulating data in databases. A working knowledge of databases and SQL is necessary for anyone who wants to start a career in Data Engineering, Data Analytics or Data Science.<br /><br /></div><div><br />The purpose of this course is to introduce relational database (RDBMS) concepts and help you learn and apply foundational and intermediate knowledge of the SQL language. You will start with performing basic Create, Read, Update and Delete (CRUD) operations using CREATE, SELECT, INSERT, UPDATE and DELETE statements. You will then learn to filter, order, sort, and aggregate data. You will also work with functions, perform sub-selects and nested queries, as well as access multiple tables in the database.<br /><br /></div><div><br />The emphasis in this course is on hands-on, practical learning. As such, you will work with real database systems, use real tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs, you will practice building and running SQL queries. At the end of the course you will apply and demonstrate your skills with a final project.<br /><br /></div><div><br />The SQL skills you learn in this course will be applicable to a variety of RDBMSes such as MySQL, PostgreSQL, IBM Db2, Oracle, SQL Server and others. No prior knowledge of databases, SQL or programming is required, however some basic data literacy is beneficial.<br /><br /></div>
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Introduction to SQL

This course introduces the fundamentals of Structured Query Language (SQL) and provides practical experience with real-world databases and tools to perform CRUD operations and manipulate data.

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  <div>In this capstone project, you will apply the skills and knowledge you’ve gained from the IBM Relational Database Administrator Professional Certificate to some real-world database administration tasks. <br /><br />You’ll begin by setting up an Online Transaction Processing (OLTP) database,  creating a data warehouse using PostgreSQL, and querying the databases using SQL.  <br /><br />Next, you’ll build Extract, Transform, and Load (ETL) pipelines with Apache Airflow to automate data flow across systems. Plus, you’ll implement backup and restore processes, starting with manual setups and then progressing to automated solutions.  <br /><br />To ensure database security, you’ll then configure user access and permissions in MySQL. You’ll optimize memory usage and improve query performance to enhance database efficiency.   <br /><br />By the end of the course, you’ll have demonstrated and applied key aspects of database administration. An ideal addition to your portfolio! <br /><br />If you’re looking to get hands-on experience in database administration that you can talk about in interviews, enroll today!</div><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Data Platform Architecture and OLTP Database</strong></div><div>In this module, you will design a data platform that uses MySQL as an OLTP database. You will be using MySQL to store the OLTP data.</div><div><br /><strong>Module 2: Build a Data Warehouse</strong></div><div>In this module, you will design and implement a data warehouse and you will then generate reports from the data in the data warehouse.</div><div><br /><strong>Module 3: ETL and Data Pipelines<br /></strong>In this module, you will use the given Python script to perform various ETL operations that load the data warehouse with the required information. You will also write a pipeline that analyzes the web server log file, extracts the required lines and fields, and transforms and loads data.</div><div><br /></div><div><strong>Module 4: Backup, Restore, and Automation</strong></div><div>In this module, you will perform back-up and restore operations on the database. You will also implement automation to streamline database management processes.</div><div><br /><strong>Module 5: Database Optimization, Security, and Access Management</strong></div><div>In this module, you will optimize the query performance and memory utilization of your database. You will also implement data security protocols, including user access management strategies and data encryption.</div><div><br /><strong>Module 6: Final Submission</strong></div><div>In this module, you will organize and submit your project deliverables for final evaluation. This module guides you through the submission process and outlines the criteria for assessing your work. </div><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>Proficiency in SQL, MySQL, PostgreSQL, ETL, Apache Airflow, and database creation, administration, and security principles is required.</div>
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Relational Database Administration Capstone Project

In this capstone project, you will apply the skills and knowledge you’ve gained from the IBM Relational Database Administrator Professional Certificate to some real-world database administration tasks. 

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  <h1>Exploring Partial Dependence Plots with Python: A Guided Journey</h1><div><br />Understanding how <strong>machine learning models</strong> make predictions is essential, especially in fields where data-driven decisions can significantly impact outcomes. In this hands-on guided project, you will explore the power of <strong>Partial Dependence Plots (PDPs)</strong> using <strong>Python</strong> and <strong>scikit-learn</strong>, focusing on two real-world datasets: the Titanic dataset for survival prediction and the Melbourne Housing dataset for price estimation.<br /><br /></div><div><br />Throughout this project, you will not only build predictive models but also learn to interpret their outputs, revealing how features such as age, fare, rooms, distance, and land size influence predictions. By visualizing these relationships through PDPs, you will gain insights into model behavior, enabling you to communicate findings effectively to stakeholders.<br /><br /></div><div><br />In just 30 minutes, you will develop a practical understanding of PDPs and their role in explaining machine learning models, equipping you with the skills to navigate the intersection of AI and real-world applications.<br /><br /></div><div>We also provide an Advanced Project of <strong>Partial Dependence Plot.</strong><br />You can take it here: <a href="https://cognitiveclass.ai/courses/med-prediction-with-explainable-ai-partial-dependence-plot">https://cognitiveclass.ai/courses/med-prediction-with-explainable-ai-partial-dependence-plot</a><br /><br /><strong><br />What You'll Learn<br /></strong><br /></div><div><br />By the end of this project, you will have mastered:<br /><br /></div><ul><li>Interpreting machine learning models using Partial Dependence Plots (PDPs) to visualize feature impacts.</li><li>Analyzing the influence of key features in both classification (Titanic dataset) and regression (Melbourne Housing dataset) contexts.</li><li>Applying Gradient Boosting Classifier and Regressor models to real-world datasets.</li></ul><div><strong><br />What You'll Need<br /></strong><br /></div><div><br />To get started with this guided project, you should have:<br /><br /></div><ul><li>A basic understanding of Python programming.</li><li>Access to modern web browsers like Chrome, Edge, Firefox, Internet Explorer, or Safari.</li></ul><div><br />Ready to unlock the insights hidden within your data? Start this guided project now and empower yourself to interpret complex algorithms, transforming raw data into actionable insights for decision-making in various domains.<br /><br /></div><div><br /></div>
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Partial Dependence Plot Applied to House Pricing Models

Partial Dependence Plots (PDPs), is a common techniques to interpret machine learning models by visualizing feature impacts on predictions. This lab explores the relationships between features such as rooms, distance, and landsize in the Melbourne Housing dataset, as well as age and fare in the Titanic dataset, translating complex model predictions into clear insights. Data scientists and stakeholders can leverage PDPs to gain a deeper understanding of model behavior, enhancing both technical analysis and informed business decision-making.

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  <div>Please Note: Learners who successfully complete this IBM course can earn a skill badge —a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!<br /><br /></div><div><br />Kickstart your learning of Python for data science, as well as programming in general with this introduction to Python course. This beginner-friendly Python course will quickly take you from zero to programming in Python in a matter of hours and give you a taste of how to start working with data in Python.<br /><br /></div><div><br />Upon its completion, you'll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this course is for you.<br /><br /></div><div><br />You can start creating your own data science projects and collaborating with other data scientists using IBM Watson Studio. When you sign up, you will receive free access to Watson Studio. Start now and take advantage of this platform and learn the basics of programming, machine learning, and data visualization with this introductory course.<br /><br /></div>
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Python Basics for Data Science

Start your journey in data science and programming with this beginner-friendly Python course. Earn a verifiable digital credential and free access to IBM Watson Studio upon completion.

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  <div>Generative AI for Data Analytics demonstrates the integration of Generative Artificial Intelligence (Generative AI) in Data Analytics. This course demonstrates how to use generative AI tools/frameworks/platforms to increase the effectiveness of various processes involved in the data analytics life cycle.  </div><div>You will learn how to use and apply generative models for tasks such as data generation, data augmentation, data preparation and finding insights, querying databases, generating visualizations, and storytelling. You will also explore challenges and ethical considerations associated with using Generative AI in data analytics.</div><div><br /></div><h1><strong>Course Syllabus</strong></h1><div><strong>Module 1: Data Analytics and Generative AI   </strong></div><ul><li><ul><li>Module Introduction and Learning Objectives </li><li>Video: Generative AI for Data Analytics  </li><li>Reading: Generative AI Tools for Data Analytics  </li><li>Video: Examples of Generative AI in Data Analytics  </li><li>Reading: Case Study on Successful Implementations of Generative AI in Data Analytics  </li><li>Lab: Explore a Simple Generative Tool </li><li>Discussion Prompt: Discuss How Generative AI Can Be Used in Data Analytics</li><li>Reading: Leveraging Generative AI in Data Analytics Process  </li><li>Demo Video: Generative AI for Data Generation and Augmentation  </li><li>Lab: Generative AI for Data Generation and Augmentation  </li><li>Demo Video: Generative AI for Data Preparation  </li><li>Lab: Generative AI for Data Preparation  </li><li>Demo Video: Generative AI for Querying Databases  </li><li>Lab: Generative AI for Querying Databases  </li><li>Video: Generative AI for Q and A Model Insights  </li><li>Lab: Generative AI for Q and A Model Insights  </li><li>Video: Successful Implementations of Generative AI for Data Preparation and Insights   </li><li>Reading: Using Microsoft Copilot   </li><li>Module 1 Practice Quiz: Data Analytics and Generative AI </li><li>Module 1 Summary: Data Analytics and Generative AI   </li><li>Module 1 Cheat Sheet: Data Analytics and Generative AI </li><li>Module 1 Graded Quiz: Data Analytics and Generative AI  </li></ul></li></ul><div><strong>Module 2: Use of Generative AI for Data Analytics  </strong> </div><ul><li><ul><li>Module Introduction and Learning Objectives </li><li>Demo Video: Generative AI for Data Insights  </li><li>Lab: Generative AI for Data Insights  </li><li>Demo Video: Generative AI for Data Visualization  </li><li>Lab: Generative AI for Data Visualization  </li><li>Video: Generative AI for Creating Dashboards  </li><li>Lab: Generative AI for Creating Dashboards </li><li>Video: Generative AI for Storytelling  </li><li>Lab: Generative AI for Storytelling  </li><li>Discussion Prompt: Dashboarding and Storytelling with Generative AI  </li><li>Video: Successful Implementations of Generative AI for Data Visualization and Storytelling  </li><li>Video: Considerations While Using Generative AI in Industries  </li><li>Video: Challenges While Using Generative AI  </li><li>Reading: Responsible Generative AI for Data Professionals </li><li>Case Study: Considerations While Using Generative AI in Healthcare </li><li>Lab: Considerations for Data Professionals Using Gen AI </li><li>Module 2 Practice Quiz: Use of Generative AI for Data Analytics </li><li>Module 2 Summary: Generative AI for Data Visualization and Storytelling  </li><li>Module 2 Cheat Sheet: Use of Generative AI for Data Analytics </li><li>Module 2 Graded Quiz: Use of Generative AI for Data Analytics  </li></ul></li></ul><div><strong>Final Project and Exam    </strong></div><ul><li><ul><li>Reading: Guided Practice Project Overview  </li><li>Lab: Guided Practice Project </li><li>Final Exam  </li></ul></li></ul><div><strong>Course prerequisites:  </strong></div><div>An introductory knowledge of data analytics, generative AI, and prompt engineering would be beneficial.  You can check these courses to get a basic understanding of these topics:</div><ul><li><a href="https://www.coursera.org/learn/generative-ai-introduction-and-applications?specialization=applied-artifical-intelligence-ibm-watson-ai">Generative AI: Introduction and Applications</a></li><li><a href="https://www.coursera.org/learn/generative-ai-prompt-engineering-for-everyone?specialization=applied-artifical-intelligence-ibm-watson-ai">Generative AI: Prompt Engineering Basics</a></li></ul><h1>Learning Objectives</h1><ul><li>Describe how you can use Generative AI tools and techniques in the context of data analytics across industries</li><li>Implement various data analytic processes such as data preparation, analysis, visualization, and storytelling using Generative AI tools</li><li>Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights </li><li>Analyze the ethical considerations and challenges associated with using Generative AI in data analytics</li></ul>
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Mastering Generative AI for Data Analytics

Enhance your data analytics career by mastering generative AI for data analysis. Learn intermediate-level skills in artificial intelligence and data augmenting techniques. Explore querying databases to improve data insights. Unlock new potential in your analytics projects. Unlock Generative AI data analytics skills: prompting, visualization, storytelling, and querying. Explore concepts, industry uses, and ethics. Designed for analysts with Python & AI basics, complete a shareable project and earn an industry certificate.

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  <ul><li>This course provides a comprehensive introduction to deep learning, covering key concepts, algorithms, and hands-on implementation using the Keras library. Through engaging lectures, practical labs, and quizzes, you’ll develop a solid foundation in deep learning techniques and gain experience building real-world models.</li></ul><div><br /></div><div>Begin your deep learning journey with an introduction to the fundamental concepts of artificial intelligence, neural networks, and deep learning. Learn how artificial neurons mimic the human brain and explore the structure of neural networks. Through hands-on labs, you’ll gain practical experience in building artificial neural networks, reinforcing key principles before testing your knowledge with quizzes and assessments.</div><div> </div><div>Dive deeper into the mechanics of deep learning by understanding key optimization techniques such as gradient descent and backpropagation. Discover the challenges of training deep networks, including the vanishing gradient problem, and explore the role of activation functions in improving model performance. Practical labs provide hands-on experience in implementing these techniques, ensuring you grasp their real-world applications.</div><div> </div><div>Explore the power of Keras and other deep learning libraries used in AI development. Learn how to build regression and classification models using Keras, and apply your knowledge in lab exercises focused on implementing these models. This module equips you with essential tools for developing deep learning applications efficiently.</div><div> </div><div>Expand your deep learning expertise by exploring different model architectures. Compare shallow and deep neural networks, and gain insights into specialized models like convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequence data, and transformers for advanced AI applications. Hands-on labs guide you through implementing CNNs and transformers in Keras, reinforcing theoretical concepts with practical applications.</div><div> </div><div>Put your knowledge to the test with a final project that challenges you to build a deep learning model for classification and captioning tasks. Apply the skills you’ve gained throughout the course to develop and evaluate your model, demonstrating your ability to create real-world AI solutions. Upon submission and evaluation, you’ll have a tangible project to showcase your deep learning expertise.</div><div> </div><div>This course is designed for professionals and students looking to enhance their deep learning skills. Whether you’re aiming for a career in AI, data science, or software development, this hands-on learning experience will provide the foundation you need.<br /><br /></div><h1>Learning Objectives: </h1><ul><li>Define key concepts such as neural networks, deep learning models, and activation functions.</li><li>Explain the differences between supervised and unsupervised deep learning models.</li><li>Implement deep learning models using Keras to solve real-world problems.</li><li>Compare and contrast different deep learning architectures, including convolutional and recurrent neural networks.</li><li>Assess the performance of deep learning models using appropriate metrics.</li><li>Design, build, train, and test deep learning models using the Keras library.</li></ul><h1><strong>Course Syllabus:</strong></h1><div><strong>Introduction to Deep Learning and Neural Networks</strong>   </div><div><br /></div><ul><li>Video: Introduction to Deep Learning </li><li>Video: Neurons and Neural Networks  </li><li>Video: Artificial Neural Networks  </li><li>Lab: Artificial Neural Networks </li><li>Practice Quiz: Introduction to Deep Learning and Neural Networks </li><li>Reading: Summary and Highlights: Introduction to Deep Learning and Neural Networks </li><li>Graded Quiz: Introduction to Deep Learning and Neural Networks </li></ul><div> <strong>Basics of Deep Learning </strong> </div><div><br /></div><ul><li>Video: Gradient Descent  </li><li>Video: Backpropagation  </li><li>Lab: Backpropagation </li><li>Video: Vanishing Gradient  </li><li>Video: Activation Functions  </li><li>Lab: Vanishing Gradient and Activation Functions </li><li>Practice Quiz: Basics of Deep Learning </li><li>Reading: Summary and Highlights: Basics of Deep Learning </li><li>Graded Quiz: Basics of Deep Learning  </li></ul><div><strong>Keras and Deep Learning Libraries  </strong> </div><div><br /></div><ul><li>Video: Deep Learning Libraries  </li><li>Video: Regression Models with Keras  </li><li>Lab: Regression with Keras </li><li>Video: Classification Models with Keras  </li><li>Lab: Classification with Keras </li><li>Practice Quiz: Keras and Deep Learning Libraries </li><li>Reading: Summary and Highlights: Keras and Deep Learning Libraries </li><li>Graded Quiz: Keras and Deep Learning Libraries </li></ul><div><strong>Deep Learning Models   </strong></div><div><br /></div><ul><li>Video: Shallow Versus Deep Neural Networks  </li><li>Video: Convolutional Neural Networks  </li><li>Lab: Convolutional Neural Networks with Keras </li><li>Video: Recurrent Neural Networks  </li><li>Video: Transformers  </li><li>Lab: Transformers with Keras </li><li>Video: Autoencoders  </li><li>Practice Quiz: Deep Learning Models </li><li>Reading: Summary and Highlights: Deep Learning Models </li><li>Graded Quiz: Deep Learning Models </li></ul><div><strong>Final Project </strong> </div><ul><li>Module Introduction and Learning Objectives </li><li>Final Project: Classification and Captioning</li><li>Final Project Submission and Evaluation</li></ul><div><strong>Course Wrap-Up   </strong></div><ul><li>Video: Course Wrap-Up  </li><li>Reading: Congratulations and Next Steps </li><li>Reading: Team and Acknowledgments </li><li>Copyrights and Trademarks </li></ul><h1>General Information:</h1><ul><li>This course is self paced. </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><ul><li>Working knowledge of Python.</li><li>Machine Learning with Python</li></ul>
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Deep Learning Fundamentals with Keras

Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.Upon course completion, learners will gain the capacity to describe neural networks and deep learning models, as well as discern the distinctions between them. 

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  <div><strong>Course Description:</strong><br /><br />In today’s data-driven world, the ability to design and manage effective Enterprise Data Architecture (EDA) is a highly sought-after skill for career opportunities in data strategy, architecture, and management. Whether you want to advance in your current role as a data engineer or database administrator or transition to a specialized position like a data architect or enterprise data strategist, this course provides the essential knowledge and skills to succeed in the in-demand field of EDA.<br /><br />Gain foundational knowledge of EDA, exploring its core components, including data models and industry-standard frameworks such as TOGAF and the Zachman Framework. This will enable you to design architecture that meets specific business requirements. <br /><br />Practical insights into ETL processes, DataOps practices, and lifecycle management provide a strong foundation for managing data operations. <br /><br />Through hands-on labs and projects, you will gain skills in designing, optimizing, and managing enterprise data architectures, ensuring efficient storage, processing, migration, and governance.<br /><br />With a mix of theory, activities, and labs, you’ll acquire the expertise needed to excel in enterprise data architecture.  <br /><br /><strong>Target Audience:</strong><br /><br />This course is designed for aspiring data architects who want to pursue a career in data architecture and design. <br />It is also beneficial for data engineers who want to expand their skills in data architecture and operations.<br /><br />This course is meant for professionals with a background in data engineering, database administration, system architecture, or data analysis.  <br /><br /><br /></div>
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Enterprise Data Architecture and Operations

This course is designed for aspiring data architects, engineers, IT professionals, and database administrators who want to optimize their organization’s data systems and operations. 

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  <div>Are you ready to dive into the world of data engineering? You’ll need a solid understanding of how data is stored, processed, and accessed. You’ll need to identify the different types of database that are appropriate for the kind of data you are working with and what processing the data requires.<br /><br /></div><div><br />In this course, you will learn the essential concepts behind relational databases and Relational Database Management Systems (RDBMS). You’ll study relational data models and discover how they are created and what benefits they bring, and how you can apply them to your own data. You’ll be introduced to several industry standard relational databases, including IBM DB2, MySQL, and PostgreSQL.<br /><br /></div><div><br />This course incorporates hands-on, practical exercises to help you demonstrate your learning. You will work with real databases and explore real-world datasets. You will create database instances and populate them with tables.<br /><br /></div><div><br />No prior knowledge of databases or programming is required.<br /><br /></div><div><br />Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course.<br /><br /></div>
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Introduction to Relational Databases (RDBMS)

Learn essential concepts of relational databases and RDBMS with this course. Hands-on exercises with real databases and datasets, no prior knowledge required. Audit for free, earn a certificate and IBM digital badge.

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  <div>This is the final course in the IBM Backend Development Professional Certificate. This capstone project course will give you the chance to practice the work that back-end developers do in real life when working with applications. <br /><br />You will be tasked to develop, deploy, and integrate an application composed of several microservices, and integrating them seamlessly. You will use various back-end tools and technologies, including Flask, Django, NoSQL, and Mongo DB, to accomplish this project while applying coding best practices. You will also deploy and scale and update each of the application’s microservices independently using Cloud Native technologies: Kubernetes, OpenShift and serverless (Code Engine).<br /><br />In this course, you’ll mostly focus on hands-on work to demonstrate and apply what you have learnt in previous courses. By successfully completing this Capstone you will have added a project to your programming portfolio to showcase to employers.<br /><br /></div>
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Back-end Application Development Capstone Project

In this course, you will assume the role of a real-life developer working to develop and deploy back-end microservices and applications. 

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  <div>This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.<br /><br /></div><div><br />By the end of this course you should be able to:<br /><br /></div><ul><li>Differentiate uses and applications of classification and classification ensembles</li><li>Describe and use logistic regression models</li><li>Describe and use decision tree and tree-ensemble models</li><li>Describe and use other ensemble methods for classification</li><li>Use a variety of error metrics to compare and select the classification model that best suits your data</li><li>Use oversampling and undersampling as techniques to handle unbalanced classes in a data set</li></ul><div><br />Who should take this course?<br /><br /></div><div><br />This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.<br /><br /></div><div><br />What skills should you have?<br /><br /></div><div><br />To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.<br /><br /></div>
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Supervised Machine Learning: Classification

This course teaches the basics of Classification in Supervised Machine Learning, with a focus on using best practices for data sets with unbalanced classes. Ideal for aspiring data scientists. Prerequisites: Python, Data Cleaning, EDA, Calculus, Linear Algebra,

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  <div>This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.<br /><br /></div><div><br />By the end of this course you should be able to:<br /><br /></div><ul><li>Differentiate uses and applications of classification and regression in the context of supervised machine learning</li><li>Describe and use linear regression models</li><li>Use a variety of error metrics to compare and select a linear regression model that best suits your data</li><li>Articulate why regularization may help prevent overfitting</li><li>Use regularization regressions: Ridge, LASSO, and Elastic net</li></ul><div><strong><br />Who should take this course?</strong><br /> This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting.<br /><br /></div><div><strong><br />What skills should you have?</strong><br /> To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.<br /><br /></div>
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Supervised Machine Learning: Regression

Learn to build and compare regression models for supervised machine learning. Gain hands-on experience with Python and explore best practices for data analysis. Prerequisites: Python, Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and

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  <p>This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.</p>

<p>By the end of this course you should be able to:</p>

<ul>
  <li>Explain the kinds of problems suitable for Unsupervised Learning approaches</li>
  <li>Explain the curse of dimensionality, and how it makes clustering difficult with many features</li>
  <li>Describe and use common clustering and dimensionality-reduction algorithms</li>
  <li>Try clustering points where appropriate, compare the performance of per-cluster models</li>
  <li>Understand metrics relevant for characterizing clusters</li>
</ul>

<p>Who should take this course?<br />
This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.</p>

<p>What skills should you have?<br />
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.</p>
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Unsupervised Machine Learning

This course teaches Unsupervised Machine Learning techniques, from data analysis to clustering algorithms, to help you find insights from unlabeled data. Suitable for aspiring data scientists with Python programming and mathematical fundamentals.

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  <div>This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few  Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.<br /><br /></div><div>After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning.<br /><br />By the end of this course you should be able to:<br />Explain the kinds of problems suitable for Unsupervised Learning approaches<br />Explain the curse of dimensionality, and how it makes clustering difficult with many features<br />Describe and use common clustering and dimensionality-reduction algorithms<br />Try clustering points where appropriate, compare the performance of per-cluster models<br />Understand metrics relevant for characterizing clusters<br /><br />Who should take this course?<br />This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning.<br /> <br />What skills should you have?<br />To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.<br /><br /></div>
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Deep Learning and Reinforcement Learning

This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning.

<div>Prepare for a career as a Deep Learning Engineer. Gain the in-demand skills and hands-on experience to get job-ready in less than 3 months.<br><br>In this professional certificate, you will:&nbsp;<br><br></div><ul><li>Define the key concepts of deep learning, including supervised learning, unsupervised learning, reinforcement learning, and neural network architectures such as CNNs and transformers.</li><li>Describe the applications and workings of popular deep learning libraries like PyTorch, Keras, and TensorFlow.</li><li>Apply deep learning frameworks and models to real-world problems, including image recognition, natural language processing, and time series prediction.</li><li>Analyze and evaluate the performance of different deep learning models, comparing models trained with various libraries.</li><li>Create custom models and layers using Keras, and integrate Keras with TensorFlow 2.X for advanced tasks.</li><li>Evaluate the results of deep learning projects, focusing on the interpretation of model performance and the optimization of neural network architectures.</li><li>Combine knowledge from multiple domains (machine learning, deep learning, neural networks) to construct end-to-end solutions and deploy them effectively.</li></ul><div>&nbsp;<br><strong>Program Overview</strong>&nbsp;<br><br></div><div>The global deep learning market is set to grow 23% annually to 2030 (Grand View Research). The IBM Deep Learning Professional Certificate builds the job-ready skills and practical experience needed to thrive in the rapidly advancing AI field.</div><div>&nbsp;</div><div>Deep learning, a core subset of machine learning, drives the generative AI revolution. This certificate program will equip you with the skills to build, train, and deploy deep learning models. Through hands-on projects, you’ll learn how to use deep neural networks to simulate human-like decision-making abilities, gaining a strong foundation in Python and key machine learning frameworks.</div><div>&nbsp;</div><div>You’ll learn to develop models using PyTorch, Keras, and TensorFlow, applying them to real-world challenges in areas like image recognition, natural language processing (NLP), and time series prediction. The program covers supervised learning, reinforcement learning, transformer models, and unsupervised learning methods.</div><div>&nbsp;</div><div>Along the way, you’ll build custom layers and models, create advanced convolutional neural networks (CNNs), and integrate Keras with TensorFlow 2.x. You’ll gain hands-on experience through practical labs and projects, where you’ll tackle real-world AI problems and create robust models that employers demand.</div><div>&nbsp;</div><div>By the end of the program, you will be well-equipped with industry-standard tools and techniques, having completed multiple projects, including a comprehensive capstone project, to showcase your deep learning expertise. Whether you’re looking to advance your current AI or data science career or start fresh, this Professional Certificate will provide you with the skills, knowledge, and practical experience to succeed in the growing deep learning job market.</div><div>&nbsp;</div><div>This program is for anyone interested in mastering Deep Learning.&nbsp; This program is ideal for professionals already in related roles, such as data scientists, software engineers, machine learning engineers, data engineers, and Python developers, who want to transition to a rewarding AI engineering career.</div><div><br><strong>Prerequisites<br><br></strong>The following skills are required to be successful in this program:&nbsp;<br><br></div><ul><li>High School level Mathematics</li><li>Working knowledge of Python</li><li>Machine Learning with Python</li></ul>

IBM Deep Learning Professional Certificate

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  <div>CAPM certification helps individuals with up to three years of experience in project management boost their resumes and make their job applications stand out. (Forbes). <br /><br /></div><div>This course helps talented project professionals to comprehensively prepare for this career-enhancing CAPM exam.  <br /><br /></div><div>During the course, you’ll get an overview of CAPM exam requirements, including education, experience, and associated costs. This includes: <br /><br /></div><div><strong>The CAPM exam registration process</strong>  <br /> Understand the steps required to register for the CAPM certification exam, with insights into eligibility requirements.  <br /><br /></div><div><strong>CAPM exam details</strong>  <br /> Learn about the structure of the CAPM exam, including the types of questions, exam format, and what to expect on test day.  <br /><br /></div><div><strong>CAPM exam domains</strong>  <br /> The CAPM exam is organized into four domains. You will explore the scope of each domain scope and understand their weighting in the exam.  <br /><br /></div><div><strong>Study resources and tips</strong>  <br /> Access resources and tips on preparing for the CAPM exam. This practical advice will help you create an effective study plan and utilize the provided practice tests to assess your readiness.  <br /><br /></div><div><strong>150-question mock exam</strong>  <br /> Complete a comprehensive 150-question mock exam which simulates the real CAPM exam experience, with a 3-hour time frame that mirrors the actual test.  <br /><br /></div><div>If you’re ready to power up your resume with a CAPM certification that catches the attention of employers, ENROLL TODAY. You’ll be exam ready in just 2 weeks! <br /><br /></div><div> </div><div><strong>You will learn:</strong></div><ul><li>Comprehensively prepare for the industry-recognized CAPM certification exam in just 2 weeks!  </li><li>Learn effective strategies and techniques to optimize your exam results, such as time management, question prioritization, and leveraging strengths </li><li>Understand the CAPM exam format, duration, topics, and objectives, enabling you to focus on the topics that matter most </li><li> Complete a 150-question mock exam that simulates the real examination environment. </li></ul><div><br /></div><h1>Course Syllabus</h1><div><br /><strong>Welcome</strong></div><ul><li>Video: Course Introduction </li><li>Reading: General Information </li><li>Reading: Learning Objectives and Syllabus </li><li>Reading: Grading Scheme </li><li>Reading: How to Make the Most of this Course</li></ul><div><strong>Module 1: CAPM Certification Test Overview</strong></div><ul><li>Reading: Module Introduction and Learning objectives  </li><li>Reading: CAPM Certification Exam Eligibility  </li><li>Video: Information on the CAPM Exam  </li><li>Reading: Overview of CAPM Exam Domains  </li><li>Reading: Breakdown of CAPM Exam Domains  </li><li>Video: Exam Taking Strategies  </li><li>Reading: Additional Study Resources and Tips  </li><li>Reading: Summary: Module 1: CAPM Certification Test Overview  </li><li>Reading: Check your readiness  </li><li>Practice Quiz: Domain 1 Project Management Fundamentals and Core Concepts  </li><li>Practice Quiz: Domain 2 Predictive Plan Based Methodologies  </li><li>Practice Quiz: Domain 3 Agile Framework and Methodologies  </li><li>Practice Quiz: Domain 4 Business Analysis Frameworks </li></ul><div><strong>Module 2: CAPM Mock Test and Next Steps </strong></div><ul><li>Reading: Module Introduction and Learning objectives  </li><li>Reading: Exam Instructions  </li><li>Graded Quiz: Graded Practice Exam for CAPM Certification  </li><li>Retake Exam  </li></ul><div><strong>Module: Course Wrap-Up</strong></div><ul><li>Reading: Thanks from the Course Team  </li><li>Reading: Congratulations and Next Steps  </li><li>Reading: Copyrights and Trademarks  </li></ul><div><br /><strong>Course Rating &amp; Feedback </strong></div><ul><li>Feedback  </li></ul><div><br /></div><div><br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>Learners should have completed prior courses in their associated program. However, an interest in practicing the CAPM exam is all that is required. </div>
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Project Management Practice Exam: CAPM Certification

Prepare for the CAPM exam with comprehensive insights, study resources, and a mock exam, and get ready to power up your resume with this industry-recognized certification. 

<div>Prepare for a career as a Generative AI Engineer. Gain the in-demand skills and hands-on experience to get job-ready in less than 6 months!<br><br>In this professional certificate, you will:&nbsp;<br><br></div><ul><li>Describe the core principles of Generative AI, machine learning, deep learning, NLP applications, and large language models by explaining their key components, functionalities, and how they are applied in real-world scenarios.</li><li>Apply Python libraries such as Flask, SciPy, Scikit-learn, Keras, and PyTorch to design, build, and deploy GenAI applications, AI-powered chatbots, and intelligent agents that interact with users dynamically.</li><li>Analyze different Generative AI architectures and NLP models, including transformers like BERT and large language models like GPT, by evaluating their structure, strengths, and limitations for various AI-driven tasks.</li><li>Evaluate the effectiveness of advanced AI techniques such as prompt engineering, model training, and fine-tuning by comparing different strategies and determining their impact on model accuracy, efficiency, and overall performance.&nbsp;</li><li>Create and deploy a fully functional Generative AI application by integrating machine learning techniques, LLMs, and frameworks such as RAG and LangChain to solve real-world NLP challenges.&nbsp;</li><li>Assess the ethical implications, risks, and potential biases associated with GenAI technologies by critically examining their societal impact and proposing responsible AI development and deployment strategies.</li></ul><div><br><strong>Program Overview</strong><br><br>The Generative AI industry is projected to expand at a CAGR of over 46% through 2030 (Statista). As a result, the demand for tech professionals skilled in Generative AI engineering is skyrocketing!</div><div>&nbsp;</div><div>The IBM Generative AI Engineering Professional Certificate equips aspiring Generative AI engineers, AI developers, data scientists, machine learning engineers, and AI research engineers with the critical expertise in Generative AI, large language models (LLMs), and natural language processing (NLP) needed to capture employers’ attention.</div><div>&nbsp;</div><div>A GenAI engineer designs AI systems that generate new data—such as text, images, audio, and video—by leveraging transformers and LLMs. This program immerses you in AI, Generative AI, and prompt engineering, alongside data analysis, machine learning, and deep learning with Python. You’ll work with libraries like SciPy and scikit-learn while developing applications using frameworks and models such as BERT, GPT, and LLaMA. You’ll also utilize Hugging Face Transformers, PyTorch, RAG, and LangChain to build and deploy LLM-based NLP applications, gaining expertise in tokenization, language models, and transformer methodologies.</div><div>&nbsp;</div><div>Through hands-on labs and projects, you’ll gain practical experience that will strengthen your resume and help you stand out in interviews. Plus, you’ll complete a major guided project where you’ll develop your own real-world GenAI application.</div><div>&nbsp;</div><div>If you’re ready to distinguish yourself with highly sought-after Generative AI skills, ENROLL TODAY and take your career to the next level in under six months!</div><div>&nbsp;</div><div><strong>Applied Learning Project</strong></div><div>&nbsp;</div><div><strong>Practical Experience That Employers Value</strong></div><div>&nbsp;</div><div>Employers prioritize hands-on experience when evaluating candidates. This Professional Certificate ensures you gain the practical expertise to demonstrate your capabilities in a job interview.</div><div>&nbsp;</div><div>Your hands-on learning includes:<br><br></div><ul><li>Generating text, images, and code using GenAI</li><li>Applying prompt engineering techniques and best practices</li><li>Developing multiple GenAI applications with Python and deploying them using Flask</li><li>Building an NLP data loader</li><li>Training a simple language model with a neural network</li><li>Using transformers for classification and creating a translation model</li><li>Implementing prompt engineering and in-context learning</li><li>Fine-tuning models to enhance performance</li><li>Leveraging LangChain tools and components for various applications</li><li>Constructing AI agents and applications with RAG and LangChain in a comprehensive guided project.</li></ul><div><br><strong>Prerequisites<br><br></strong>The following skills are required to be successful in this program:&nbsp;</div><div>&nbsp;</div><ul><li>Basic computer literacy</li><li>Working knowledge of Python</li></ul>

IBM Generative AI Engineering PC

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  <div>Explore the revolutionary capabilities of DALL·E in AI-driven image generation, transforming simple text prompts into stunning visuals. This guided project empowers developers, educators, and creatives to harness the power of OpenAI's DALL·E models, enabling them to create unique images in just 30 minutes. Learn how to craft effective prompts, utilize the OpenAI API, and experiment with various parameters to enhance your creative projects. Perfect for anyone looking to innovate in digital art, design, or content creation!<br /><br />DALL·E, an advanced AI model developed by OpenAI, is revolutionizing the way we create and visualize content. By bridging the gap between textual inputs and visual outputs, it offers an unprecedented tool for creativity and innovation. Whether you're a developer looking to integrate cutting-edge technology, an educator aiming to bring new dimensions to your teaching, or a creative professional seeking to expand your artistic toolkit, this project is your gateway to mastering AI-driven image generation.<br /><br /><strong>What You'll Learn</strong><br />After completing this project, you will be able to:<br />- Understand the fundamentals of DALL·E and its role in AI-driven image generation.<br />- Craft effective prompts to guide DALL·E in producing desired visual outputs.<br />- Utilize the OpenAI API to create and manage image generation projects.<br />- Experiment with various prompts to customize and enhance your creative projects.<br /><br /><strong>What You'll Need</strong><br />To get the most out of this guided project, you should have:<br />- Basic familiarity with programming concepts.<br />- A working understanding of APIs and their integration.<br />- Access to a computer with internet connectivity and a modern web browser like Chrome, Edge, Firefox, Internet Explorer, or Safari.<br /><br />Dive into this transformative project and unlock the potential of AI to elevate your digital creations to new heights!<br /><br /></div>
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DALL·E: Image Generation Guide for Beginners

Learn to use OpenAI's API to interact with DALL·E 2 and DALL·E 3, turning simple text ideas into breathtaking visuals. This hands-on project is designed to help developers and educators tap into the potential of DALL·E models, making it possible to create one-of-a-kind images in just 30 minutes. You'll get tips on writing effective prompts, using the OpenAI API, and tweaking settings to bring your creative ideas to life. Whether you're into digital art, design, or content creation, this is a great way to push your creative boundaries.


<div>Kickstart your career as a cybersecurity analyst. Gain the essential skills needed for this highly in-demand role—no degree or prior experience necessary.<br><br>In this professional certificate, you will:&nbsp;<br><br></div><ul><li>Gain the essential skills to secure an entry-level cybersecurity analyst role in just 4–6 months.</li><li>Develop a strong foundation in cybersecurity, including managing database vulnerabilities, operating system administration, and security protocols.</li><li>Learn to apply cybersecurity tools and techniques for penetration testing, incident response, and digital forensics.</li><li>Enhance your productivity and effectiveness as a cybersecurity analyst by leveraging generative AI.</li><li>Prepare for highly valued CompTIA Security+ and CySA+ Certifications.</li></ul><div><br><strong>Program Overview<br><br></strong>This IBM Professional Certificate equips you with the essential skills to help you land your first role as a cybersecurity analyst—no prior experience or degree required.<br><br></div><div>Cybersecurity analysts safeguard an organization’s systems and networks by detecting security breaches, analyzing threats, and implementing protective measures to prevent cyberattacks.<br><br></div><div>Through this program, you’ll gain a strong foundation in cybersecurity principles, tools, and technologies. You’ll explore topics such as cyber threats, operating systems, networking fundamentals, database security, vulnerabilities, cybersecurity architecture, and compliance frameworks. Additionally, you’ll learn how to leverage generative AI for cybersecurity and develop essential job search strategies, including resume building and interview preparation.<br><br></div><div>You’ll also gain hands-on experience in penetration testing, incident response, forensics, and threat intelligence. Real-world projects will allow you to create cybersecurity plans and compliance frameworks, giving you valuable practical knowledge to showcase in job interviews.<br><br></div><div>Upon successful completion, you’ll receive an IBM Professional Certificate and a digital badge to enhance your resume and demonstrate your expertise to employers.<br><br></div><div>This Professional Certificate is designed to provide practical experience that aligns with industry expectations. Throughout the program, you’ll engage in interactive labs and projects to refine your skills, including investigating real-world cybersecurity cases to build expertise in incident response and forensics, gaining hands-on practice with industry-standard security tools. You’ll be applying cybersecurity best practices to mitigate risks, enhance security, and ensure compliance, using generative AI to improve cybersecurity strategies and efficiency, and analyzing a security breach by identifying attack vectors, vulnerabilities, financial impacts, and prevention strategies.<br><br></div><div>Take the next step toward a rewarding career in cybersecurity—enroll now!</div><div><strong><br>Prerequisites&nbsp;</strong></div><div><br>No degree or prior experience in Cybersecurity required. You should have basic computer literacy.&nbsp;</div><div><br><br></div>

IBM Cybersecurity Analyst Professional Certificate

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  <div><br />Generative AI skills are in demand, with Ascend.io reporting that 89% of respondents reporting using generative AI in some capacity. ZipRecruiter reports that data engineers with generative AI skills earn an average of USD 115,000 annually, with top-end salaries of USD 179,000 annually.<br /><br /></div><div>Data engineering is responsible for building strong data pipelines, managing data infrastructure, and ensuring high-quality data evaluation.<br /><br /></div><div>This course is suitable for existing and aspiring data engineers, data warehousing specialists, and other data professionals such as data analysts, data scientists, and BI analysts.<br /><br /></div><div>First, learn about the current impact of generative AI on data engineering.<br /><br /></div><div>Throughout the course, you will assume the role of a data engineer and gain experience using generative AI to enhance productivity by introducing innovative ways to deliver projects.<br /><br /></div><div>You will learn how to use and apply generative models for tasks such as architecture design, database querying, data warehouse schema design, data augmentation, data pipelines, ETL workflows, data analysis and mining, data lake house, and data repositories. You will also explore challenges and ethical considerations associated with using Generative AI.<br /><br /></div><div>To complete this course, you'll demonstrate your new generative AI skills in a real-world, shareable, hands-on data engineering project.<br /><br /></div><div>After you successfully complete your final quiz, you will receive your certificate, and you can request your Badge. You can share both your project and certificate with your current or prospective employers.</div><h1>Course Syllabus</h1><div><strong>Module 1: Data Engineering and Generative AI<br /></strong><br /></div><ul><li>Welcome</li><li>Data Engineering and Generative AI</li><li>Generative AI for Data, Database, Warehouse, and Design</li><li>Module Summary and Quiz</li></ul><div><strong>Module 2: Use of Generative AI for Data Engineering<br /></strong><br /></div><ul><li>Generative AI for ETL and Data Repositories</li><li>Generative AI Considerations in Data Engineering</li><li>Module Summary and Quiz</li></ul><div><strong>Module 3: Guided Project and Final Exam<br /></strong><br /></div><ul><li>Guided Project</li><li>Final Exam</li><li>Course Wrap-Up</li></ul><div><br /></div><h1>General Information</h1><div><strong><br />What you'll learn<br /></strong><br /></div><ul><li>Use generative AI tools and techniques in data engineering processes across industries</li><li>Apply generative AI solutions for data generation, augmentation, and anonymization</li><li>Evaluate real-world case studies that feature successful application of Generative AI for ETL and data repositories</li><li>Build generative AI skills in hands-on labs and projects for data warehouse schema design and infrastructure setup</li></ul>
</div>


Mastering Generative AI for Data Engineering

Learn to apply AI models to enhance productivity for tasks like architecture design, database querying, ETL workflows, and data analysis. Explore ethical considerations and prove your skills with a real-world project. Unlock generative AI skills for data engineering. Boost your skills to revolutionize data pipelines, boost productivity, and stay ahead in the rapidly evolving tech landscape. Complete a shareable project and earn an industry certificate

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  <div>Do you want to enhance your software development career by leveraging the power of generative AI? This course explores the transformative applications of generative AI across the entire software development lifecycle. <br /><br />The course teaches how generative AI-based tools enable code generation, scripting, and program creation, boosting developer productivity and automating repetitive tasks. You will learn how to train and fine-tune these models for specific software development needs, from generating custom code snippets to creating intelligent applications. <br /><br />You will gain hands-on experience building static websites and prototypes using AI, accelerating the design process and fostering innovation. The course covers techniques for AI-assisted design and architecture, allowing you to explore new creative possibilities and optimize software structure. <br /><br />You will also discover how generative AI can be used for bug detection, code refactoring, and program optimization, leading to improved code quality and maintainability. The course provides insights into the ethical considerations, challenges, and limitations of using generative AI in software development, emphasizing responsible AI practices. <br /><br />Build a standout portfolio with hands-on labs and real-world projects, applying generative AI to real software development scenarios. From AI-powered features to streamlined workflows, you'll gain the practical experience employers demand. Plus, you'll learn to evaluate the impact of generative AI, preparing you for the future of software engineering. Enroll now and future-proof your career!  <br /><br />Job-ready AI-powered software development skills in less than 1 month, plus you’ll get practical experience How to generate code snippets, scripts, and programs using generative AI models, automating development tasks Hands-on experience training and fine-tuning generative AI models for software development needs, creating custom solutions How to create static websites and prototypes using AI, accelerating design and development processes.<br /><br /></div><h1>Learning Objectives</h1><ul><li>Job-ready AI-powered software development skills in less than 1 month, plus you’ll get practical experience  </li><li>How to generate code snippets, scripts, and programs using generative AI models, automating development tasks  </li><li>Hands-on experience training and fine-tuning generative AI models for software development needs, creating custom solutions  </li><li>How to create static websites and prototypes using AI, accelerating design and development processes  </li></ul><h1>Course Syllabus</h1><div><strong>Module 0: Welcome </strong> </div><ul><li>Video: Course Introduction  </li><li>Reading: General Information  </li><li>Reading: Learning Objectives and Syllabus  </li><li>Reading: Grading Scheme  </li></ul><div><strong>Module 1: Generative AI and Software Development </strong> </div><ul><li>Reading: Module Introduction and Learning Objectives  </li><li>Video: Introduction to Generative AI in Software Development  </li><li>Video: Meet Your Experts  </li><li>Video: Gen AI in Software Development  </li><li>Video: Leveraging Generative AI in Software Development Lifecycle  </li><li>Video: Large Language Models (LLM) and Transformers  </li><li>Video: Natural Language Processing (NLP) and Its Significance  </li><li>Reading: Popular Tools of Generative AI for Software Development  </li><li>Lab: OpenAI Account Setup and API Key Generation  </li><li>Optional Lab: Setting Up the Development Environment Using Visual Code Studio  </li><li>Lab: Building a Basic Chatbot using ChatGPT  </li><li>Lab: Get Familiar with Generative AI Classroom  </li><li>Video: Leveraging AI for Technical Help and Best Practices on Design Patterns and Architecture  </li><li>Video: AI for Coding and Development  </li><li>Lab: Generate Database Design with ChatGPT  </li><li>Lab: Get a Solution to the Given Coding Problem  </li><li>Demo Video: GitHub CoPilot for Code Generation and Troubleshooting  </li><li>Generating Static Website using Generative AI  </li><li>Demo Video: Generating Prototypes with Generative AI  </li><li>Reading: Generating Architecture and Design Diagrams Using Generative AI  </li><li>Demo Video: Generating Architecture and Design Diagrams Using Generative AI  </li><li>Reading: Managing Legacy Code with Gen AI  </li><li>Video: Gen AI for Technical Help and Software Architecture  </li><li>Module 1 Practice Quiz: Generative AI and Software Development  </li><li>Module 1 Summary: Generative AI and Software Development  </li><li>Reading: Useful Prompts for Software Design and Development  </li><li>Module 1 Graded Quiz: Generative AI and Software Development  </li><li>Optional Reading Setting Up Your Own AI Development Environment with ChatGPT  </li></ul><div><strong>Module 2: Generative AI for Software Development Workflows and its Considerations </strong> </div><ul><li>Reading: Module Introduction and Learning Objectives  </li><li>Video: Introduction to CI/CD Using AI  </li><li>Video: Gen AI for CI/CD Tools  </li><li>Lab: Write a Dockerfile for a Node App Deployment  </li><li>Video: Generative AI for Security in Software  </li><li>Video: Popular Generative AI Tools for Security in Software  </li><li>Video: Generative AI for Software Testing  </li><li>Lab: Generate Test Cases for Specific Use Case Using Generative AI  </li><li>Video: Integration of Generative AI Into Software Development Workflows  </li><li>Lab: Software Documentation Using Chat GPT  </li><li>Lab: Review Code with Generative AI  </li><li>Reading: Module Introduction and Learning Objectives  </li><li>Video: Introduction to CI/CD Using AI  </li><li>Video: Gen AI for CI/CD Tools  </li><li>Lab: Write a Dockerfile for a Node App Deployment  </li><li>Video: Generative AI for Security in Software  </li><li>Video: Popular Generative AI Tools for Security in Software  </li><li>Video: Generative AI for Software Testing  </li><li>Lab: Generate Test Cases for Specific Use Case Using Generative AI  </li><li>Video: Integration of Generative AI Into Software Development Workflows  </li><li>Lab: Software Documentation Using Chat GPT  </li><li>Lab: Code Translation Using Generative AI  </li><li>Lab: Review Code with Generative AI  </li><li>Reading: Considerations for Software Development for Generative AI  </li><li>Reading: Ethical Considerations for Using Generative AI in Software Development  </li><li>Video: Ethical Considerations for Software Development in AI  </li><li>Video: Innovation with Generative AI in Software Development  </li><li>Module 2 Practice Quiz: Generative AI for Software Development Workflows and Its Considerations  </li><li>Module 2 Cheatsheet: Generative AI for Software Development Workflows and its Considerations  </li><li>Module 2 Summary: Generative AI for Software Development Workflows and its Considerations  </li><li>Module 2: Generative AI for Software Development Workflows and its Considerations  </li></ul><div><strong>Module 3: Final Project </strong> </div><ul><li>Final Project: Pre-Work  </li><li>Reading: Final Project Overview  </li><li>Final Project: CodeCraftHub: Building Personalized Learning for Developers  </li></ul><div><strong>Module 4: Final Exam </strong> </div><ul><li>Final Exam: Generative AI for Software Developers  </li></ul><div><strong>Module 5: Course Wrap-Up </strong> </div><ul><li>Reading: Enhancing Your Software Developer Career with Generative AI  </li><li>Video: Wrap-up  </li><li>Reading: Congratulations and Next Steps  </li><li>Reading: Thanks from the Course Team  </li><li>Copyrights and Trademarks  </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>To get the most out of this course, you should have a working knowledge of Python and JavaScript and familiarity with software development processes.  </div>
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Mastering Generative AI for Software Development

Build job-ready AI skills for software development, automate coding, optimize design, detect bugs, and create AI-powered solutions while considering ethics.  

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  <div>With generative AI transforming the data science landscape, mastering its applications is crucial to data science professionals. This course equips you with cutting-edge generative AI skills tailored to the needs of a data scientist.  <br /><br />Through real-world scenarios, you will explore the use of generative AI throughout the data science lifecycle. Starting with data querying and preparation, you will learn how to leverage generative AI to generate, augment, and refine the data. You will also apply advanced AI techniques to develop and refine machine learning models while exploring ethical implications. <br /><br />This course is designed for existing data scientists, data professionals, analysts, and engineers looking to thrive in the evolving AI landscape.  <br /><br />You’ll gain hands-on experience using generative AI tools, culminating in a course-end project that simulates a real-world scenario. Your project would make an excellent addition to prospective employers. Finally, you’ll also be completing a final quiz to obtain your certificate,  <br /><br />A basic understanding of generative AI, prompt engineering, and data science is essential to complete this course successfully. <br /><br /></div><h1><strong>Course Learning Objectives</strong></h1><ul><li>Leverage various tools like GPT 3.5, ChatCSV, tomat.ai, and so on, available for data scientists working with generative AI for querying and preparing data </li><li>Examine real-world scenarios where generative AI can enhance data science workflows </li><li>Practice generative AI skills in hands-on labs and projects by generating and augmenting datasets for specific use cases </li><li>Apply generative AI techniques in the development and refinement of machine learning models </li></ul><h1>Course Syllabus</h1><div><strong>Module 1: Data Science and Generative AI  </strong></div><ul><li>Video: Generative AI and Data Science  </li><li>Reading: Generative AI Tools for Data Scientists </li><li>Video: Generative AI's Impact Across Industries</li><li>Video: Leveraging Generative AI in Data Science Lifecycle </li><li>Video: Types of Generative AI Models</li><li>Reading: Guide to Choosing a Generative AI Model Type</li><li>Reading: Case Study: Successful Implementation of Generative AI </li><li>Lab: Explore a Simple Generative Tool</li><li>Demo Video: Generative AI for Data Generation and Augmentation</li><li>Lab: Generative AI for Data Generation and Augmentation </li><li>Video: Generative AI for Data Preparation and Data Querying </li><li>Demo Video: Generative AI for Data Preparation </li><li>Lab: Generative AI for Data Preparation </li><li>Demo Video: Generative AI for Querying Databases </li><li>Lab: Generative AI for Querying Databases </li></ul><div><strong>Module 2: Use of Generative AI for Data Science </strong></div><ul><li>Demo Video: Generative AI for Data Insights</li><li>Lab: Generative AI for Data Insights </li><li>Demo Video: Generative AI for Data Visualization</li><li>Lab: Generative AI for Data Visualization </li><li>Video: Generative AI Tools for Model Development</li><li>Lab: Generative AI for Model Development </li><li>Video: Generative AI for Understanding Data and Model Development</li><li>Video: Considerations While Using Generative AI in Industries</li><li>Video: Challenges While Using Generative AI</li><li>Reading: Responsible Generative AI for Data Professionals</li><li>Case Study: Considerations While Using Generative AI in Healthcare</li><li>Lab: Considerations for Data Professionals Using Gen AI</li></ul><div><strong>Module 3: Final Project and Exam</strong></div><ul><li>Reading: Final Project Overview</li><li>Lab: Final Project: Generative AI for Data Science</li><li>Final Exam Instructions</li><li>Final Exam</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>A basic understanding of generative AI, prompt engineering, and data science is essential to complete this course successfully. </div>
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Mastering Generative AI for Data Science

This course helps you master generative AI skills to enhance data science workflows, improve data preparation, and refine machine learning models with real-world applications.  

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  <div>Imagine you're a real estate analyst examining housing markets across California neighborhoods. Some areas command premium prices while similar-looking houses sell for much less. You begin to wonder: <strong>What makes certain locations more valuable? Which combination of factors truly drives housing prices?</strong> Traditional machine learning models can predict prices accurately, but they rarely explain <strong>why</strong> one neighborhood is worth more than another. This is where <strong>Explainable AI (XAI)</strong> becomes invaluable.</div><div><br />In this hands-on project, you'll use IBM's <strong>AI Explainability 360 toolkit</strong> to develop clear explanations for housing prices. Using <strong>GLRMExplainer (Generalized Linear Rule Models) and LinearRuleRegression</strong>, you'll identify specific rules determining property values and visualize how features influence predictions. By leveraging <strong>interpretable rule-based models</strong>, you'll analyze relationships between <strong>income, house age, location, and other characteristics</strong>, making complex market dynamics understandable. This project demonstrates how explainable AI empowers real estate professionals, policymakers, and homebuyers to <strong>make informed decisions and understand market trends with confidence</strong>.</div><div><br /></div><h1><strong>A Look at the Project Ahead</strong></h1><div>In this project, you'll work with the California Housing dataset to uncover the factors driving property values using intepretable AI models. By the end of the project, you will:</div><ul><li><strong>Preprocess and transform housing data</strong> for rule-based AI analysis</li><li><strong>Build and train interpretable models</strong> using LinearRuleRegression and GLRMExplainer</li><li><strong>Extract and analyze decision rules</strong> to understand key factors influencing housing prices</li><li><strong>Visualize feature contributions</strong> to see how individual characteristics like income, location, and house age impact property values</li><li><strong>Explain specific predictions</strong> for individual properties using transparent rule-based reasoning</li></ul><div><br /></div><h1><strong>What You'll Need</strong></h1><div>To successfully complete this project, you'll need:</div><ul><li>Basic understanding of Python programming and libraries such as pandas, scikit-learn, and matplotlib</li><li>Familiarity with fundamental regression concepts and housing market terminology</li><li>A web browser to access tools and run your code</li></ul><div>By the end of this project, you will have built an AI model that not only predicts housing prices but also <strong>explains the reasoning behind each prediction</strong>, allowing real estate professionals to make data-driven decisions based on transparent, interpretable insights.</div>
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Explainable AI in Housing Markets: Rule-Based Analysis

Get AI to explain what shapes California housing prices. Learn AI Explainability methods which are essential to implementing AI in the regulated industries. As a real estate analyst, explore interpretable AI techniques to reveal why prices vary. Use rule-based models to extract decision rules from housing data, visualizing how income, age, and location influence property values. Turn complex market trends into clear, explainable insights, helping stakeholders make informed decisions with transparent AI-driven analysis instead of black-box predictions. 

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  <div>This course provides the foundational knowledge needed to build Cybersecurity skills as part of the Cybersecurity Analyst Professional Certificate program. </div><div><br />You’ll dive into incident response methodologies and security models, gaining the ability to identify and classify key vulnerabilities and attacks targeting modern organizations. Through detailed analysis of past and recent breaches, you’ll understand how they were detected and what measures could have reduced risk to the organization. Additionally, you’ll examine the financial impact of data breaches through case studies of well-known incidents. <br /><br /></div><div>As part of the course, you'll research a current cybersecurity breach, applying your skills to analyze the attack type, timeline, affected systems, and missed prevention opportunities. Your findings will be evaluated by your peers. </div><div><br />This course is ideal for anyone aiming to pursue a career as a Cybersecurity Analyst or Specialist, and completing it qualifies you to earn the Cybersecurity: Case Studies and Capstone Project digital badge. <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Investigate and explain the concept of a watering hole attack and its impact on organizations. </li><li>Analyze ransomware threats, detailing their potential consequences on organizational security and operations. </li><li>Examine third-party data breaches and assess their implications on an organization's security and business continuity. </li><li>Explore the effects of phishing attacks, including the risks they pose to organizations and their overall impact. </li></ul><h1>General Information</h1><ul><li>This course is self-paced.  </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Cybersecurity and Computer Science fundamentals knowledge.  </div>
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Cybersecurity: Case Studies and Capstone Project

Learn essential Cybersecurity skills, including incident response, vulnerability recognition, and breach analysis, with real-world case studies in this course. 

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  <div>This IBM course will equip you with the essential skills required to manage and investigate cybersecurity incidents effectively. You will dive deep into critical topics such as industry-leading incident response frameworks (NIST and SANS), digital forensics methodologies, and best practices for handling and preserving digital evidence. <br /><br /></div><div>Throughout the course, you’ll participate in hands-on labs and real-world projects that will allow you to apply your knowledge in detecting, analyzing, and mitigating cybersecurity threats. You will also gain valuable experience in conducting forensic investigations using industry-standard tools, giving you the practical expertise necessary to respond to cyber incidents swiftly and accurately. <br /><br /></div><div>By the end of this course, you will be prepared to implement incident response plans, collect and analyze digital evidence, and produce detailed reports that support organizational cybersecurity efforts.  <br /><br /></div><div>Whether you’re looking to enhance your existing skills or take your career in cybersecurity to the next level, this course is ideal for learners seeking to build proficiency in digital forensics and incident response. It’s an essential learning experience for anyone aiming to excel in the rapidly growing cybersecurity field. <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Clarify the principles and significance of incident response in cybersecurity. </li><li>Apply techniques to detect and analyze security incidents. </li><li>Utilize methods for collecting and examining digital evidence. </li><li>Evaluate and report digital forensic findings to strengthen cybersecurity initiatives.</li></ul><div><br /></div><h1>Course Syllabus</h1><div>Welcome to the Course</div><ul><li>Video: Course Introduction (3:32)</li><li>Reading: Course Overview</li><li>Plugin/Reading: Helpful Tips for Course Completion</li></ul><div>Module 1: Incident Response Fundamentals</div><ul><li>Module Introduction and Objectives</li><li>Video: What is Incident Response? (5:50)</li><li>Reading: The NIST and SANS Incident Response Frameworks</li><li>Video: Incident Response: Preparation (8:06)</li><li>Reading: The Importance of Incident Response Documentation</li><li>Video: Incident Response: Detection and Analysis (5:37)</li><li>Video: Containment, Eradication, and Recovery (6:01)</li><li>Reading: Incident Response:Tools and Automation</li><li>Video: Post-Incident Activities (5:34)</li><li>Activity: Respond to a Cybersecurity Incident</li><li>Reading: Incident Response and Preparedness Resources</li><li>Practice Quiz: Incident Response Fundamentals</li><li>Reading: Module Summary and Highlights</li><li>Graded Quiz: Incident Response</li></ul><div>Module 2: Digital Forensics and Investigation</div><ul><li>Module Introduction and Objectives</li><li>Video: Digital Forensics (4:27)</li><li>Video: Data Collection and Examination (6:29)</li><li>Lab: Investigate Logs using Cowrie</li><li>Reading: Handling Digital Evidence</li><li>Video: Analysis and Reporting (5:07)</li><li>Video: Forensic Data: Data Files (4:51)</li><li>Reading: Digital Forensics Tools</li><li>Activity: Digital forensics investigation</li><li>Reading: Chances of Recovering Data</li><li>Practice Quiz:  Digital Forensics</li><li>Reading: Module Summary and Highlights</li><li>Graded Quiz: Digital Forensics</li></ul><div>Module 3: Final Project</div><ul><li>Module Objectives</li><li>Glossary: Forensic Analysis and Incident Response</li><li>Reading: Final Project Overview</li><li>Final Project</li><li>Peer Review Assignment</li></ul><div>Course Wrap Up</div><ul><li>Video: Course Wrap Up (2:26)</li><li>Reading: Congratulations and Next Steps</li><li>Reading: Team and Acknowledgments</li><li>Copyrights and Trademarks</li></ul><div>Course Rating and Feedback<br /><br />Badge</div><div><br /></div><h1>General Information</h1><ul><li>This course is self-paced.  </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Basic knowledge of networking fundamentals and cybersecurity. </div>
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Cybersecurity: Forensic Analysis and Incident Response

Learn incident response and digital forensics with hands-on labs, covering NIST, SANS frameworks, and best practices for detecting, analyzing, and mitigating cyber threats. 

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  <div>The cost of security breaches continues to rise, prompting businesses to seek skilled professionals who can anticipate and prevent cyber threats. This IBM course in Penetration Testing, Threat Hunting, and Cryptography helps you develop the critical skills needed to identify vulnerabilities, protect assets, and strengthen your organization's cybersecurity defenses using AI and cryptography. <br /><br /></div><div>Throughout the course, you’ll learn the various phases of penetration testing and gain hands-on experience performing software pen testing and scanning code repositories. You’ll also master the creation of detailed penetration testing reports, engage in threat hunting, and integrate AI into threat intelligence processes. In addition, you'll explore Cyber Threat Intelligence (CTI) frameworks to identify and assess potential threats more effectively. <br /><br /></div><div>The course covers essential cryptography techniques, including encryption, decryption, and hashing, while also examining common cryptographic attacks and cryptanalysis methods. By the end, you'll have practical, real-world skills to enhance your organization's security posture. <br /><br /></div><div>To participate in the labs and final project, you will need a laptop or desktop, along with basic knowledge of networking and cybersecurity. This course is ideal for both current cybersecurity professionals looking to sharpen their skills and those aspiring to enter the field.  <br /><br />If you’re ready to gain in-demand cybersecurity expertise in penetration testing, threat hunting, and cryptography, this hands-on course will equip you with the skills needed to advance your career. <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Gain in-demand penetration testing and threat hunting skills, with hands-on practice that employers value on your resume. </li><li>Acquire practical experience applying penetration testing principles using tools like OWASP ZAP and SNYK. </li><li>Learn how to create detailed penetration testing reports and integrate AI for advanced threat hunting and intelligence gathering. </li><li>Master cryptography and cryptanalysis techniques, including encryption and hashing, to ensure data integrity and confidentiality. </li></ul><div><br /></div><h1>Course Syllabus</h1><div>Welcome<br />Video: Course Introduction (4:03)<br />General Information<br />Plugin/Reading: Helpful Tips for Course Completion<br />Grading Scheme<br /><br />Module 1: Penetration Testing Planning and Discovery</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: What is Penetration Testing? (7:40)</li><li>Video: Penetration Testing Phases (7:12)</li><li>Activity: Phases of the Penetration Testing Challenge</li><li>Video: Penetration Testing - Planning Phase (8:29)</li><li>Video: Penetration Testing - Discovery Phase (5:56)</li><li>Reading: Examples of passive and active reconnaissance</li><li>Reading: Google Dorking</li><li>Hands-on Lab: Practicing Google Dorking Commands</li><li>Practice Quiz: Penetration Testing Planning and Discovery</li><li>Reading: Summary and Highlights: Penetration Testing Planning and Discovery</li><li>Module Glossary: Penetration Testing Planning and Discovery Phases</li><li>Graded Assignment: Graded Quiz: Penetration Testing Planning and Discovery</li></ul><div>Module 2: Penetration Testing: Attack Phase</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Penetration Testing: Attack Phase (6:02)</li><li>Reading: Penetration Testing Tools</li><li>Hands-on Lab: Penetration Testing</li><li>Video: Port Scanning (6:52)</li><li>Lab: Port Scanning with NMap</li><li>Lab: Scanning Network Environment with NMap</li><li>Video: Overview Of Network Protocol Analyzers (7:05)</li><li>Hands-on Lab: Network Protocol Analyzers</li><li>Practice Quiz: Penetration Testing: Attack Phase</li><li>Summary and Highlights: Penetration Testing: Attack Phase</li><li>Reading: Glossary:  Penetration Testing: Attack Phase</li><li>Graded Assignment: Graded Quiz: Penetration Testing: Attack Phase</li></ul><div>Module 3: Penetration Testing: Reporting Phase</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Software and Application Pen Testing (6:50)</li><li>Video: Scanning Code Repositories (5:28)</li><li>Hands-on Lab: Getting started with GitHub (optional)</li><li>Hands-on Lab: Using SNYK to scan your code repository (optional)</li><li>Video: Penetration Testing - Reporting Phase (7:35)</li><li>Reading: PTES Framework</li><li>Activity: PTES Framework</li><li>Practice Quiz: Penetration Testing Reporting</li><li>Reading: Summary and Highlights:  Penetration Testing: Reporting Phase</li><li>Plugin/Reading: Glossary:  Penetration Testing: Reporting Phase</li><li>Graded Assignment: Graded Quiz: Penetration Testing: Reporting Phase</li></ul><div>Module 4: Threat Hunting and Threat Intelligence</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Threat Hunting Overview (5:09)</li><li>Video: Threat Intelligence Overview (5:09)</li><li>Reading: Threat Intelligence Process</li><li>Activity: Implement Threat Intelligence</li><li>Reading: Threat Intelligence Sources</li><li>Lab: Review X-Force Exchange Threat Reports</li><li>Video: Security Information and Event Management (7:13)</li><li>Video: AI in Threat Intelligence and Threat Hunting (5:17)</li><li>Reading: User and Entity Behavior Analytics</li><li>Video: Threat Intelligence Framework Overview (7:15)</li><li>Reading: Use Cases of the MITRE ATT&amp;CK Model and the Diamond Model of Intrusion Analysis</li><li>Practice Quiz: Threat Intelligence Platforms and Frameworks</li><li>Reading: Summary and Highlights: Threat Hunting and Threat Intelligence</li><li>Plugin/Reading: Glossary: Threat Hunting and Threat Intelligence</li><li>Graded Assignment: Graded Quiz: Threat Hunting and Threat Intelligence</li></ul><div>Module 5: Cryptography: Principles and Techniques</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Importance of Cryptography (6:12)</li><li>Video:  Overview of Encryption, Decryption, and Hashing (6:32)</li><li>Video: Symmetric and Asymmetric Encryption (6:16)</li><li>Reading: Essentials of Cryptography and Security</li><li>Lab : End of Encryption/ Decryption (RSA Algorithm)</li><li>Video: Common Cryptographic Attacks (6:32)</li><li>Video: Securing SSL/TLS and Strengthening Key Management Edit Set Access Page Actions (4:20)</li><li>Video: Cryptanalysis and Its Impact on Encryption (6:55)</li><li>Reading: Cryptographic Algorithms, Applications, and Attacks</li><li>Lab : Cryptoanalysis Attack</li><li>Reading: Quantum-Safe Cryptography</li><li>Video: IBM Quantum Safe Cryptography Solutions (3:17)</li><li>Practice Quiz: Cryptographic Attacks</li><li>Reading: Summary and Highlights: Cryptography: Principles and Techniques</li><li>Plugin/Reading: Glossary: Cryptography: Principles and Techniques</li><li>Graded Quiz: Cryptography and Cryptographic Attacks</li></ul><div>Module 6: Final Project and Final Quiz</div><ul><li>Module Introduction and Learning Objectives</li><li>Project Overview</li><li>Final Project: Part 1 - Perform Vulnerability Analysis and Penetration Testing</li><li>Final Project: Part 2 - Secure Information Using Symmetric Encryption</li><li>Peer Graded Assignment</li><li>Course Glossary</li><li>Video: Course Highlights and Key Takeaways (7:25)</li></ul><div>Course Wrap-Up</div><ul><li>Reading: Congratulations and Next Steps</li><li>Reading: Team and Acknowledgments</li><li>Copyrights and Trademarks</li></ul><div>Course Rating<br /><br />Claim your Badge</div><div><br /></div><h1>General Information</h1><ul><li>This course is self-paced.  </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>A basic understanding of IT systems and cybersecurity principles is recommended prior to enrollment. </div>
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Cybersecurity: Pen-Testing, Threat Hunting, & Encryption

Build in-demand cybersecurity skills in penetration testing, threat hunting, and cryptography. Learn to prevent cyber threats, create reports, and use AI for threat intelligence. 

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  <div>In today’s rapidly evolving digital landscape, where cybersecurity threats are increasingly sophisticated, the demand for skilled cybersecurity professionals continues to rise globally. With more talented candidates entering the field, standing out in this competitive job market can be challenging. So, how can you gain the edge needed to secure your place? <br /><br /></div><div>This course is designed to prepare you for success in landing your first cybersecurity job. Whether you're new to the field or looking to strengthen your application, this course will equip you with the practical skills and knowledge needed to become a top candidate. You’ll learn how to create essential job application materials such as resumes, portfolios, cover letters, and elevator pitches that highlight your strengths and make you an attractive hire. <br /><br /></div><div>You'll gain critical insights into how to effectively search for job opportunities, evaluate job listings, and apply strategically. The course also guides you through the interview preparation process, helping you understand the most common cybersecurity interview questions and providing mock interview scenarios to practice your responses. <br /><br /></div><div>Throughout this four-module, self-paced course, you’ll hear from seasoned cybersecurity professionals who share their personal experiences, advice on networking, and strategies for succeeding in coding tests and interview questions. By the end of the course, you’ll not only be equipped to apply confidently for entry-level cybersecurity positions but also be prepared to tackle real-world challenges in the interview process. <br /><br /></div><div>Join us on this journey to launch your cybersecurity career with the knowledge and tools that will set you apart from the competition!<br /><br /></div><h1>Learning Outcomes</h1><ul><li>Describe cybersecurity roles, job titles, responsibilities, and the key skills and competencies needed, while also exploring the NICE Framework. </li><li>Develop a resume, portfolio, elevator pitch, and cover letter, incorporating best practices to effectively prepare for a cybersecurity job interview. </li><li>Prepare for and respond to a variety of cybersecurity interview questions, including those about industry knowledge, technical skills, experience, and behavioral traits. </li><li>Outline the necessary steps to take after an interview to enhance your chances of success, and apply strategies for negotiating salary effectively<strong>.</strong> </li></ul><h1>Course Syllabus</h1><div><strong>Welcome</strong></div><ul><li>Video: Course Introduction (03:38)</li><li>Reading: General Information</li><li>Reading: Learning Objectives and Course Syllabus</li><li>General Information</li><li>Reading: Grading Scheme</li><li>Reading: Helpful Tips for Course Completion</li></ul><div><strong>Module 1: Getting Ready for a Cybersecurity Career </strong></div><ul><li>Reading: Module Introduction and Learning Objectives </li><li>Video: The Role of the Cybersecurity Professional (05:57)</li><li>Video: Expert Viewpoints: Meet the Experts (02:21)</li><li>Video: Expert Viewpoints: Cybersecurity Roles and Skills (05:05)</li><li>Lab: Perform Cybersecurity Skills' Readiness Check</li><li>Video: IBM Cybersecurity Professional Certificates (06:58)</li><li>Reading: Additional Program Recommendations</li><li>Video: Cybersecurity Career Activities (05:45)</li><li>Video: Expert Viewpoints: Cybersecurity as a Career (04:55)</li><li>Reading: Career Pathways: NICE Framework</li><li>Reading: Purpose and Value of a Professional Portfolio</li><li>Video: Illustrating a Cybersecurity Professional's Portfolio (05:37)</li><li>Video: Expert Viewpoints: Professional Portfolios (06:54)</li><li>Lab: Develop a Professional Portfolio</li><li>Video: Creating Profiles for Online Sites (04:56)</li><li>Video: Drafting the Elevator Pitch (06:22)</li><li>Video: Expert Viewpoints: Elevator Pitch (06:10)</li><li>Lab: Develop an Elevator Pitch</li><li>Reading: Module 1 Summary: Getting Ready for a Cybersecurity Career</li><li>Practice Quiz: Getting Ready for a Cybersecurity Career</li><li>Reading: Module 1 Glossary: Getting Ready for a Cybersecurity Career</li><li>Module 1 Graded Quiz: Getting Ready for a Cybersecurity Career</li></ul><div><strong>Module 2: Identifying Jobs, Creating Resumes, and Drafting Cover Letters </strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Video: Conducting Company and Industry Research (06:06)</li><li>Reading: Finding Job Postings</li><li>Lab: Assess Job Postings</li><li>Video: Expert Viewpoints: Conducting Company Research (06:41)</li><li>Reading: Online and Offline Networking</li><li>Video: Working with Third-Party Recruiters (07:06)</li><li>Video: Drafting a Resume (07:02)</li><li>Reading: Gathering Professional References</li><li>Video: Expert Viewpoints: Developing a Basic Resume (03:04)</li><li>Lab: Draft a Basic Resume</li><li>Video: Drafting a Cover Letter (04:39)</li><li>Lab: Draft a Cover Letter</li><li>Reading: Module Summary: Identifying Jobs, Creating Resumes, and Drafting Cover Letters</li><li>Practice Quiz:  Identifying Jobs, Creating Resumes, and Drafting Cover Letters </li><li>Reading: Module 2 Glossary: Identifying Jobs, Creating Resumes, and Drafting Cover Letters</li><li>Module 2 Graded Quiz: Identifying Jobs, Creating Resumes, and Drafting Cover Letters</li></ul><div><strong>Module 3: The Interview Process </strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Video: The End-to-End Interview Process (03:35)</li><li>Video: Submitting Your Application (06:02)</li><li>Video: Preparing for the Interview (05:51)</li><li>Reading: Human Resources (HR) Screening</li><li>Reading: First Stage Interview Process</li><li>Video: Expert Viewpoints: First Round Interview (03:22)</li><li>Video: Cybersecurity Specialist Mock Interview: First Stage (08:16)</li><li>Reading: Technical Screening</li><li>Video: Cybersecurity Specialist Mock Interview: The Technical Interview (08:33)</li><li>Lab: Develop Responses to Common Cybersecurity Specialist Interview Questions</li><li>Reading: Final Interview Checklist</li><li>Video: Final Interview: Pre-Interview Steps (05:18)</li><li>Reading: The Remote or Virtual Interview</li><li>Video: Expert Viewpoints: Interview Preparation (04:59)</li><li>Video: Attending the Interview (04:48)</li><li>Video: Answering Questions (05:37)</li><li>Video: Expert Viewpoints: Responding to Questions (03:01)</li><li>Reading: Closing the Interview</li><li>Video: Cybersecurity Specialist Mock Interview: Final Round (09:35)</li><li>Lab: Respond to Questions Using the STAR Technique</li><li>Reading: Module Summary: The Interview Process</li><li>Practice Quiz: The Interview Process</li><li>Reading: Module 3 Glossary: The Interview Process</li><li>Module 3 Graded Quiz: The Interview Process </li></ul><div><strong>Module 4: Understanding Interview Questions and Post-Interview Next Steps </strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Video: Core and Behavioral Interview Questions (06:23)</li><li>Reading: Case Study Questions</li><li>Video: Expert Viewpoints: Interview Questions (05:41)</li><li>Lab: Critique Cybersecurity Specialist Mock Interview Responses</li><li>Reading: Other Interview Methods</li><li>Video: Trick Questions and Illegal Questions (05:08)</li><li>Video: Expert Viewpoints: Difficult Questions (04:09)</li><li>Reading: Post-Interview Checklists</li><li>Reading: If You Get an Offer</li><li>Video: Salary Negotiation (05:07)</li><li>Reading: If You Don’t Get an Offer</li><li>Video: Expert Viewpoints: Job Offers (03:54)</li><li>Reading: Final Course Checklist</li><li>Reading: Module Summary: Understanding Interview Questions and Post-Interview Next Steps</li><li>Practice Quiz: Understanding Interview Questions and Post-Interview Next Steps</li><li>Reading: Navigating the Future of Cybersecurity: Key Trends and How to Stay Ahead</li><li>Reading: Module 4 Glossary: Understanding Interview Questions and Post-Interview Next Steps</li><li> Module 4 Graded Quiz: Understanding Interview Questions and Post-Interview Next Steps </li></ul><div><strong>Course Wrap-Up</strong></div><ul><li>Reading: Course Summary</li><li>Reading: Congratulations and Next Steps</li><li>Reading: Thanks from the Course Team</li><li>Copyright and Trademarks</li></ul><div><strong>Course Rating and Feedback<br /></strong><br /></div><div><strong>Badge</strong><br /><br /></div><h1>General Information</h1><ul><li>This course is self-paced.  </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Completion of prior courses in the Cybersecurity Analyst and a basic understanding of IT systems and cybersecurity principles is recommended prior to enrollment. </div>
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Cybersecurity Job Guide: Resume & Interview Preparation

Boost your cybersecurity career with this course. Learn to craft resumes, cover letters, and ace interviews, gaining insights from experts to stand out in a competitive field.

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  <div>This comprehensive course is designed to help you prepare for the CompTIA Security+ and CySA+ certifications, two key credentials for advancing in the cybersecurity field. Split into two focused modules, the course covers the essential exam details, eligibility requirements, and sign-up procedures for both certifications. You'll explore the five domains for Security+ and the four domains for CySA+, learning how to allocate study time effectively based on the question distribution for each domain. <br /><br /></div><div>With practical study tips, additional resource recommendations, and testing strategies, you’ll be fully equipped to tackle the exams. The course includes ungraded practice tests to assess your readiness, along with timed mock exams to simulate real testing conditions. By the end of the course, you’ll have the confidence and skills needed to excel in both CompTIA exams. <br /><br /></div><div>Plus, take advantage of exclusive IBM discounts on CompTIA content, exam vouchers, and bootcamps to further support your cybersecurity career journey. <br /><br /></div><h1>Learning Outcomes:</h1><ul><li>Explain the CompTIA certification sign-up process, requirements, and exam costs for Security+ and CYSA+. </li><li>Identify the domains and question distribution for each CompTIA Security+ and CYSA+ exam. </li><li>Find additional study resources tailored for the CompTIA Security+ and CYSA+ certifications. </li><li>Assess your exam readiness with practice tests and prepare for the test day with mock, timed exams. </li></ul><h1>General Information</h1><ul><li>This course is self-paced.  </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Completion of prior courses in the Cybersecurity Analyst Professional Certificate is strongly suggested. </div>
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Practice Exams for CompTIA Security+ & CySA Certifications

Master CompTIA Security+ and CySA+ exams in this 2-module course, with study tips, practice tests, and exam prep strategies to boost your cybersecurity career.

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  <div>Jakarta Faces is a framework for building component-based web applications that simplifies user interface (UI) development by managing reusable components, handling user interactions, and binding data to backend logic. It provides built-in lifecycle management, event handling, and server-side validation, reducing the need for manual request processing. Jakarta Faces also includes tag libraries that allows developers define UI components using markup and connect them to backend objects without writing repetitive setup code.<br /><br />To further streamline development, Jakarta Faces works with Jakarta Contexts and Dependency Injection (CDI)   to manage backend components. CDI allows beans to be automatically created and injected where needed, making it easier to manage application logic. Jakarta Expression Language enables data binding between the UI and backend, allowing UI components to dynamically display data and trigger backend actions.<br /><br />You’ll learn how to build a dynamic web application using Jakarta Faces for UI, CDI for managing backend logic, and Jakarta Expression Language (EL) for data binding.<br /><br /><br /></div>
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Building a dynamic web app with integrated UI and backend

Learn how to build a dynamic web application using Jakarta Faces, Jakarta Contexts and Dependency Injection, and Jakarta Expression Language.

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  <div>Cyberthreats are a growing challenge, costing billions and impacting everyone, from governments to small businesses. Are you ready to be part of the solution? <br /><br />This course offers a comprehensive understanding of cybersecurity compliance principles, industry standards, regulations, and audit processes. You’ll explore essential topics such as governance, risk management, compliance, cybersecurity frameworks, and process management. <br /><br /></div><div>You’ll dive into IT service management, the NIST Risk Management Framework, and AI ethical considerations. The course also covers cybersecurity laws and regulations, including HIPAA, GDPR, and PCI DSS, from both US and global perspectives. Additionally, you’ll learn about audit processes using the COBIT framework and SOC reports, and explore key standards like OWASP, ISO, and IEEE, with practical guidance on applying them. <br /><br /></div><div>Over five self-paced modules, you’ll engage in interactive activities to apply your knowledge in real-world scenarios and complete a final project to demonstrate your skills and understanding. <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Understand the core concepts of cybersecurity, including governance, risk management, compliance, AI ethics, and the audit process. </li><li>Identify key cybersecurity laws and regulations at both national and international levels, and understand their impact on organizations. </li><li>Explore industry standards and their role in cybersecurity practices, with a focus on the COBIT framework and SOC reports. </li><li>Apply cybersecurity standards and best practices to reduce risks, strengthen security, and ensure compliance through effective audit processes.</li></ul><h1>Course Syllabus</h1><div><strong>Welcome</strong></div><ul><li>Video: Course Introduction (3:49)</li><li>General Information</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li></ul><div><strong>Module 1: Introduction to Information Security and Compliance</strong></div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Governance, Risk, and Compliance (GRC) </li><li>Video: Governance Components </li><li>Reading: GRC Tools </li><li>(Lab) Match Activity: Applying GRC Concepts </li><li>Video: NIST Cybersecurity Framework </li><li>Reading: Aligning to the NIST Cybersecurity Framework  </li><li>Activity: Align to the NIST Cybersecurity Framework </li><li>Video: Elements of Effective Security Compliance </li><li>Video: Standardize Processes   </li><li>Video: Automation and Orchestration </li><li>Video: Change Management </li><li>Reading: Asset Management </li><li>Module 1 Summary: Introduction to Information Security and Compliance </li><li>Module 1 Glossary: Introduction to Information Security and Compliance </li><li>Module 1 Graded Quiz: Introduction to Information Security and Compliance </li><li>Discussion Prompt: (Optional) CSF Implementation </li></ul><div><strong>Module 2: Foundations of IT Service Management and Risk Governance</strong> </div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Information Technology Infrastructure Library (ITIL) Overview </li><li>Video: Key ITIL Processes  </li><li>Activity: Identify the ITIL Phases </li><li>Reading: ITIL Process Implementation </li><li>Video: Risk Management </li><li>Reading: Risk Analysis </li><li>(Lab) Matching: Accept, Transfer, Avoid, or Mitigate?  </li><li>Video: Third-Party Risk Assessment and Management </li><li>Video: AI Ethics  </li><li>Reading: EU AI Act  </li><li>Video: How to Implement AI Ethics  </li><li>Activity: Apply AI Ethics </li><li>Module 2 Summary: Foundations of IT Service Management and Risk Governance  </li><li>Module 2 Glossary: Foundations of IT Service Management and Risk Governance  </li><li>Module 2 Graded Quiz: Foundations of IT Service Management and Risk Governance  </li><li>Discussion Prompt:(Optional) Foundations of IT Service Management and Risk Governance </li></ul><div><strong>Module 3: Understanding Cybersecurity Laws and Regulations </strong></div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Overview of US Cybersecurity Laws and Regulations  </li><li>Video: HIPAA Security Rule  </li><li>Reading: HIPAA Cybersecurity Guidance </li><li>Activity: Apply HIPAA Guidelines </li><li>Reading: US Laws and Regulations Cheat Sheet </li><li>Video: Overview of Global Cybersecurity Laws and Regulations  </li><li>Reading: Applying Global Cybersecurity Laws and Regulations </li><li>Activity: Apply global cybersecurity laws and regulations </li><li>Reading: Global Cybersecurity Laws and Regulations Cheat Sheet </li><li>Practice Quiz: Understanding Cybersecurity Laws and Regulations  </li><li>Module 3 Summary:  Understanding Cybersecurity Laws and Regulations  </li><li>Module 3 Glossary:  Understanding Cybersecurity Laws and Regulations  </li><li>Module 3 Graded Quiz:  Understanding Cybersecurity Laws and Regulations  </li><li>Discussion Prompt: (Optional) Cybersecurity Laws</li></ul><div><strong>Module 4: Understanding Cybersecurity Standards and Audits </strong></div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Industry Standards  </li><li>Reading: OWASP Use Cases </li><li>Activity: Apply OWASP Standards </li><li>Video: NIST, ISO, and IEEE Overview </li><li>Reading: NIST Standards  </li><li>Reading: ISO Standards  </li><li>Reading: IEEE Standards </li><li>Video: Security Control Audits  </li><li>Video: Performing a Security Audit </li><li>Video: ISACA Standards </li><li>Reading: COBIT </li><li>Video: SOC Reports  </li><li>Practice Quiz: Understanding Cybersecurity Standards and Audits</li><li>Module 4 Summary: Understanding Cybersecurity Standards and Audits </li><li>Module 4 Glossary: Understanding Cybersecurity Standards and Audits </li><li>Module 4 Graded Quiz: Understanding Cybersecurity Standards and Audits </li><li>Discussion Prompt: (Optional) Conducting Security Audits </li></ul><div><strong>Module 5: Final Project</strong></div><ul><li>Module Introduction and Learning Objectives </li><li>Final Project Overview </li><li>Final Project Scenarios </li><li>Final Project  </li></ul><div><strong>Course Wrap-Up</strong></div><ul><li>Video: Course Summary</li><li>Congratulations and Next Steps</li><li>Thanks from the Course Team </li><li>Copyrights and Trademarks </li></ul><div><strong>Course Rating &amp; Feedback<br /></strong><br /></div><div><strong>Badge<br /></strong><br /></div><h1>General Information</h1><ul><li>This course is self-paced.  </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Knowledge of cybersecurity and IT fundamentals. </div>
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Cybersecurity: Framework, Compliance, and Governance

Learn cybersecurity principles, industry standards, regulations, and audit processes. Explore key concepts in information security, compliance, and risk management. 

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  <div>As cyberattacks increasingly target databases and the cost of a data breach continues to rise, businesses are urgently seeking skilled cybersecurity professionals to safeguard their data. This course provides you with essential database security skills that are in high demand. <br /><br />You'll learn key database concepts, such as relational and non-relational databases, and master SQL fundamentals. You'll gain hands-on experience creating and executing SQL queries while building foundational knowledge in relational data management, roles, and permissions. <br /><br /></div><div>The course covers vital information security measures, including encryption, hashing, masking, tokenization, and access restrictions to protect sensitive data.  <br /><br /></div><div>You will learn to manage database user profiles, implement password policies, assign privileges, and design and implement robust security and auditing models. <br /><br /></div><div>Additionally, the course addresses database injection vulnerabilities like OS command and SQL injection attacks, providing strategies for mitigating these risks. <br /><br /></div><div>With practical labs focused on user management and access control, this course will help you develop critical database security skills that will make you attractive to potential employers. </div><div><br /></div><h1>Learning Outcomes</h1><ul><li>Master the key data management skills needed for job readiness, with a focus on data protection techniques to safeguard sensitive information. </li><li>Understand relational database (RDBMS) fundamentals and practice querying databases with SQL. </li><li>Learn how to configure and manage database user profiles, implement password policies, and assign roles and privileges effectively. </li><li>Identify potential vulnerabilities in databases, such as OS command and SQL injection attacks, and gain strategies to defend against these threats. </li><li>Apply data protection practices to ensure security and transparency in data management processes. </li></ul><h1>Course Syllabus</h1><div>Welcome<br />Video: Course Introduction (4:01)<br />General Information<br />Learning Objectives and Syllabus<br />Grading Scheme<br /><br />Module 1: Database Fundamentals</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Introduction to Data and Databases (2:56)</li><li>Video: Relational Databases (7:34)</li><li>Video: Non-Relational Databases (7:33)</li><li>Video: Relational Data Concepts (5:34)</li><li>Lab: Relational Data Concepts</li><li>Practice Quiz: Database Fundamentals</li><li>Module 1 Summary: Database Fundamentals</li><li>Module 1 Graded Quiz: Database Fundamentals</li><li>(Optional) Discussion Prompt: Relational and non-relational databases</li></ul><div>Module 2: SQL Basics and Database Management Fundamentals</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: SELECT Statement (3:41)</li><li>Reading: SELECT statement examples</li><li>Lab: Simple SELECT Statements</li><li>Video: COUNT, DISTINCT, and LIMIT (2:31)</li><li>Lab: COUNT, DISTINCT, and LIMIT</li><li>Video: INSERT Statement (2:42)</li><li>Video: UPDATE and DELETE Statements (3:17)</li><li>Lab: INSERT, UPDATE, and DELETE</li><li>Video: Database Roles and Permissions (5:29)</li><li>Video: Database Management (6:56)</li><li>Video: Backing Up Databases (5:39)</li><li>Lab: Performing Basic Database Operations</li><li>Practice Quiz: SQL Basics and Database Management Fundamentals</li><li>Module 2 Summary: SQL Basics and Database Management Fundamentals</li><li>Module 2 Graded Quiz: SQL Basics and Database Management Fundamentals</li><li>Discussion Prompt: SQL Statements</li></ul><div>Module 3: Database Security and Data Protection Strategies</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Data Types (7:47)</li><li>Video: Data Classifications (7:22)</li><li>Video: General Data Protection Considerations (6:33)</li><li>Video: Methods to Securing Data: Part 1 (0:00)</li><li>Video: Methods to Securing Data: Part 2 (6:53)</li><li>Lab: Securing Data</li><li>Video: Database User Profiles, Password Policies, Privileges, and Roles (6:57)</li><li>Video: Design and Implement Database Application Security Models (7:25)</li><li>Video: Design and Implement Database Auditing Models (7:23)</li><li>Video: Application Data Auditing (6:13)</li><li>Video: Auditing Database Activities (7:08)</li><li>Lab: Managing MySQL User Accounts, Access Control, and Encryption</li><li>Reading: Case Study: Data Breach</li><li>Practice Quiz: Database Security and Data Protection Strategies</li><li>Module 3 Summary: Database Security and Data Protection Strategies</li><li>Module 3 Graded Quiz: Database Security and Data Protection Strategies</li><li>Discussion Prompt (Optional): Data Protection Methods</li></ul><div>Module 4: Injection Vulnerabilities</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Introduction to Injection Flaws (2:29)</li><li>Video: OS Command Injection: Part 1 (5:37)</li><li>Video: OS Command Injection: Part 2 (5:16)</li><li>Video: OS Command Injection: Part 3 (6:02)</li><li>Video: SQL Injection: Part 1 (7:34)</li><li>Video: SQL Injection: Part 2 (5:21)</li><li>Video: Database Injection Vulnerabilities (6:31)</li><li>Video: Other Types of Injections (4:14)</li><li>Lab: Exploiting SQL Injection Vulnerabilities</li><li>Reading: Additional Resources</li><li>Practice Quiz: Injections Vulnerabilities</li><li>Module 4 Summary: Injection Vulnerabilities</li><li>Module 4 Graded Quiz: Injection Vulnerabilities</li><li>(Optional) Discussion Prompt: Mitigating Injection Flaws</li></ul><div>Module 5: Final Assessment and Final Project</div><ul><li>Module Introduction and Learning Objectives</li><li>Reading: Final Project Overview</li><li>Lab: Final Project: Comprehensive Database Security and Management</li><li>Peer-Graded Assignment: Comprehensive Database Security and Management</li><li>Module 5 Graded Quiz: Database Basics and Security</li><li>Course Glossary: Database Fundamentals and Vulnerabilities</li></ul><div>Course Wrap-Up</div><ul><li>Video: Course Wrap-Up (4:18)</li><li>Reading: Congratulations and Next Steps</li><li>Reading: Thanks from the Course Team</li><li>Copyrights and Trademarks</li></ul><div>Course Rating<br /><br />Digital Badge<br /><br /></div><h1>General Information</h1><ul><li>This course is self-paced.  </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul>
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Database Fundamentals and Vulnerabilities

Learn how to secure data, manage database users and permissions, and prevent injection vulnerabilities to protect databases from expensive breaches, while gaining valuable cybersecurity skills.

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  <div>The demand for information security analysts is growing as companies seek professionals to protect their networks. This course offers practical, hands-on experience in computer networking and network security, equipping you with the skills employers need. Through interactive labs, you’ll learn to secure small office/home office (SOHO) networks, configure DHCP, filter DNS, and use open-source Extended Detection and Response (XDR) systems. </div><div><br />You’ll also gain a solid understanding of network fundamentals such as ports, protocols, IP addresses (including IPv6), and network routing. Learn about Layer 2 and 3 addressing, routers, and routing tables, which are essential for effective network management. </div><div><br />In addition, you’ll explore cybersecurity tools for data protection, endpoint security, and SIEM, preparing you to meet an organization’s compliance and threat intelligence needs. This knowledge is vital in today’s fast-evolving cybersecurity environment. <br /><br /></div><div>You’ll complete a final project that demonstrates your ability to handle network and security planning tasks. <br /><br /></div><div>Invest in your future and gain the networking and security expertise that employers are looking for—enroll today! <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Identify key computer network and network security skills needed to protect networks and endpoint devices from unauthorized access. </li><li>Apply organizational policies for endpoint device protection using firewall configurations and inbound/outbound rulesets. </li><li>Install and configure security tools, apply patches, and use cloud-based software for detecting, quarantining, and removing malware, as well as managing intrusion detection systems. </li><li>Recommend network hardware, security solutions, and monitoring practices to enhance network security and management. </li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1 – Networking Fundamentals </strong></div><ul><li>Module Introduction and Learning Objectives </li><li>Computer Networks: Networking Hardware Devices  </li><li>Models, Standards, Protocols, and Ports </li><li>Computer Networks: Wireless Networks and Standards  </li><li>Activity: Identifying Wireless Network Technologies </li><li>Protocol Table </li><li>Hands-On-Lab: Exploring Networking Ports and Protocols </li><li>Reading: Designing a Basic Network Structure </li><li>Hands-On-Lab: Designing a Basic Network Structure </li><li>Hands-On-Lab: Ports and Protocols in Windows Defender </li><li>Practice Quiz: Networking Fundamentals </li><li>Summary </li><li>Module 1 Glossary </li><li>Module 1 Graded Quiz </li><li>Discussion Prompt: Design a network structure for the future </li></ul><div><strong>Module 2 – IP Addressing Routing and Switching </strong></div><ul><li>Basics of IP Addressing </li><li>Module Introduction and Learning Objectives </li><li>IP Addressing - The Basics of Binary  </li><li>IP Address Structure and Network Classes  </li><li>IP Protocol and Traffic Routing  </li><li>Introduction to the IPv6 Address Schema  </li><li>Hands-On-Lab: IP Addressing and Subnet Masking </li><li>Basics of Network Routing </li><li>Layer 2 and Layer 3  Addressing  </li><li>Address Resolution Protocol  </li><li>Routers and Routing Tables Part 1 </li><li>Routers and Routing Tables, Part 2 </li><li>Routers and Routing Tables, Part 3  </li><li>Hands-On-Lab: Securing a SOHO Network  </li><li>Practice Quiz: IP Addressing Routing and Switching </li><li>Summary </li><li>Module 2 Glossary </li><li>Module 2 Graded Quiz </li></ul><div><strong>Module 3: Network Protocols </strong></div><ul><li>Module Introduction and Learning Objectives </li><li>Application and Transport Protocols Part 1  </li><li>Application and Transport Protocols Part 2  </li><li>Hands-On-Lab: Analyzing TCP and UDP Traffic with Wireshark </li><li>Hands-On-Lab: Protecting Endpoints with OpenEDR </li><li>DNS and DHCP  </li><li>Hands-On-Lab: Installing and Configuring DHCP  </li><li>DNS Filtering  </li><li>Hands-On-Lab: DNS Filtering </li><li>Syslog Message Logging Protocol  </li><li>Flows and Network Analysis  </li><li>Port Mirroring and Promiscuous Mode  </li><li>User Behavior Analysis  </li><li>Practice Quiz: Network Protocols </li><li>Summary </li><li>Module 3 Glossary </li><li>Module 3 Graded Quiz </li><li>Discussion Prompt: Design a network structure for the future </li></ul><div><strong>Module 4: Network Security </strong></div><ul><li>Stateless Inspection  </li><li>Stateful Inspection  </li><li>Firewall Filters- IDS and IPS Systems  </li><li>Hands-On-Lab: Protocols and Ports in Windows Defender </li><li>Hands-on Lab: Windows Firewall with Advanced Security </li><li>Using Your Microsoft Windows Lab Workspace </li><li>Hands-On-Lab: Configuring Firewalls in Microsoft Windows </li><li>Intrusion Detection and Prevention Systems Compared </li><li>Network Address Translation </li><li>Hands-On-Lab: Intrusion Detection System Activity </li><li>File Integrity Monitoring  </li><li>Data Loss Prevention (DLP)  </li><li>Network Access Control (C) </li><li>Endpoint Detection and Response (EDR)  </li><li>Extended Detection and Response (XDR)  </li><li>Practice Quiz: Network Security Techniques </li><li>Summary </li><li>Module 4 Glossary </li><li>Module 4 Graded Quiz </li></ul><div><strong>Module 5: Final Project  </strong></div><ul><li>Final Project Overview  </li><li>Final Project: Network Design and Security Configuration  </li><li>Peer-Graded Final Assignment </li></ul><div><strong>Course Rating and Feedback </strong></div><ul><li>Course Rating and Feedback </li></ul><div><strong>Badge </strong></div><ul><li>Badge Frequently Asked </li><li>Claim your badge here </li></ul><div><strong>Course Wrap Up </strong></div><ul><li>Course Wrap Up </li><li>Congratulations and Next Steps </li><li>Course Team and Acknowledgements </li><li>Copyright and Trademarks </li></ul><h1>General Information</h1><ul><li>This course is self-paced.  </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul>
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Network Fundamentals and Security

This course equips you with in-demand networking and network security skills, including configuring settings such as DHCP, securing networks, and using network diagnostic and protection tools. 

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  <div>Many organizations are increasing their security investments due to recent breaches, highlighting the growing demand for skilled IT support and cybersecurity professionals. Whether you’re new to tech or transitioning into a new role, this course offers the critical operating systems (OS) knowledge you need to advance your career. <br /><br />Operating Systems are foundational to all aspects of computing. In this course, you’ll explore key concepts shared across all OSes and dive into the unique features of Windows, Linux, and macOS. <br /><br /></div><div>You’ll learn about OS file systems and their security features, discover the innovations introduced by macOS, and explore the concepts of virtualization and containerization. Through hands-on labs and a final project, you’ll practice managing virus protection, configuring user accounts and permissions using command-line tools, and securing systems against various threats. <br /><br /></div><div>If you're looking to start a career in IT or cybersecurity, enroll today to gain essential OS knowledge that will help you move forward in roles such as cybersecurity, system administration, network engineering, or IT support. <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Boost your front-end development career by creating interactive web applications with JavaScript technologies like React, JSX, and ES6.</li><li>Quickly build dynamic client-side apps using reusable React components.</li><li>Enhance the flexibility and scalability of your components through features such as props, state, hooks, forms, and Redux.</li><li>Showcase your React skills by developing multiple front-end applications, such as a shopping cart.</li></ul><h1>Course Syllabus</h1><div>Welcome </div><ul><li>Video: Course Introduction (3:40)</li><li>General Information</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li></ul><div>Module 1: Windows Operating Systems</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: An Introduction to Operating Systems (7:48)</li><li>Video: Microsoft Windows Systems Overview (7:08)</li><li>Video: File Systems (5:19)</li><li>Video: Directory Structure (7:14)</li><li>Reading: File Naming Best Practices</li><li>Video: Using Your Windows Lab Workspace (3:51)</li><li>Lab: Using File Explorer to Manage Files and Folders'</li><li>Video: User and Kernel Modes (5:04)</li><li>Video: Microsoft Windows Server Overview (8:00)</li><li>Video: Using Your Microsoft Windows Lab Workspace (3:51)</li><li>Lab: Exploring Microsoft Windows Server Features</li><li>Video: Overview of Microsoft Windows Command Prompt Tools for Administration (6:19)</li><li>Reading: Using Microsoft Windows Command Prompt Tools for Administration</li><li>Lab: Use the Windows Command Prompt for Administration</li><li>Video: Microsoft Management Console (4:41)</li><li>Reading: Manage User Accounts</li><li>Lab: Creating Windows Users and Groups</li><li>Video: What is Active Directory? (4:42)</li><li>Module Summary: Microsoft Windows Operating System</li><li>Practice Quiz: Microsoft Windows Operating System</li><li>Module 1 Glossary: Microsoft Windows Operating System</li><li>Graded Quiz: Microsoft Windows Operating System</li></ul><div>Module 2: Windows Security</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Active Directory Accounts and Security Considerations (6:18)</li><li>Reading: Authentication Methods</li><li>Video: Windows Security Settings (3:50)</li><li>Video: User Management and Permissions (5:37)</li><li>Video: Antimalware Tools and Microsoft Windows Firewall Configuration (7:59)</li><li>Lab: Windows Defender Firewall</li><li>Lab: Creating a Firewall Rule in Microsoft Windows Defender</li><li>Video: Overview of Patching (6:03)</li><li>Video: Microsoft Update (5:50)</li><li>Video: Kerberos (7:14)</li><li>Video: Overview of Windows Auditing (5:38)</li><li>Module Summary: Windows Security</li><li>Practice Quiz: Windows Security</li><li>Module 2 Glossary: Windows Security</li><li>Graded Quiz : Windows Security</li></ul><div>Module 3: Linux Operating Systems</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Linux Systems Overview (5:11)</li><li>Video: Exploring Linux Operating System Essentials (6:49)</li><li>Video: Linux Terminal Overview (6:11)</li><li>Lab: Getting Started with Linux Terminal</li><li>Reading: Linux Commands</li><li>Video: Common Linux/Unix Shell Commands (6:11)</li><li>Lab: Common Linux/Unix Commands</li><li>Reading: Linux and Bash Command Cheat Sheet: The Basics</li><li>Lab: Linux User Management</li><li>Video: Linux File Systems and Directory Structure (5:22)</li><li>Reading: Linux Run Levels</li><li>Reading: Securing Linux</li><li>Lab: Updating Linux</li><li>Module Summary: Linux Operating Systems</li><li>Practice Quiz: Linux Operating Systems</li><li>Module 3 Glossary: Linux Operating Systems</li><li>Graded Quiz: Linux Operating Systems</li></ul><div><br />Module 4: macOS and Virtualization</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: macOS Overview (6:48)</li><li>Video: macOS Features and Tools (5:09)</li><li>Activity: Explore macOS Features</li><li>Reading: Upgrading macOS</li><li>Video: macOS System Preferences (2:52)</li><li>Reading: Updates for macOS</li><li>Video: Introduction to Mobile Devices: iOS Overview (7:53)</li><li>Video: Introduction to Mobile Devices: Android Overview (8:08)</li><li>Video: An overview of Virtualization (7:37)</li><li>Activity: Create a Virtual Machine Simulation</li><li>Video: Overview of Cloud Computing (6:39)</li><li>Reading: Differences Between Virtualization and Cloud Computing</li><li>Video: Introduction to Containers (6:13)</li><li>Reading: Differences Between Containers and Virtual Machines</li><li>Video: Introduction to Docker (3:36)</li><li>Lab: Introduction to Containers, Docker, and IBM Cloud Container Registry</li><li>Module Summary: macOS and Virtualization</li><li>Practice Quiz: macOS and Virtualization</li><li>Module 4 Glossary: macOS and Virtualization</li><li>Graded Quiz: macOS and Virtualization</li></ul><div>Module 5: Final Project</div><ul><li>Learning Objectives</li><li>Reading: Final Project Overview</li><li>Video: Using Your Microsoft Windows Lab Workspace (3:50)</li><li>Lab: Final Project Part 1: Windows</li><li>Lab: Final Project Part 2: Linux Tasks</li><li>Peer Review</li></ul><div>Course Wrap-Up</div><ul><li>Video: Course Wrap-Up (7:22)</li><li>Congratulations and Next Steps</li><li>Thanks from the Team</li><li>Copyrights and Trademarks</li></ul><div>Course Rating<br /><br />Digital Badge</div><div><br /></div><h1>General Information</h1><ul><li>This course is self-paced.  </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.</li></ul>
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Operating Systems and Security Fundamentals

This course covers the core concepts common to all operating systems, with a focus on the unique features of Windows, Linux, and macOS.

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  <div>Cyberattacks have been on the rise and are expected to continue growing, highlighting the increasing need for skilled cybersecurity professionals. Start your journey into the world of cybersecurity with this introductory IBM course, designed to introduce you to key concepts, common threats, and effective preventive measures. <br /><br />Dive into the evolution of cybersecurity, explore the motivations behind cyberattacks, and learn about various types of threats such as malware and ransomware. The course will also help you apply critical thinking to common cybersecurity practices and architectural frameworks. <br /><br /></div><div>Gain valuable insights from industry experts who share their career journeys, skills, and perspectives. You'll also explore the role of cybersecurity frameworks, standards, and organizations in protecting against attacks. <br /><br /></div><div>Throughout the course, you’ll build hands-on technical skills and practical knowledge through interactive labs. The final project will allow you to showcase your understanding of key cybersecurity principles. <br /><br /></div><div>This course is perfect for anyone interested in learning the basics of cybersecurity and will help you launch a career as a Cybersecurity Analyst.<br /><br /></div><h1>Learning Outcomes</h1><ul><li>Start your career in cybersecurity with this course, covering essential foundational concepts. </li><li> Learn how to apply key cybersecurity principles, tools, and strategies to safeguard both digital and physical assets. </li><li> Identify common security threats and best practices to protect against them. </li><li> Understand authentication and identity management techniques to secure your organization. </li></ul><h1>Course Syllabus</h1><div>Welcome</div><ul><li>Video: Course Introduction</li><li>General Information</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li><li>Helpful Tips for Course Completion</li></ul><div>Module 1: Cybersecurity Insights</div><ul><li>Learning Objectives and Module Introduction</li><li>Video: The Impact of the WarGames Movie on Cybersecurity</li><li>Video: The Impact of 9/11 on Cybersecurity</li><li>Video: The Impact of Covid 19 on Cybersecurity</li><li>Reading: The History of Cybersecurity</li><li>Practice Quiz</li><li>Video: Critical thinking in Cybersecurity</li><li>Video: Critical Thinking: A Model</li><li>Video: Critical Thinking: 5 key Skills</li><li>Activity: Would You Rather?</li><li>Practice Quiz</li><li>Module 1 Summary: Cybersecurity Insights</li><li>Graded Quiz: Cybersecurity Insights</li><li>Module 1 Glossary: Cybersecurity Insights</li></ul><div>Module 2: Cybersecurity Threats</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Types of Actors and Their Motives</li><li>Reading: Insider Attacks</li><li>Video: Cybersecurity Trends</li><li>Activity: Exploring IBM's X-Force Threat Intelligence Index</li><li>Practice Quiz</li><li>Video: What is Malware? Let's Hear the Hacker's Viewpoint</li><li>Video: What is Ransomware?</li><li>Reading: Malware Types</li><li>Video: How to Prevent Malware Attacks</li><li>Reading: Malware Detection with Rootkit Hunters</li><li>Hands-on Lab: Malware Scan using Rkhunter</li><li>Practice Quiz</li><li>Video: Social Engineering - How Bad Guys Hack Users</li><li>Reading: Types of social engineering</li><li>Video: What is Phishing?</li><li>Video: Humans vs. AI Who's Better at Phishing?</li><li>Reading: How to Prevent Social Engineering</li><li>Practice Quiz</li><li>Module 2 Summary: Cybersecurity Threats</li><li>Module 2 Graded Quiz: Cybersecurity Threats</li><li>Module 2 Glossary: Cybersecurity Threats</li></ul><div>Module 3: Cybersecurity Controls</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Internet Security Threats: Mapping</li><li>Video: Internet Security Threats: Packet Sniffing</li><li>Video: Internet Security Threats: IP Spoofing</li><li>Video: Internet Security Threats: Denial-of-Service Attacks</li><li>Video: Internet Security Threats: Injection Attacks</li><li>Practice Quiz</li><li>Video: Security Controls</li><li>Reading: Types of Controls</li><li>Video: System Security</li><li>Video: Network Security</li><li>Video: Application Security by Design</li><li>Video: Vulnerability Management</li><li>Video: Incident Response and Digital Forensics</li><li>Practice Quiz</li><li>Module 3 Summary: Cybersecurity Controls</li><li>Module 3 Graded Quiz: Cybersecurity Controls</li><li>Module 3 Glossary: Cybersecurity Controls</li></ul><div>Module 4: Identity and Physical Controls</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Identity and Access Management (IAM)</li><li>Video: Authentication</li><li>Video: Authorization</li><li>Video: Access Controls</li><li>Video: What is Multi-Factor Authentication?</li><li>Activity: Enable Two-Factor Authentication</li><li>Video: What is Single Sign On (SSO)?</li><li>Video: Passkeys FIDO</li><li>Practice Quiz</li><li>Video: Physical Threats and Controls</li><li>Reading: Physical Cybersecurity Controls</li><li>Practice Quiz</li><li>Module 4 Summary: Identity and Physical Controls</li><li>Module 4 Graded Quiz: Identity and Physical Controls</li><li>Module 4 Glossary: Identity and Physical Controls</li></ul><div>Module 5: Final Project - Secure Access</div><ul><li>Module Introduction and Learning Objectives</li><li>Reading: Final Project Overview</li><li>Final Project: Secure Access</li></ul><div>Course Wrap-Up</div><ul><li>Video: Course Wrap-up</li><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li><li>Copyrights and Trademarks</li></ul><div>Digital Badge</div><ul><li>Claim your Badge Here</li></ul><div>Course Rating and Feedback</div><ul><li>Course Rating and Feedback</li></ul><h1>General Information</h1><ul><li>This course is self-paced.  </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul>
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Cybersecurity Tools and Cyberattacks Fundamentals

Identify threats, implement security measures, and protect data. Jumpstart your cybersecurity career with this introductory course.

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  <div>Education is evolving, and AI-powered learning is transforming how students engage with knowledge. Some learners need detailed explanations, while others prefer quick, to-the-point answers. That’s where DeepSeek-R1 and Distill-Llama-70B step in, each offering a unique approach to AI-driven education.</div><div><br />DeepSeek-R1 is like a seasoned professor, providing comprehensive, well-structured explanations—ideal for students who need deep learning and thorough understanding. Distill-Llama-70B, in contrast, acts as an efficient tutor, delivering fast, accurate responses without compromising clarity, making it perfect for quick learning and interactive quizzes.</div><div><br />While R1 ensures a strong foundation through detailed insights, Distill-Llama-70B prioritizes speed and efficiency, catering to students who need rapid comprehension. Both models are shaping the future of AI-powered education, adapting to different learning styles and making learning more accessible, engaging, and effective.</div><div><br /></div><h1>A Look at the Project Ahead</h1><div>You will be able to do as a result of completing your project:</div><ul><li><strong>Learn DeepSeek’s different models</strong> for AI-powered education.</li><li><strong>Compare DeepSeek’s different models</strong> to find the best one for AI teaching.</li><li><strong>Generate personalized lesson plans</strong> using DeepSeek AI.</li><li><strong>Train AI to answer student questions</strong> with DeepSeek's advanced reasoning.</li></ul><div><br /></div><h1>What You'll Need</h1><div>Before starting this project, ensure you have:</div><ul><li><strong>Technical Requirements:</strong> A basic understanding of Python programming, including familiarity with libraries such as os for environment variable management.</li><li><strong>Browser Setup:</strong> A web browser for accessing tools and running code.</li></ul><div>By the end of this project, you'll be equipped with the knowledge and skills to effectively implement DeepSeek R1 in your applications, opening doors to innovative AI-driven solutions.</div>
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DeepSeek R1 Reasoning Model Compared for Educational Chatbot

In this guided project, you’ll explore different versions of DeepSeek-R1, large-parameter R1 and Distilled models, to create an AI-powered educational chatbot that enhances learning. Learn how to use DeepSeek-R1 to analyze student behavior, simplify complex concepts, and generate thought-provoking questions to personalize learning. Master DeepSeek’s advanced AI reasoning to provide real-time feedback and create adaptive learning experiences that truly engage users. Whether you're an educator or AI enthusiast, this project equips you with the skills to create smarter more effective AI solutions

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  <div>In today’s digital landscape, data is essential to the global economy, and cyberattacks often focus on stealing sensitive information or compromising systems. <br /><br />If you're interested in starting a career in cybersecurity or simply want to learn how it works, this five-module course on Cybersecurity Architecture is perfect for you. You’ll be guided by an experienced IBM cybersecurity engineer with extensive knowledge of the field. <br /><br /></div><div>This course covers the core fundamentals of cybersecurity, including the five security principles, the CIA triad, Identity and Access Management (IAM), endpoint security, and security domains. You’ll also dive into detection and response strategies, gaining a well-rounded understanding of prevention, detection, and response in cybersecurity. <br /><br /></div><div>Through practical activities, labs, and quizzes, you’ll apply what you’ve learned and test your knowledge at each step. Explore the world of cybersecurity and build a strong foundation for your future career! <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Explain the five core principles of cybersecurity and the key concepts of the Confidentiality, Integrity, and Availability (CIA) triad </li><li>Outline the various areas a cybersecurity architect must address to design a secure enterprise solution. </li><li>Discuss key security domains, such as IAM, endpoint security, network security, application security, and data security </li><li>Show your ability to recommend strategies for enhancing network security in a specific scenario </li></ul><h1>Course Syllabus</h1><div>Welcome</div><ul><li>Video: Course Introduction (3:59)</li><li>General Information</li><li>Prerequisites and Course Syllabus</li><li>Grading Scheme</li><li>Helpful Tips for Course Completion</li></ul><div><br />Module 1: Cybersecurity Architecture Overview</div><ul><li>Module 1 Introduction and Learning Objectives</li><li>Video: Five Principles to Follow (and One to Avoid) (17:02)</li><li>Reading: Key Takeaways: Five Principles</li><li>Reading: How to Implement Least Privilege Security</li><li>Activity: Apply the Concept of Least Privilege</li><li>Video: Fundamentals of Confidentiality, Integrity, and Availability (12:25)</li><li>Reading: Key Takeaways: CIA Fundamentals</li><li>Reading: CIA Triad</li><li>Activity: Identify Compromised CIA Principles</li><li>Video: Roles and Tools (13:57)</li><li>Reading: Key Takeaways: Roles and Tools</li><li>Reading: NIST Cybersecurity Framework</li><li>Module 1 Summary: Cybersecurity Architecture Overview</li><li>Module 1 Glossary: Cybersecurity Architecture Overview</li><li>Module 1 Practice Quiz: Cybersecurity Architecture Overview</li><li>Module 1 Graded Quiz: Cybersecurity Architecture Overview</li></ul><div>Module 2 Access Management and Endpoint Security</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Who Are You? Identity and Access Management Really Wants to Know (31:04)</li><li>Reading: Key Takeaways: IAM</li><li>Reading: Understanding Multi-Factor Authentication</li><li>Activity: Apply Multi-Factor Authentication</li><li>Video: Endpoints Are the IT Front Door - Guard Them (13:51)</li><li>Reading: Key Takeaways: Endpoint Security</li><li>Reading: Endpoint Management with IBM</li><li>Activity: Implementing Endpoint Management</li><li>Module 2 Summary: Access Management and Endpoint Security</li><li>Module 2 Glossary: Access Management and Endpoint Security</li><li>Module 2 Practice Quiz: Access Management and Endpoint Security</li><li>Module 2 Graded Quiz: Access Management and Endpoint Security</li></ul><div>Module 3 Network, Application, and Data Security</div><ul><li>Module 3 Introduction and Learning Objectives</li><li>Video: Network Security (27:23)</li><li>Reading: Key Takeaways: Network Security</li><li>Reading: Securing Network Infrastructure</li><li>Video: Lab Demo (3:18)</li><li>Hands-on Lab: Secure Network Diagram</li><li>Video: Application Security (16:27)</li><li>Reading: Key Takeaways: Application Security</li><li>Reading: Secure Coding Practices</li><li>Video: Data Security (14:39)</li><li>Reading: Key Takeaways: Data Security</li><li>Reading: Data Security: Why a Proactive Stance is Best</li><li>Reading: Quantum Safe Encryption</li><li>Module 3 Summary: Network, Application, and Data Security</li><li>Module 3 Glossary: Network, Application, and Data Security</li><li>Module 3 Practice Quiz: Network, Application, and Data Security</li><li>Module 3 Graded Quiz: Network, Application, and Data Security</li></ul><div>Module 4 Detection and Response</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Detection (17:01)</li><li>Reading: Key Takeaways: Detection</li><li>Reading: Indicators of Compromise</li><li>Reading: Threat Intelligence</li><li>Hands-on Lab: Review Recent Security Threat Reports using X-Force Threat Exchange</li><li>Video: Response (15:49)</li><li>Reading: Key Takeaways: Response</li><li>Reading: Incident Response Frameworks</li><li>Activity: Incident Response</li><li>Reading: Case Study - Stopping Cyberattacks at a Major International Airport</li><li>Module 4 Summary: Detection and Response</li><li>Module 4 Glossary: Detection and Response</li><li>Module 4 Practice Quiz: Detection and Response</li><li>Module 4 Graded Quiz: Detection and Response</li></ul><div>Module 5 Final Project and Exam</div><ul><li>Module Introduction and Learning Objectives'</li><li>Reading: Final Project: Recommendations to Improve Network Security</li><li>Final Project Graded Quiz: Recommendations to Improve Network Security</li><li>Reading: Case Study: Network Architecture Powered by IBM</li><li>Course Glossary: Cyber Defense Architecture</li><li>Final Exam: Cyber Defense Architecture</li></ul><div>Course Wrap-Up</div><ul><li>Course Summary: Cyber Defense Architecture</li><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li><li>Copyrights and Trademarks</li></ul><div>Course Rating and Feedback<br /><br />Badge<br /><br /></div><h1>General Information</h1><ul><li>This course is self-paced.  </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Cybersecurity and Networking Fundamentals knowledge.  </div>
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Digital Security Architecture

Learn the fundamentals of cybersecurity architecture, including the CIA triad, IAM, endpoint security, and detection tools. 

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  <h1>About this course</h1><div>In this capstone project course you will apply your deep learning skills and demonstrate your mastery with deep neural networks.</div><div> </div><div>You’ll begin by loading data in PyTorch and Keras. You will explore the various methods and tools to efficiently handle datasets in both libraries, providing a foundation for model development.</div><div> </div><div>Next, the course covers data preprocessing. You’ll prepare images and other data for model training in both PyTorch and Keras. Through labs, you will gain experience in applying different preprocessing techniques such as scaling, normalization, and augmentation.</div><div> </div><div>The course then focuses on model training. You’ll apply the concept of pre-trained models and how to use them in both PyTorch and Keras. By participating in hands-on labs and peer reviews, you will develop a deeper understanding of model training, fine-tuning, and optimization.</div><div> </div><div>Once familiar with the training process, you will compare the performance of models built with PyTorch and Keras. In this section, you will analyze and evaluate the two frameworks, and assess the advantages and challenges of each through comparative exercises.</div><div> </div><div>Finally, the course concludes with a section dedicated to assessing the models you’ve trained. By using pre-trained models and comparing them to custom-built models, you’ll refine your evaluation techniques, gaining insight into the strengths and weaknesses of different model architectures.<br /><br /></div><h1>What you will learn: </h1><div>After completing this course, you will be able to: </div><ul><li>Demonstrate mastery in deep learning.</li><li>Describe the process of loading data into PyTorch and Keras, and the methods available for preprocessing data.</li><li>Apply data loading and preprocessing techniques in PyTorch and Keras to prepare datasets for machine learning tasks.</li><li>Analyze and compare the performance of models trained in PyTorch and Keras, evaluating the impact of different architectures and preprocessing methods.</li><li>Evaluate pre-trained models and assess their performance for real-world machine learning tasks.</li><li>Create workflows to train, compare, and assess models using PyTorch and Keras.</li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Chapter 1 - Loading Data<br /></strong><br /></div><div><strong>Getting Started</strong></div><ul><li>Getting Started</li><li>Learning Objectives</li></ul><div><strong>1.1 Loading Data - PyTorch</strong></div><ul><li>Video: Loading Data - PyTorch</li><li>Lab: Loading Data - PyTorch</li><li>Quiz: Loading Data - PyTorch</li></ul><div><strong>1.2 Loading Data - Keras</strong></div><ul><li>Video: Loading Data -Keras</li><li>Lab: Loading Data - Keras</li><li>Quiz: Loading Data - Keras</li></ul><div><br /><strong>Chapter 2 - Data Preparation</strong></div><ul><li>Learning Objectives</li></ul><div><strong>2.1 Processing Data - PyTorch</strong></div><ul><li>Video: Image Preprocessing PyTorch</li><li>Lab: Processing Data - PyTorch</li><li>Quiz: Processing Data - PyTorch</li></ul><div><strong>2.2 Processing Data - Keras</strong></div><ul><li>Video: Processing Data - Keras</li><li>Lab: Processing Data - Keras</li><li>Quiz: Processing Data - Keras</li></ul><div><br /><strong>Chapter 3 - Assessment Training Model</strong></div><ul><li>Learning Objectives</li></ul><div><strong>3.1 Training Models - PyTorch</strong></div><ul><li>Video: Pre-trained Models PyTorch</li><li>Lab: Training Model - PyTorch</li><li>Peer Review: Training Models - PyTorch</li></ul><div><strong>3.2 Training Models - Keras</strong></div><ul><li>Video: Pre-trained Models in Keras</li><li>Lab: Training Models - Keras</li><li>Peer Review: Training Models - Keras</li></ul><div><br /><strong>Chapter 4 - Compare Two Models</strong></div><ul><li>Learning Objectives</li></ul><div><strong>4.1 Compare Two Models - PyTorch</strong></div><ul><li>Instruction: Compare Two Models - PyTorch</li><li>Lab: Compare Two Models - PyTorch</li><li>Quiz: Compare Two Models - PyTorch</li></ul><div><strong>4.2 Compare Two Models - Keras</strong></div><ul><li>Instruction: Compare Two Models - Keras</li><li>Lab: Compare Two Models - Keras</li><li>Quiz: Compare Two Models - Keras</li></ul><div><strong>Course Rating and Feedback</strong></div><ul><li>Course Rating and Feedback</li></ul><div><strong>Badge</strong></div><ul><li>Badges Frequently Asked Questions</li><li>Claim your badge here</li></ul><div><br /></div><h1>General Information</h1><ul><li>This course is self-paced. </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>The following skills are required to be successful with this course: </div><ul><li>High School level Mathematics</li><li>Working knowledge of Python.</li><li>Machine Learning with Python</li><li>Fundamentals of Deep Learning</li><li>Working knowledge of PyTorch</li><li>Working knowledge of Keras/Tensorflow</li></ul>
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Deep Learning Capstone Project

Demonstrate your mastery in deep learning with PyTorch and Keras. Compare models, use pre-trained models, train new models, and optimize machine learning workflows with practical hands-on labs.

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  <h1>About this course</h1><div>In this course, you will begin by understanding the foundations of PyTorch for deep learning. You’ll explore how to calculate and implement logistic regression using PyTorch for binary classification tasks.</div><div> </div><div>Next, you’ll dive into SoftMax regression and how it’s used for multi-class classification. By learning to implement the SoftMax function in PyTorch, you will gain an understanding of how to apply SoftMax regression to multi-class problems and fine-tune models accordingly.</div><div> </div><div>The course then shifts to shallow neural networks, where you will explore how neural networks function with a single input layer and gradually progress to networks with multiple hidden layers. Through practical exercises, you will implement backpropagation and activation functions, which are crucial for effective learning in neural networks.</div><div> </div><div>Deep neural networks are introduced, and you’ll learn to design more complex architectures. Techniques such as dropout for regularization, the role of weight initialization, and momentum in gradient descent will be covered. Additionally, batch normalization will be explored to stabilize and accelerate training.</div><div> </div><div>Next, you will study convolutional neural networks (CNNs), which are especially powerful for image classification. You will implement CNNs using PyTorch, learning to work with convolution layers, pooling layers, and multiple input-output channels. Furthermore, you’ll explore how to integrate GPUs for faster computation and take advantage of PyTorch’s torch vision models.</div><div> </div><div>The course culminates in a hands-on final project where you’ll apply the concepts you’ve learned by building a classification model for the Fashion MNIST dataset. This project will allow you to put all the skills into practice, from model development to fine-tuning and evaluation.</div><div><br />  </div><h1>What you will learn: </h1><div>After completing this course, you will be able to: </div><ul><li>Explain the concept of logistic regression, cross-entropy loss, and the SoftMax function.</li><li>Apply PyTorch for implementing logistic regression, SoftMax regression, and neural network models.</li><li>Analyze the role of activation functions, backpropagation, and regularization techniques like dropout in deep neural networks.</li><li>Evaluate the effectiveness of deep learning architectures, including shallow and convolutional neural networks, in various machine learning tasks.</li><li>Create and optimize complex neural networks for classification tasks, utilizing techniques such as batch normalization and gradient descent with momentum.</li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Logistic Regression Cross Entropy Loss  <br /></strong><br /></div><div><strong>Welcome</strong></div><ul><li>Video: Course Introduction</li><li>Reading: Course Introduction</li><li>Reading: Course Overview</li><li>Helpful Tips for Course Completion</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li></ul><div><strong>Logistic Regression Cross Entropy Loss  </strong></div><ul><li>Logistic Regression Cross Entropy Loss</li><li>Logistic Regression Mean Square Error</li><li>Logistic Regression Cross Entropy  </li><li>Module-level Graded Quiz: Logistic Regression Cross Entropy Loss </li></ul><div><br /><strong>Module 2: Softmax Regression </strong></div><div><br /><strong>Softmax Prediction </strong></div><ul><li>Softmax</li></ul><div><strong>Softmax function</strong></div><ul><li>Softmax function: Using Lines to classify data Prediction</li></ul><div><strong>Softmax PyTorch </strong></div><ul><li>Softmax PyTorch</li><li>Softmax Classifier 1</li><li>Softmax Classifier 2</li><li>Module-level Graded Quiz: Softmax Regression   </li></ul><div><br /><strong>Module 3: Shallow Neural Networks   <br /></strong><br /></div><div><strong>Neural Networks in One Dimension </strong></div><ul><li>What’s a neural network?</li><li>Neural Networks in One Dimension</li><li>Graded Quiz:Neural Networks </li></ul><div><strong>Neural Networks More Hidden Neurons </strong></div><ul><li>More Hidden Neurons </li><li>More Hidden Neurons </li></ul><div><strong>Neural Networks with Multiple Dimensional Input</strong></div><ul><li>Neural Networks with Multiple Dimensional Input</li><li>Multi-Dimensional Neural Networks  </li></ul><div><strong>Multi-Class Neural Networks </strong></div><ul><li>Multi-Class Neural Networks</li><li>Multi-Class Neural Networks with MNIST</li></ul><div><strong>Backpropagation</strong></div><ul><li>Backpropagation</li></ul><div><strong>Activation Functions </strong></div><ul><li>Activation Functions </li><li>Activation Functions </li><li>Neural network with different Activation Functions </li><li>Module-level Graded Quiz:Multi-Class Neural Networks </li></ul><div><br /><strong>Module 4: Deep Networks  <br /></strong><br /></div><div><strong>Deep Neural Networks</strong></div><ul><li>Deep Neural Networks</li><li>Deep Neural Networks</li><li>Graded Quiz: Deep Neural Networks</li><li>Deeper Neural Networks: nn.ModuleList()</li><li>Deeper Neural Networks : nn.ModuleList()</li></ul><div><strong>Dropout</strong></div><ul><li>Dropout</li><li>Dropout Classification</li><li>Dropout Regression</li></ul><div><strong>Neural Network Initialization Weights </strong></div><ul><li>The Role of Dropout in Regularization and Model Generalization</li><li>Neural Network Initialization Weights  </li><li>Initialization with Same Weights</li><li>Initialization Xavier</li><li>Understanding the Importance of Initialization Techniques in Deep Learning</li></ul><div><strong>Gradient Descent with Momentum </strong> </div><ul><li>Gradient Descent with Momentum</li><li>Momentum with Different Polynomials </li><li>Neural networks with momentum </li></ul><div><strong>Batch Normalization</strong></div><ul><li>Batch Normalization   </li><li>Batch Normalization with the MNIST Dataset</li><li>Graded Quiz: Batch Normalization   </li><li>Module-level Graded Quiz:Deep Networks</li></ul><div><br /><strong>Module 5: Convolutional Neural Networks <br /></strong><br /></div><div><strong>Convolution</strong></div><ul><li>Convolution </li><li>What's Convolution </li></ul><div><strong>Activation Functions and Max Pooling  </strong> </div><ul><li>Activation Functions and Max Pooling  </li><li>Activation Functions and Max Pooling  </li></ul><div><strong>Multiple Input and Output Channels</strong>   </div><ul><li>Multiple Input and Output Channels</li><li>Multiple Input and Output Channels</li></ul><div><strong>Convolutional Neural Network </strong></div><ul><li>Convolutional Neural Network </li><li>Convolutional Neural Network for MNIST  </li><li>Convolutional Neural Network Simple example </li><li>Convolutional Neural Network MNIST </li><li>Convolutional Neural Networks with Batch Norm (Honors only) </li></ul><div><strong>Torch Vision Models </strong></div><ul><li>Torch Vision Models </li></ul><div><strong>Graphics Processing Unit  </strong> </div><ul><li>Graphics Processing Unit</li><li>GPU Setup</li><li>Module-level Graded Quiz: Convolutional Neural Networks</li></ul><div><br /><strong>Module 6: Final Project <br /></strong><br /></div><div><strong>Final Project</strong></div><ul><li>Practice Project</li><li>Final Project</li><li>Fashion MNIST Classification Assignment</li></ul><div><strong>Course Wrap-up </strong></div><ul><li>Congratulations and Next Steps </li><li>Thanks from the Course Team </li></ul><div><br /></div><h1>General Information</h1><ul><li>This course is self-paced. </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>The following skills are required to be successful with this course: </div><ul><li>High School level Mathematics</li><li>Working knowledge of Python.</li><li>Machine Learning with Python</li><li>Fundamentals of Deep Learning</li><li>Working knowledge of PyTorch</li></ul>
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PyTorch for Deep Learning

Learn key concepts in deep learning, from logistic and softmax regression to shallow & deep neural networks. Implement these models in PyTorch, mastering techniques like backpropagation, dropout, and convolutional networks for machine learning tasks.

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  <h1>About this course</h1><div>In this intermediate course, you’ll learn the essentials of PyTorch for machine learning techniques. Starting with tensors, you’ll understand their role in machine learning and how to perform basic operations with them. You’ll also explore differentiation in PyTorch, which is key for optimizing models. With hands-on labs, you’ll apply these concepts to work with simple and Torch Vision datasets, gaining experience in handling data for model training.</div><div> </div><div>You’ll then move into regression techniques, beginning with linear regression, where you’ll learn how to predict outcomes and optimize models using gradient descent. You’ll understand key concepts like loss functions and the steps involved in model training, which will prepare you for more complex machine learning tasks.</div><div> </div><div>The course will also cover more advanced regression techniques, including multiple input-output linear regression. You’ll learn how to handle multi-dimensional data, make predictions with multiple features and outputs, and train more sophisticated models.</div><div> </div><div>Classification is another key area of focus, where you’ll dive into logistic regression and learn how to apply it to solve classification problems. Through exercises, you’ll explore how to use linear classifiers, apply loss functions like Cross Entropy Loss, and implement effective models for binary classification.</div><div> </div><div>Finally, you’ll apply your learning to real-world problems. The practice project will involve building a neural network to classify breast cancer data, while the final project will challenge you to develop a League of Legends match predictor. You’ll work through data preparation, model building, and optimization, while receiving peer feedback to refine your skills.</div><div> </div><div>By the end of the course, you’ll be equipped to build and optimize machine learning models with PyTorch, solving regression and classification problems efficiently and effectively. You’ll gain the experience necessary to tackle machine learning challenges and develop your skills further in this rapidly growing field.<br /><br /></div><h1>What you will learn: </h1><div>After completing this course, you will be able to: </div><ul><li>Define tensors and understand their applications in PyTorch.</li><li>Explain linear regression and how to apply it using PyTorch.</li><li>Implement stochastic and mini-batch gradient descent algorithms.</li><li>Compare different methods of regression (linear, multiple) in terms of predictions and loss functions.</li><li>Assess the performance of a model using training, validation, and test splits.</li><li>Develop a final project that applies learned techniques to classify or predict outcomes in a dataset.</li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Tensors 1D<br /></strong><br /></div><div><strong>Welcome</strong></div><ul><li>Video: Course Introduction</li><li>Reading: Course Overview</li><li>General Information</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li><li>Helpful Tips for Course Completion</li><li>Video: Overview of Tensors </li></ul><div><strong>Tensors 1D</strong></div><ul><li>Errata:Tensors ID</li><li>Video: Tensors 1D </li><li>Reading: Tensors 1D </li><li>Lab2: Tensors 1D </li></ul><div><strong>Two-Dimensional Tensors</strong></div><ul><li>Errata:Two-Dimensional Tensors </li><li>Video: Two-Dimensional Tensors </li><li>Lab: Two-Dimensional Tensors </li></ul><div><strong>Derivatives in PyTorch</strong></div><ul><li>Video: Differentiation in PyTorch </li><li>Lab: Differentiation in PyTorch </li></ul><div><strong>Simple Dataset</strong></div><ul><li>Video: Simple Dataset </li><li>Lab: Simple Dataset</li></ul><div><strong>Dataset</strong></div><ul><li>Video: Dataset </li><li>Lab: Dataset </li><li>Lab: Torch Vision Datasets </li><li>Module-level Graded Quiz: Tensor and Dataset</li></ul><div><br /><strong>Module 2: Linear Regression<br /></strong><br /></div><div><strong>Linear Regression Prediction</strong></div><ul><li>Video: Linear Regression Prediction </li><li>Lab: Linear Regression 1D: Prediction  </li></ul><div><strong>Linear Regression Training</strong></div><ul><li>Errata: Linear Regression Training  </li><li>Video: Linear Regression Training  </li><li>Video: Loss  </li></ul><div><strong>Gradient Descent and Cost</strong></div><ul><li>Video: Gradient Descent and Cost </li><li>Video: Cost  </li></ul><div><strong>PyTorch Slope</strong></div><ul><li>Video: Linear Regression PyTorch </li><li>Lab: Linear Regression ID: Training one Parameter</li></ul><div><strong>Linear Regression Training</strong></div><ul><li>Errata: PyTorch Linear Regression Training Slope and Bias  </li><li>Video: PyTorch Linear Regression Training Slope and Bias  </li><li>Lab: Linear Regression: Prediction </li><li>Module-level Graded Quiz: Linear Regression</li></ul><div><br /><strong>Module 3: Linear Regression PyTorch Way<br /></strong><br /></div><div><strong>Stochastic Gradient Descent and Data Loader</strong></div><ul><li>Video: Stochastic Gradient Descent  </li><li>Lab: Stochastic Gradient Descent and Data Loader   </li></ul><div><strong>Mini-Batch Gradient Descent</strong></div><ul><li>Video: Mini-Batch Gradient Descent  </li><li>Lab: Mini-Batch Gradient Descent  </li></ul><div><strong>Optimization in PyTorch</strong></div><ul><li>Video: Optimization in PyTorch </li><li>Lab: Optimization in PyTorch </li></ul><div><strong>Training, Validation, and Test Split</strong></div><ul><li>Video: Training, Validation, and Test Split </li><li>Errata: Training, Validation, and Test Split PyTorch</li><li>Video: Training, Validation, and Test Split PyTorch </li><li>Lab: Training, Validation, and Test Split in PyTorch </li><li>Module-level Graded Quiz: Linear Regression PyTorch Way</li></ul><div><br /><strong>Module 4: Multiple Input Output Linear Regression<br /></strong><br /></div><div><strong>Multiple Linear Regression Prediction</strong></div><ul><li>Video: Multiple Linear Regression Prediction </li><li>Lab: Multiple Linear Regression Prediction </li><li>Video: Multiple Linear Regression Training </li><li>Lab: Multiple Linear Regression Training </li></ul><div><strong>Multiple Output Linear Regression</strong></div><ul><li>Video: Linear Regression Multiple Outputs  </li><li>Lab: Multiple Linear Regression Prediction </li><li>Video: Multiple Output Linear Regression Training </li><li>Lab: Multiple Linear Regression Training </li><li>Module-level Graded Quiz: Multiple Input Output Linear Regression </li></ul><div><br /><strong>Module 5: Logistic Regression for Classification<br /></strong><br /></div><div><strong>Linear Classier</strong></div><ul><li>Video: Linear Classifiers  </li></ul><div><strong>Logistic Regressioin Prediction</strong></div><ul><li>Video: Logistic Regression Prediction   </li><li>Lab: Logistic Regression Prediction  </li></ul><div><strong>Bernoulli Distribution and Maximum Likelihood Estimation</strong></div><ul><li>Video: Bernoulli Distribution and Maximum Likelihood Estimation </li></ul><div><strong>Logistic Regression Cross Entropy Loss</strong></div><ul><li>Video: Logistic Regression Cross Entropy Loss    </li><li>Lab: Logistic Regression Mean Square Error </li><li>Lab: Logistic Regression Cross Entropy  </li><li>Module-level Graded Quiz: Linear Classifiers</li></ul><div><br /><strong>Module 6: Practice Project and Final Project<br /></strong><br /></div><div><strong>Practice Project and Final Project</strong></div><ul><li>Practice Project: Neural Network for Breast Cancer Classification</li><li>Reading: Final Project Overview</li><li>Final Project: League of Legends Match Predictor</li><li>Reading Peer-Review: Final Project League of Legends Match Predictor</li></ul><div><strong>Course Wrap-Up</strong></div><ul><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li><li>Reading: Copyrights and Trademarks</li></ul><h1>General Information</h1><ul><li>This course is self-paced. </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>The following skills are required to be successful with this course: </div><ul><li>High School level Mathematics</li><li>Working knowledge of Python.</li><li>Machine Learning with Python</li><li>Fundamentals of Deep Learning</li></ul>
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PyTorch Fundamentals for Machine Learning

Learn the fundamentals of PyTorch for machine learning in this course. Topics include tensors, linear regression, logistic regression, and optimization techniques like gradient descent. Apply your skills through hands-on projects and quizzes.

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  <h1>About this course</h1><div>In this Intermediate course, you will gain an in-depth understanding of Keras and its functionalities. Learn how to use the Keras Functional API to design flexible and complex models, and explore subclassing to create custom layers and models. You will become proficient in building custom deep learning architectures, utilizing TensorFlow 2.x to enhance model performance.</div><div> </div><div>You will dive into advanced techniques in convolutional neural networks (CNNs) and data augmentation. Learn how to augment data effectively to improve model accuracy and performance, and apply transfer learning using pre-trained models for image classification. You’ll also explore the use of transpose convolutions in image processing tasks.</div><div> </div><div>The course also covers unsupervised learning techniques, such as autoencoders and diffusion models, allowing you to create and train generative models like GANs. You’ll discover how to use these methods to generate new data and work with complex unsupervised tasks, laying the foundation for powerful machine learning applications.</div><div> </div><div>You will further enhance your skills by mastering advanced Keras techniques, such as custom training loops, hyperparameter tuning, and model optimization. Through hands-on labs, you’ll learn how to fine-tune models and optimize them for better accuracy and efficiency, equipping you with the tools to improve any deep learning model.</div><div> </div><div>The course also introduces reinforcement learning, where you will learn the principles behind Q-learning and deep Q-networks (DQNs). Gain practical experience in building and implementing these models, exploring their potential in solving decision-making problems and real-world applications.</div><div> </div><div>Finally, you will apply your new knowledge by tackling a real-world deep learning project. Using transfer learning, you will classify waste products, gaining hands-on experience in practical model deployment and problem-solving. By the end of the course, you will have a strong grasp of the latest advancements in deep learning and the skills needed to build and deploy cutting-edge models in Keras.<br /><br /></div><h1>What you will learn:</h1><div>After completing this course, you will be able to: </div><ul><li>Define advanced deep learning concepts, including Keras APIs, CNNs, transformers, GANs, and reinforcement learning principles.</li><li>Explain the inner workings of custom layers, data augmentation, transfer learning, and reinforcement learning algorithms.</li><li>Implement advanced techniques like custom training loops, model optimization, and generative models in Keras.</li><li>Compare and contrast CNNs, transformers, and unsupervised learning models, assessing their strengths for different use cases.</li><li>Optimize deep learning models through hyperparameter tuning and reinforcement learning strategies.</li><li>Design and develop advanced deep learning models using Keras, including transformers, GANs, and reinforcement learning models, and implement them in practical scenarios.</li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Advanced Keras Functionalities  <br /></strong><br /></div><div><strong>Welcome to the Course</strong></div><ul><li>Video: Course Introduction</li><li>Reading: Course Overview</li><li>Plugin/Reading: Helpful Tips for Course Completion</li></ul><div><strong>Advanced Keras Functional API </strong></div><ul><li>Video: Introduction to Advanced Keras </li><li>Video: Keras Functional API  and Subclassing API</li><li>Lab: Implementing the Functional API in Keras</li><li>Practice Quiz: Advanced Keras Functional API </li></ul><div><strong>Custom Layers with Keras</strong></div><ul><li>Video: Creating Custom Layers in Keras </li><li>Video: Overview of TensorFlow 2.x </li><li>Lab: Creating Custom Layers and Models </li><li>Practice Quiz: Custom Layers with Keras</li></ul><div><strong>Advanced Keras Functionalities  Summary</strong></div><ul><li>Reading: Summary and Highlights: Advanced Keras Functionalities </li><li>Reading: Glossary: Advanced Keras Functionalities </li><li>Graded Quiz: Advanced Keras Functionalities  </li><li>Discussion Prompt: Meet and Greet [ ungraded]</li></ul><div><strong>Module 2: Advanced CNNs in Keras </strong></div><div><br /><strong>Advanced CNNs and Data Augmentation</strong></div><ul><li>Video: Advanced CNNs in Keras </li><li>Video: Data Augmentation Techniques </li><li>Lab: Advanced Data Augmentation with Keras </li><li>Practice Quiz: Advanced CNNs and Data Augmentation</li></ul><div><strong>Transfer Learning on Pre-trained Models and Image Processing</strong></div><ul><li>Video: Transfer Learning in Keras </li><li>Video: Using Pre-trained Models </li><li>Lab: Transfer Learning Implementation </li><li>Video: TensorFlow for Image Processing </li><li>Practice Quiz: Transfer Learning on Pre-trained Models and Image Processing</li></ul><div><strong>Introducing Transpose Convolution</strong></div><ul><li>Video: Introducing Transpose Convolution </li><li>Lab: Practical Application of Transpose Convolution  </li><li>Practice Quiz: Introducing Transpose Convolution </li></ul><div><strong>Advanced CNNs in Keras Summary</strong></div><ul><li>Reading:  Summary and Highlights: Advanced CNNs in Keras</li><li>Reading: Glossary: Advanced CNNs in Keras</li><li>Graded Quiz: Advanced CNNs in Keras </li><li>Discussion Prompt: Data Augmentation and Transfer Learning</li></ul><div><strong>Module 3: Transformers in Keras  <br /></strong><br /></div><div><strong>Transformers in Keras  </strong></div><ul><li>Video: Introduction to Transformers in Keras </li><li>Video: Building Transformers for Sequential Data </li><li>Lab: Building Advanced Transformers   </li><li>Practice Quiz: Transformers in Keras </li></ul><div><strong>Advanced Transformers and Sequential Data using TensorFlow</strong></div><ul><li>Video: Advanced Transformer Applications </li><li>Video: Transformers for Time Series Prediction </li><li>Video: TensorFlow for Sequential Data </li><li>Lab: Implementing Transformers for Text Generation</li><li>Practice Quiz: Advanced Transformers and Sequential Data using TensorFlow </li></ul><div><strong>Transformers in Keras Summary</strong></div><ul><li>Reading: Summary and Highlight: Transformers in Keras  </li><li>Reading: Glossary: Transformers in Keras  </li><li>Graded Quiz: Transformers in Keras  </li><li>Discussion Prompt: Transforming Sequential Data with Transformers</li></ul><div><strong>Module 4: Unsupervised Learning and Generative Models in Keras <br /></strong><br /></div><div><strong>Unsupervised Learning, Autoencoders, and Diffusion Models </strong></div><ul><li>Video: Introduction to Unsupervised Learning in Keras </li><li>Video: Building Autoencoders in Keras </li><li>Lab: Building Autoencoders </li><li>Video: Diffusion Models </li><li>Lab: Implementing Diffusion Models </li><li>Practice Quiz: Unsupervised Learning, Autoencoders, and Diffusion Models </li></ul><div><strong>GANs and TensorFlow</strong></div><ul><li>Video: Generative Adversarial Networks (GANs) </li><li>Video: TensorFlow for Unsupervised Learning </li><li>Lab: Develop GANs using Keras </li><li>Practice Quiz: GANs and TensorFlow </li></ul><div><strong>Unsupervised Learning and Generative Models in Keras Summary</strong></div><ul><li>Reading: Summary and Highlight: Unsupervised Learning and Generative Models in Keras</li><li>Reading: Glossary: Unsupervised Learning and Generative Models in Keras</li><li>Graded Quiz: Unsupervised Learning and Generative Models in Keras </li><li>Discussion Prompt: Exploring Autoencoders and GANs</li></ul><div><strong>Module 5: Advanced Keras Techniques <br /></strong><br /></div><div><strong>Advanced Keras techniques and Custom Training Loops  </strong></div><ul><li>Video: Advanced Keras Techniques </li><li>Video: Custom Training Loops in Keras </li><li>Lab: Custom Training Loops in Keras </li><li>Practice Quiz: Advanced Keras techniques and Custom Training Loops </li></ul><div><strong>Hyperparameter and Model Optimization</strong></div><ul><li>Video: Hyperparameter Tuning with Keras Tuner </li><li>Lab: Hyperparameter Tuning with Keras Tuner </li><li>Video: Model Optimization </li><li>Video: TensorFlow for Model Optimization </li><li>Practice Quiz: Hyperparameter and Model Optimization </li></ul><div><strong>Advanced Keras Techniques Summary</strong></div><ul><li>Reading: Summary and Highlight: Advanced Keras Techniques </li><li>Reading: Glossary: Advanced Keras Techniques </li><li>Graded Quiz: Advanced Keras Techniques and Custom Training Loops</li><li>Discussion Prompt: Custom Training Loops and Hyperparameter Optimization</li></ul><div><strong>Module 6: Introduction to Reinforcement Learning with Keras <br /></strong><br /></div><div><strong>Reinforcement Learning, Q-Learning, Q-Networks (DQNs) </strong></div><ul><li>Video: Introduction to Reinforcement Learning</li><li>Video: Q-Learning with Keras </li><li>Lab: Implementing Q-Learning in Keras </li><li>Video: Deep Q-Networks (DQNs) with Keras </li><li>Lab: Building a Deep Q-Network with Keras</li><li>Practice Quiz: Reinforcement Learning, Q-Learning, Q-Networks (DQNs) </li></ul><div><strong>Module Summary</strong></div><ul><li>Reading: Summary and Highlight: Introduction to Reinforcement Learning with Keras</li><li>Reading: Glossary: Introduction to Reinforcement Learning with Keras</li><li>Graded Quiz: Introduction to Reinforcement Learning with Keras  </li><li>Discussion Prompt: The Promise and Challenge of Reinforcement Learning</li></ul><div><strong>Module 7: Final Project and Assignment</strong></div><ul><li>Reading: Practice Project Overview: Fruit Classification Using Transfer Learning</li><li>Lab:Practice Project: Fruit Classification Using Transfer Learning</li><li>Reading: Final Project: Classify Waste Products Using Transfer Learning</li><li>Final Project:  Classify Waste Products Using Transfer Learning</li><li>Project: Peer-graded Assignment:Classify Waste Products Using Transfer Learning</li></ul><div><strong>Course Wrap Up</strong></div><ul><li>Video: Course Wrap-up </li><li>Reading: Congratulations and Next Steps</li><li>Reading: Thanks from the Course Team</li></ul><div><br /></div><h1>General Information</h1><ul><li>This course is self-paced. </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>The following skills are required to be successful in this course: </div><ul><li>Working knowledge of Python.</li><li>Machine Learning with Python</li><li>Fundamentals of Deep Learning with Keras</li></ul><h1><br /></h1>
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Keras and Tensorflow for Deep Learning

Enhance your deep learning skills with Keras and Tensorflow! Learn advanced CNN techniques, build transformers and GANs, dive into reinforcement learning, and optimize models. Apply your knowledge with hands-on labs and a final project using transfer learning.

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  <h1>About this course</h1><div>This course provides a comprehensive introduction to deep learning, covering key concepts, algorithms, and hands-on implementation using the Keras library. Through engaging lectures, practical labs, and quizzes, you’ll develop a solid foundation in deep learning techniques and gain experience building real-world models.</div><div> </div><div>Begin your deep learning journey with an introduction to the fundamental concepts of artificial intelligence, neural networks, and deep learning. Learn how artificial neurons mimic the human brain and explore the structure of neural networks. Through hands-on labs, you’ll gain practical experience in building artificial neural networks, reinforcing key principles before testing your knowledge with quizzes and assessments.</div><div> </div><div>Dive deeper into the mechanics of deep learning by understanding key optimization techniques such as gradient descent and backpropagation. Discover the challenges of training deep networks, including the vanishing gradient problem, and explore the role of activation functions in improving model performance. Practical labs provide hands-on experience in implementing these techniques, ensuring you grasp their real-world applications.</div><div> </div><div>Explore the power of Keras and other deep learning libraries used in AI development. Learn how to build regression and classification models using Keras, and apply your knowledge in lab exercises focused on implementing these models. This module equips you with essential tools for developing deep learning applications efficiently.</div><div> </div><div>Expand your deep learning expertise by exploring different model architectures. Compare shallow and deep neural networks, and gain insights into specialized models like convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequence data, and transformers for advanced AI applications. Hands-on labs guide you through implementing CNNs and transformers in Keras, reinforcing theoretical concepts with practical applications.</div><div> </div><div>Put your knowledge to the test with a final project that challenges you to build a deep learning model for classification and captioning tasks. Apply the skills you’ve gained throughout the course to develop and evaluate your model, demonstrating your ability to create real-world AI solutions. Upon submission and evaluation, you’ll have a tangible project to showcase your deep learning expertise.</div><div> </div><div>This course is designed for professionals and students looking to enhance their deep learning skills. Whether you’re aiming for a career in AI, data science, or software development, this hands-on learning experience will provide the foundation you need.<br /><br /></div><h1>Learning Objectives: </h1><div>After completing this course, you will be able to: </div><ul><li>Define key concepts such as neural networks, deep learning models, and activation functions.</li><li>Explain the differences between supervised and unsupervised deep learning models.</li><li>Implement deep learning models using Keras to solve real-world problems.</li><li>Compare and contrast different deep learning architectures, including convolutional and recurrent neural networks.</li><li>Assess the performance of deep learning models using appropriate metrics.</li><li>Design, build, train, and test deep learning models using the Keras library.</li></ul><div><br /></div><h1><strong>Course Syllabus:</strong></h1><div>Welcome   </div><ul><li>Video: Course Introduction </li><li>General Information </li><li>Learning Objectives and Course Syllabus </li><li>Grading Scheme </li><li>Helpful Tips for Course Completion </li></ul><div>Introduction to Deep Learning and Neural Networks   </div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Introduction to Deep Learning </li><li>Video: Neurons and Neural Networks  </li><li>Video: Artificial Neural Networks  </li><li>Lab: Artificial Neural Networks </li><li>Practice Quiz: Introduction to Deep Learning and Neural Networks </li><li>Reading: Summary and Highlights: Introduction to Deep Learning and Neural Networks </li><li>Graded Quiz: Introduction to Deep Learning and Neural Networks  </li></ul><div>Basics of Deep Learning   </div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Gradient Descent  </li><li>Video: Backpropagation  </li><li>Lab: Backpropagation </li><li>Video: Vanishing Gradient  </li><li>Video: Activation Functions  </li><li>Lab: Vanishing Gradient and Activation Functions </li><li>Practice Quiz: Basics of Deep Learning </li><li>Reading: Summary and Highlights: Basics of Deep Learning </li><li>Graded Quiz: Basics of Deep Learning  </li></ul><div>Keras and Deep Learning Libraries   </div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Deep Learning Libraries  </li><li>Video: Regression Models with Keras  </li><li>Lab: Regression with Keras </li><li>Video: Classification Models with Keras  </li><li>Lab: Classification with Keras </li><li>Practice Quiz: Keras and Deep Learning Libraries </li><li>Reading: Summary and Highlights: Keras and Deep Learning Libraries </li><li>Graded Quiz: Keras and Deep Learning Libraries </li></ul><div>Deep Learning Models   </div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Shallow Versus Deep Neural Networks  </li><li>Video: Convolutional Neural Networks  </li><li>Lab: Convolutional Neural Networks with Keras </li><li>Video: Recurrent Neural Networks  </li><li>Video: Transformers  </li><li>Lab: Transformers with Keras </li><li>Video: Autoencoders  </li><li>Practice Quiz: Deep Learning Models </li><li>Reading: Summary and Highlights: Deep Learning Models </li><li>Graded Quiz: Deep Learning Models </li></ul><div>Final Project   </div><ul><li>Module Introduction and Learning Objectives </li><li>Final Project: Classification and Captioning</li><li>Final Project Submission and Evaluation</li></ul><div>Course Wrap-Up   </div><ul><li>Video: Course Wrap-Up  </li><li>Reading: Congratulations and Next Steps </li><li>Reading: Team and Acknowledgments </li><li>Copyrights and Trademarks </li></ul><div>Course Rating and Feedback   </div><ul><li>Course Rating and Feedback </li></ul><div>Badge   </div><ul><li>Claim Your Badge Here</li></ul><div><br /></div><h1>General Information:</h1><ul><li>This course is self paced. </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.</li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><ul><li>Working knowledge of Python.</li><li>Machine Learning with Python</li></ul><div><br /><br /></div>
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Fundamentals of Deep Learning using Keras

Master the fundamentals of deep learning with IBM! This course covers neural networks, supervised & unsupervised models, and how to build, train, and test deep learning models using Keras. Gain hands-on skills to kickstart your AI career.

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  <div>Cybersecurity professionals are in high demand, and this trend is expected to continue for the foreseeable future, making now the perfect time to pursue a career in the field. <br /><br />This course offers an overview of the diverse career paths within cybersecurity. It is designed to equip you with the knowledge needed to find and secure the right role for you in the information security (Infosec) industry. The course begins by addressing the growing employment gap in cybersecurity and outlines the various roles and responsibilities at different career levels. <br /><br /></div><div>You will gain an understanding of both technical and soft skills, providing a well-rounded approach to professional growth. Additionally, the course will introduce you to some of the most respected certifications in the field, such as CompTIA A+ and ISC2’s CISSP, among others. <br /><br /></div><div>Throughout the course, you’ll hear from cybersecurity experts who will share valuable insights and personal experiences on how to break into the field. <br /><br /></div><div>This course is ideal for anyone looking to start a rewarding career in cybersecurity, regardless of prior experience or educational background. <br /><br /><br /></div><h1>Learning Outcomes</h1><ul><li>Compare cybersecurity career roles, responsibilities, and progression paths. </li><li>Align IT fundamentals, technical, and soft skills with cybersecurity job levels. </li><li>Identify key certifications (e.g., Security+, A+, CISSP, CEH) and their requirements. </li><li>Find the right cybersecurity role for you and learn how to prepare for it. </li></ul><h1>Course Syllabus</h1><div>Welcome</div><ul><li>Video: Course Introduction (5:47)</li><li>General Information</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li><li>Helpful Tips for Course Completion</li></ul><div>Module 1: Cybersecurity Careers</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Why Cybersecurity (3:15)</li><li>Reading: A Growing Field</li><li>Video: Expert Viewpoints: The Cybersecurity Field (5:54)</li><li>Video: How to get into the cybersecurity field (6:01)</li><li>Video: Entry Level Careers (3:22)</li><li>Reading: Entry-Level Roles, Responsibilities, and Salaries</li><li>Video: Expert Viewpoints: Getting started as a Cybersecurity Professional (5:33)</li><li>Video: Mid-Level Careers (3:41)</li><li>Reading: Mid-Level Roles, Responsibilities, and Salaries</li><li>Video: Expert Viewpoints: A Typical Day in the Life of a Cybersecurity Analyst (5:49)</li><li>Video: Advanced-Level Careers (4:44)</li><li>Reading: Advanced-Level Roles, Responsibilities, and Salaries</li><li>Lab: Cybersecurity Career Paths</li><li>Video: Expert Viewpoints: Cybersecurity Career Path (6:48)</li><li>Reading: Module 1 Summary : Cybersecurity Careers</li><li>Module 1 Practice Quiz : Cybersecurity Careers</li><li>Module 1 Glossary: Cybersecurity Careers</li><li>Module 1 Graded Quiz : Cybersecurity Careers</li><li>Discussion Prompt: Introduce yourself and why you are interested in Cybersecurity</li></ul><div>Module 2: Cybersecurity Skills</div><ul><li>Module Introduction</li><li>Video: Importance of IT Fundamentals Technical Skills (4:59)</li><li>Reading: IT Fundamentals Technical Skills Facts</li><li>Video: Expert Viewpoints: Fundamental IT Skills (4:20)</li><li>Video: Importance of Cybersecurity Technical Skills (4:22)</li><li>Reading: Cybersecurity Technical Skills Facts</li><li>Video: Expert Viewpoints:  Cybersecurity Technical Skills (4:02)</li><li>Video: Importance of Soft Skills (3:49)</li><li>Reading: Soft Skills Facts</li><li>Video: Expert Viewpoints: Soft Skills (5:55)</li><li>Reading: Skills to Job Role Mapping</li><li>Module 2 Summary: Cybersecurity Skills</li><li>Module 2 Practice Quiz: Cybersecurity Skills</li><li>Module 2 Glossary: Cybersecurity Skills</li><li>Module 2 Graded Quiz: Cybersecurity Skills</li></ul><div>Module 3: Cybersecurity Certifications</div><ul><li>Module Introduction</li><li>Video: Benefits of Cybersecurity Certifications (4:33)</li><li>Reading: Cybersecurity Certifications Facts</li><li>Video: Expert Viewpoints: Benefits of Industry Certifications (5:59)</li><li>Video: Overview of Cybersecurity Certifications (4:49)</li><li>Reading: CompTIA Certifications</li><li>Reading: ISC2 Certifications</li><li>Reading: Other Certifications</li><li>Video: Expert Viewpoints: Impact of Certifications (4:40)</li><li>Video: Choosing the Right Certification (2:50)</li><li>Video: Study for the Exam (3:18)</li><li>Video: Schedule and Take the Exam (3:16)</li><li>Video: Expert Viewpoints: Tips for Taking and Passing a Certification Exam (4:31)</li><li>Module 3 Summary: Cybersecurity Certifications</li><li>Module 3 Practice Quiz: Cybersecurity Certifications</li><li>Module 3 Glossary: Cybersecurity Certifications</li><li>Module 3 Graded Quiz: Cybersecurity Certifications</li></ul><div>Module 4: Final Project and Assessment</div><ul><li>Module Introduction and Learning Objectives</li><li>Final Project: Develop your Cybersecurity Career Plan</li><li>[Optional] Project Submission and Evaluation</li><li>Reading: Course Summary</li><li>Glossary (Cumulative)</li><li>Graded Quiz: Final Assessment (15 Questions)</li></ul><div>Course Wrap Up</div><ul><li>Video: Landing your first job in Cybersecurity (5:55)</li><li>Video: Expert Viewpoints: Tips for a Cybersecurity Career (5:02)</li><li>Video: Cybersecurity Career FAQ</li><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li><li>Copyrights and Trademarks</li></ul><div>Course Rating</div><ul><li>Course Rating and Feedback</li></ul><div>Badge</div><ul><li>Claim your Badge</li></ul><h1>General Information</h1><ul><li>This course is self-paced.  </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>No prior experience is required. </div>
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Cybersecurity: Kickstart your Career

Launch your cybersecurity career with this course! Learn about roles, certifications, and key skills to succeed in the growing information security industry.

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  <div><br />Understanding how <strong>machine learning models</strong> make predictions is a critical skill, especially in high-stakes domains like <strong>healthcare</strong>. In this hands-on guided project, you’ll predict <strong>heart attack risks</strong> using <strong>Python</strong>, <strong>scikit-learn</strong>, and <strong>Explainable AI (XAI)</strong> techniques like <strong>Partial Dependence Plots (PDPs)</strong>. By working with a real-world <strong>heart disease dataset</strong>, you’ll not only create predictive models but also learn to interpret their outputs, uncovering how features such as age, cholesterol levels, and blood pressure influence predictions. Beyond technical skills, this project emphasizes using data to make informed decisions, empowering you to communicate findings that can positively impact patient care. In just 60 minutes, you'll gain a practical understanding of PDPs and their role in explaining machine learning models, equipping you to navigate the intersection of AI and healthcare.<br /><br /><strong>What You'll Learn</strong><br />By the end of this project, you will have mastered:</div><ul><li>- <strong>Interpreting machine learning models</strong> using <strong>Partial Dependence Plots (PDPs)</strong> to visualize feature impact.</li><li>- Understanding and analyzing <strong>feature interactions</strong> within models.</li><li>- Applying and comparing <strong>logistic regression</strong> and <strong>random forest models</strong> on healthcare data.</li><li>- Using metrics and visualization tools to evaluate model performance and interpretability effectively.</li></ul><div><br /><br /><strong>What You'll Need</strong><br />To get started with this guided project, you should have:</div><ul><li>- A basic understanding of Python programming.</li><li>- Familiarity with fundamental machine learning concepts.</li><li>- Access to modern web browsers like Chrome, Edge, Firefox, Internet Explorer, or Safari.</li></ul><div><br /><strong>Ready to demystify machine learning models?</strong> Start this guided project now and unlock the ability to interpret complex algorithms, transforming raw data into actionable healthcare insights.</div>
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Med Prediction with Explainable AI: Partial Dependence Plot

 Learn to interpret machine learning models by visualizing feature impacts using Python, scikit-learn, and Explainable AI (XAI) techniques like Partial Dependence Plots (PDPs). These visualizations show relationships between features like age, income, or medical metrics and model predictions, translating complex algorithms into clear insights. Data scientists and stakeholders can use PDPs to understand model behaviour. This interpretability technique supports technical analysis and business decision-making. 

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  <div>In an era where <strong>customer expectations are higher than ever</strong>, businesses must find innovative ways to <strong>deliver fast, reliable, and personalized support</strong>. This course introduces you to <strong>watsonx Assistant</strong>, a cutting-edge AI-powered virtual assistant that leverages <strong>Generative AI and Large Language Models (LLMs)</strong> to enhance customer and employee interactions while <strong>reducing operational costs</strong>.</div><div>Designed for beginners and professionals alike, this course explores <strong>the evolving role of AI assistants</strong> in modern customer service and workforce efficiency. You will learn how watsonx Assistant can improve user experiences, streamline operations, and free up valuable developer time for higher-impact projects.<br /><br /></div><div>By the end of this course, you will:</div><ul><li>Understand the limitations of traditional customer service and how human-led models can sometimes hinder brand perception.</li><li>Explore common pitfalls of existing chat technologies and why many are discarded due to poor performance.</li><li>Learn how watsonx Assistant enhances customer and employee experiences while delivering significant cost savings.</li><li>Discover how Generative AI is reshaping AI assistants and how LLM-powered features improve AI-driven interactions.</li><li>Shift from customer support to customer care, creating more engaging and effective interactions.</li><li>See how AI assistants optimize developer resources, allowing companies to focus on high-value innovation.</li><li>Identify the three key success factors in Generative AI deployment: trust, speed, and cost.</li><li>Explore the extensible architecture of watsonx Assistant, enabling seamless integration across platforms.</li></ul><div><br /></div><div>With a blend of <strong>theory, real-world applications, and hands-on insights</strong>, this course empowers you to <strong>harness the potential of watsonx Assistant</strong> for business success. Whether you're looking to improve customer support, automate workflows, or explore the impact of <strong>AI-powered virtual assistants</strong>, this course provides the foundation you need.</div>
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watsonx Assistant: Virtual Assistants with Generative AI

This introductory course on watsonx Assistant is designed for anyone interested in exploring the latest advancements in Generative AI and how they can enhance customer and employee experiences through the creation of AI assistants.

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  <div>In today’s fast-evolving digital landscape, AI-powered automation is revolutionizing the way businesses operate. This course provides a deep dive into <strong>watsonx Orchestrate</strong> and its role in <strong>Agentic AI</strong>, equipping you with the knowledge and skills to enhance business processes using intelligent automation.</div><div>Throughout the course, you will explore the power of <strong>AI assistants with agents</strong> and learn how to integrate them with business automation skills. You will gain hands-on experience in deploying intelligent assistants, leveraging automated decision-making, and implementing conversational search—all using <strong>watsonx Orchestrate’s low-code tools</strong>.</div><div><br /></div><div>By the end of this course, you will:</div><ul><li>Understand <strong>Agentic AI</strong> and its application in AI-driven business assistants.</li><li>Learn how to integrate <strong>business automation workflows</strong>—such as automated decision-making—into intelligent assistants.</li><li>Deploy a fully functional <strong>AI-powered assistant</strong> on your website with <strong>watsonx Orchestrate</strong>.</li><li>Explore <strong>conversational search</strong> and understand how to implement it for a more intuitive user experience.</li><li>Discover how <strong>LLMs (Large Language Models)</strong> facilitate seamless conversations between users and AI-driven services.</li><li>Utilize <strong>watsonx Orchestrate’s low-code environment</strong> to rapidly build and deploy <strong>Agentic AI</strong> solutions.</li></ul><div><br /></div><div>Whether you're a business professional, developer, or automation enthusiast, this course will equip you with the tools to <strong>streamline operations, enhance customer experiences, and drive efficiency</strong> with AI-powered automation.<br /><br /></div>
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watsonx Orchestrate: Automating Business with Agentic AI

During this course you will discover how to leverage your business automation capabilities in an agentic context. You will learn the power of AI assistants with agents and how to enrich them with business automation skills using watsonx Orchestrate.

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  <div>Imagine you’re working on a <strong>data science project</strong>, and you need AI assistance—whether it’s for <strong>data cleaning, feature engineering, or debugging your code</strong>. You turn to large language models (LLMs) like <strong>OpenAI o3-mini</strong> and <strong>DeepSeek R1</strong>, but how do you know which one is the best fit?<br /><br />AI reasoning models are a hot topic in today’s world, with claims of superior problem-solving abilities and efficiency. But are they really all the same? Do some models think faster? Are others more accurate or cheaper to use? In this hands-on tutorial, we’ll put these models to the test across key stages of the data science workflow to help you decide which AI model can best support your work.<br /><br />With our AI Classroom, you can find out for yourself—quickly, easily, and at no cost. No credit card, no setup, no guesswork. Just log in, test models side by side, and make data-driven decisions in minutes.<br /><br /></div><h1>Project Overview</h1><div>This project is designed to compare the reasoning abilities of OpenAI o3-mini and DeepSeek R1 in practical data science tasks. Instead of learning data science itself, you’ll focus on how well these AI models understand, process, and assist with different steps in a data workflow.<br /><br />You'll evaluate both models based on:<br />✅ Speed – Which model generates responses faster?<br />✅ Cost – Which model is more cost-effective to use?<br />✅ Accuracy – Which model provides better and more correct answers?<br /><br />Using the <strong>AI Classroom</strong>, you’ll conduct side-by-side comparisons of how these models perform in error correction, feature engineering, model selection, and more.<br /><br />By the end, you'll have a data-driven perspective on which model is better suited for AI-assisted data science.<br /><br /></div><h1> What You’ll Learn</h1><div>By completing this project, you will:</div><ul><li>Understand how different reasoning models perform across real-world tasks.</li><li>Learn how AI can assist in data science workflows (but also its limitations).</li><li>Compare cost, accuracy, and efficiency of LLMs in a structured way.</li><li>Gain hands-on experience using the AI Classroom for model benchmarking.</li></ul><div><br /></div><h1>Who Should Do This Project?</h1><div>This project is for anyone curious about AI’s role in data science and wants to compare different reasoning models before integrating them into their workflow. It’s especially useful for:</div><ul><li>Data scientists &amp; ML engineers evaluating LLMs for their work.</li><li>AI enthusiasts &amp; researchers interested in model comparisons.</li><li>Tech professionals &amp; developers looking to incorporate AI into data-driven projects.</li><li>Students &amp; learners wanting to understand how AI reasoning models differ.</li></ul><div><br />No extensive programming experience is needed, as this project focuses on prompt engineering and AI reasoning, rather than coding from scratch.<br /><br /></div><h1>What You Need</h1><div>Before starting, make sure you have:<br /><br />✅ No setup required – Just open the AI Classroom and start testing!<br />✅ Basic knowledge of data science workflows – No need to be an expert, but familiarity helps!<br />✅ Curiosity &amp; willingness to experiment – The best way to learn is by testing and drawing your own conclusions!<br />✅ A current version of web browers: Chrome, Edge, Firefox, Internet Explorer, or Safari for the best experience.<br /><br /><br />By the end of this project, you’ll have a clear understanding of how OpenAI o3-mini and DeepSeek R1 compare, allowing you to choose the best model for your AI-assisted data science tasks.</div>
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Compare DeepSeek-R1 vs OpenAI o3-mini on Data Science tasks

This project compares OpenAI o3-mini versus DeepSeek-R1 across key data science workflow tasks like feature engineering, model selection, and error correction—evaluating cost, speed, and accuracy. Find the best model for your needs in under 30 minutes! No credit card, no hassle—just hop into our AI Classroom and start running your own experiments instantly. Test, compare, and refine insights for free!

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  <h1><strong>How Many R’s Are Actually in Strawberry? AI Tries to Reason It Out</strong></h1><div>You’d think counting the number of <em>R’s</em> in “strawberry” is an easy task, right? Well, ask an AI, and you might get a surprising variety of answers—some correct, some overcomplicated, and some that make you question reality itself.</div><div>Modern AI reasoning models, like <strong>OpenAI’s o3-mini, DeepSeek’s R1, and IBM’s Granite-3.2-8B-Instruct-Preview</strong>, are designed to excel in logical inference, math, and real-time decision-making. But how well do they handle <strong>classic riddles and puzzles</strong>? Do they crack them effortlessly, or do they overthink themselves into oblivion?</div><div>In this guided project, we’ll put AI’s reasoning to the test—throwing tricky puzzles at different models to see which ones <em>get it</em> and which ones take a wild detour. Can AI solve riddles efficiently, or will it get tangled in its own logic like a confused detective overanalyzing a case? Let’s find out!  <br /><br /></div><h1><strong>A Look at the Project Ahead</strong></h1><div>Through this experiment, you’ll:<br />✅ <strong>Compare AI reasoning styles</strong> by testing multiple models on logic puzzles.<br />✅ <strong>Observe overthinking vs. direct solutions</strong>, identifying when excessive reasoning helps or hinders.<br />✅ <strong>Experiment with prompt engineering</strong> to guide AI reasoning effectively.<br />  
      <img src="https://author.skills.network/rails/active_storage/representations/redirect/eyJfcmFpbHMiOnsiZGF0YSI6NDE2NDQsInB1ciI6ImJsb2JfaWQifX0=--63378e6969b8e41b3a3bceea1e44a9e7420006f5/eyJfcmFpbHMiOnsiZGF0YSI6eyJmb3JtYXQiOiJwbmciLCJsb2FkZXIiOnsibiI6LTF9LCJyZXNpemVfdG9fbGltaXQiOlsxMDI0LDc2OF19LCJwdXIiOiJ2YXJpYXRpb24ifX0=--d5cef5cc4423e679c464df97b6ff7a8fa3dec7ab/reasoning-compare.png" />

  
      Comparing o3-mini and DeepSeek-R1-Distill_Llama-70B in Generative AI Classroom
  
</div><div>Some models might <strong>instantly recognize the trick.</strong> Others might <strong>overcomplicate the question</strong>, dissecting each word before arriving at an answer. By analyzing their approaches, you’ll learn how AI handles reasoning and where it still struggles.   <br /><br /></div><h1><strong>What You'll Need</strong></h1><div>Before starting, make sure you have:<br />🔹 <strong>Access to a Browser to run the Generative AI Classroom lab</strong>, where you can easily compare models side by side.<br />🔹 <strong>Basic understanding of logic puzzles</strong> (no programming required!).<br />🔹 <strong>Curiosity to explore AI’s strengths and quirks</strong> in solving problems.  <br /><br /></div><h1><strong>Key Takeaways: What You’ll Learn</strong></h1><div>🔍 <strong>How AI Thinks</strong> – Compare different models’ approaches to logic puzzles.<br />🧠 <strong>Overthinking vs. Clarity</strong> – Learn when detailed reasoning is beneficial and when it leads to unnecessary complexity.<br />📊 <strong>AI Strengths and Weaknesses</strong> – Discover where AI excels and where it struggles with common sense and probabilistic thinking.  <br /><br /></div><div>By the end of this project, you’ll have a hands-on understanding of <strong>how modern AI models reason, where they overcomplicate things, and how to refine their responses with better prompts.  <br /></strong><br /></div><h1><strong>Final Thought</strong></h1><div>AI reasoning isn’t just about <strong>getting the right answer</strong>—it’s about <strong>how</strong> the model arrives at it. While some models, like DeepSeek-R1, excel in step-by-step logic, they sometimes hesitate in drawing conclusions due to biases in their training. Others may simplify too much or get tangled in unnecessary details.</div><div>This project will help you appreciate <strong>the quirks of AI reasoning</strong> and recognize why tracking logical problem-solving is key to advancing AI’s real-world applications. So, let’s dive in, put these models to the test, and see which ones can outthink a brain teaser—and which ones need a reboot! 🚀</div>
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Overthinking AI? Comparing Top LLMs on Reasoning

OpenAI’s o3, DeepSeek-R1, and IBM’s Granite-3.2 are redefining problem-solving with logical thinking. This guided project puts their reasoning to the test with classic riddles, revealing their strengths, weaknesses, and tendencies to overthink. Compare responses from different models—who solves puzzles efficiently, and who gets lost in details? Observe how prompt instructions shape reasoning, from concise answers to step-by-step breakdowns. Gain insights into AI's thinking process and its balance between clarity and complexity.

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