<section class="about">
<h2>ABOUT THIS COURSE</h2>

<p>This course serves as an introduction to Kubernetes operators. Operators are a design pattern for building and deploying an application onto a Kubernetes cluster such that
it can be used in the same manner as core Kubernetes resources. It covers the internal architecture of operators and how they fit into the larger Kubernetes ecosystem, as
well as how to build basic operators using Operator-sdk. Operator-sdk is a command line tool for scaffolding operators, generating the code skeleton and Kubernetes yaml needed
to deploy the operator.</p>
<p>This course is of interest to anyone who wants to learn how to deploy and manage applications on Kubernetes using a more Kubernetes-native style, such as Kubernetes application
devlopers, architects, or operators. It is also of interest to consumers of Kubernetes applications users interested in learning how some of the applications they use work internalally.
It features hands-on labs that have you constructing and deploying real operators on a live Kubernetes cluster.</p>

<hr />
<h2>LEARNING OBJECTIVES</h2>

<p>In this course you will learn about:</p>

<ul>
	<li>What a Kubernetes operator is</li>
<li>Why you would want to use an operator</li>
<li>The basic internal architecture of an operator</li>
<li>How to build a basic operator using<ul>
<li>Golang</li>
<li>Helm</li>
<li>Ansible</li>
</ul>

<hr />
<h2>COURSE SYLLABUS</h2>

<p><strong>Module 1 - Intro to Operators and Building your first Golang operator

</strong></p>

<ul>
	<li>Overview of operators</li>
<li>Overview of basic Kubernetes architecture</li>
<li>Architecture of an operator</li>
<li>Hands-on Lab</li><ul>
<li>Build a Golang operator with Operator-sdk</li></ul>
</ul>

<p><strong>Module 2 - Building your first Helm operator</strong></p>

<ul>
	<li>Overview of Helm operators</li>
<li>Hands-on Lab</li><ul>
<li>Build a Helm operator with Operator-sdk</li></ul>
</ul>

<p><strong>Module 3 - Building your first Ansible operator</strong></p>

<ul>
	<li>Overview of Ansible operators</li>
<li>Hands-on Lab</li><ul>
<li>Build an Ansible operator with Operator-sdk</li></ul>
</ul>

<hr />
<h2>REQUIREMENTS</h2>

<p>Basic familiarity with Kubernetes (i.e. creating/updating/deleting Kubernetes resources such as Pods and Services)</p>
 <p> The labs in this course cover the creation, deployment, and use of operators on a Kubernetes cluster. It assumes you are already familiar with the basics of Kubernetes, such as using kubectl to create resources on a Kubernetes cluster. </p>
  
  <p>The requirements for the hands-on labs are as follows:</p>
<ul>
<li>Requirements for all hands-on labs</li>
<li>Operator-sdk v1.5.0+ installed</li>
<li>Kubectl v1.17.0+ installed</li>
<li>Admin access to a Kubernetes cluster. The next section covers how to provision a small cluster for free on IBM Cloud if you do not have a one already.</li>
<li>Docker v3.2.2+ installed</li>
<li>Access to a Docker image repository such as Docker Hub or quay.io</li>
</ul>
<hr />
<h2>COURSE INSTRUCTORS</h2>

<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Jonathan%20Berkhahn%20pic.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<p><a href="https://www.linkedin.com/in/jonathan-berkhahn-285779103"><strong>Jonathan Berkhahn </strong></a> is an open source software contributor for IBM. He’s worked on various OSS cloud computing projects such as Cloud Foundry and Kubernetes and currently contributes to Operator-sdk and related projects.</p>
  
  <p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Martin%20Hickey.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>
  
  <p><strong>Martin Hickey </strong> works on the Open Technology team at IBM focusing on open source software. He is a regular contributor to open source and a core maintainer for Helm. He has also contributed previously to the OpenStack and Elastic communities. Martin enjoys speaking at conferences and meet-ups. He has many years’ experience in the creation of enterprise software for different industries, from Telcos to Cloud.</p>

<p> </p>


<hr />
<h2>FREQUENTLY ASKED QUESTIONS</h2>

<article>
<h3>What web browser should I use?</h3>

<p>The Open edX platform works best with current versions of Chrome, Firefox, or Safari, or with Internet Explorer version 9 and later.</p>

<p>See our <a href="http://edx.readthedocs.org/projects/open-edx-learner-guide/en/latest/front_matter/browsers.html">list of supported browsers</a> for the most current information.</p>
</article>
</li></ul></section>

Kubernetes Operators Intermediate

This course serves as an introduction to Kubernetes operators. Operators are a design pattern for building and deploying an application onto a Kubernetes cluster such that it can be used in the same manner as core Kubernetes resources.

<section class="about">
<h2>ABOUT THIS CLOUD COURSE</h2>

<p>This course introduces you to the core concepts of cloud computing. You will gain the foundational knowledge required for understanding cloud computing from both business and practitioner perspectives.</p>

<p>You will learn about the definition and essential characteristics of cloud computing, its history, emerging trends, and the business case for cloud computing. You also learn about the various cloud service models (IaaS, PaaS, SaaS) and deployment models (Public Cloud, Private Cloud, Hybrid Cloud) and the key components of a cloud architecture (Virtualization, VMs, Storage, Networking, Containers, Serverless).</p>

<hr />
<h2>LEARNING OBJECTIVES</h2>

<p>After completing this course, you will be able to:</p>

<ul>
	<li>Define cloud computing and explain its essential characteristics, history, &amp; emerging trends</li>
	<li>Articulate the business case for cloud computing</li>
	<li>Describe the service &amp; deployment models of cloud computing</li>
	<li>Explain the various components of a cloud architecture</li>
</ul>

<hr />
<h2>COURSE SYLLABUS</h2>

<h3><strong>Module 1: Overview of Cloud Computing</strong></h3>

<ul>
	<li>Definition and essential characteristics</li>
	<li>A brief history and evolution of Cloud</li>
	<li>Key cloud service providers and their services</li>
</ul>

<h3><strong>Module 2: Cloud Adoption and Emerging Technologies</strong></h3>

<ul>
	<li>Business case for Cloud Computing</li>
	<li>Emerging technologies supported by Cloud: AI, IoT, Blockchain, Analytics</li>
</ul>

<h3><strong>Module 3: Cloud Computing Service and Deployment Models</strong></h3>

<ul>
	<li>Service Models: IaaS, PaaS, SaaS</li>
	<li>Deployment Models: Public, Private, Hybrid</li>
</ul>

<h3><strong>Module 4: Components of Cloud Computing</strong></h3>

<ul>
	<li>Cloud Infrastructure Overview</li>
	<li>Virtualization, VMs, Bare Metal</li>
	<li>Secure Cloud Networks</li>
	<li>Containers</li>
</ul>

<h3><strong>Module 5: Cloud Storage</strong></h3>

<ul>
	<li>Direct Attached</li>
	<li>File Storage</li>
	<li>Block Storage</li>
	<li>Object Storage</li>
	<li>Content Delivery Networks (CDN)</li>
</ul>

<h3><strong>Module 6: Cloud Native and Emergent Cloud Trends</strong></h3>

<ul>
	<li>Hybrid Multicloud</li>
	<li>Serverless</li>
	<li>Microservices</li>
	<li>Cloud Native</li>
	<li>Application Modernization</li>
</ul>

<hr />
<h2>GENERAL INFORMATION</h2>

<ul>
	
	<li>This course is self-paced.</li>
	<li>It can be taken at any time.</li>
</ul>

<hr />
<h2>RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE</h2>

<ul>
	<li>Basic Computer Literacy</li>
</ul>

<hr />
<section>
<h2>COURSE STAFF</h2>
</section>

<section>
<article>
<p><img alt="Rav Ahuja" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Rav.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<h3>Rav Ahuja</h3>

<p>Rav Ahuja is a Program Director for IBM Skills Network and leads the work on strategy, programs, and partnerships. He works at IBM Canada Lab in Toronto and is also a co-founder of CognitiveClass.ai, an initiative offering free courses for democratizing in-demand technology skills. He presents at events worldwide and has authored numerous papers, articles, books and courses on subjects in AI, Data Science, Data Engineering, and Cloud Computing. Rav holds B. Eng. from McGill University and MBA from Richard Ivey School of Business, University of Western Ontario.</p>

<p> </p>

<h3>Other Contributors</h3>

<p>The following individuals also contributed content for this course:</p>

<ul>
	<li>James Belton</li>
	<li>Ahmad Zamer</li>
	<li>Mary Ann Johnson</li>
</ul>
</article></section></section>

Introduction to Cloud

This course introduces you to the core concepts of cloud computing. You will gain the foundational knowledge required for understanding cloud computing from both business and practitioner perspectives.

<div class="trix-content">
  <div><br /></div><h1>About</h1><div>In this guided project, you will use front-end Javascript frameworks React.js and Chart.js to create a dynamic single-page application that visualizes data from COVID-19 Canada Open Data Working Group's API as a dashboard. This guided project serves as an introduction to utilizing and integrating front-end frameworks with APIs to build simple but powerful applications.</div><div><br /></div><h1>A Look at the Project Ahead</h1><div>After completing this project, you will be able to</div><ul>
<li>Develop a React application from scratch</li>
<li>Apply and interpret JSX syntax</li>
<li>Describe and create a React Component</li>
<li>Utilize React hooks to store data and work with renders/re-renders</li>
<li>Use third party libraries to streamline development</li>
</ul><div><br /></div><h1>
<strong>What You’ll Need  </strong> </h1><div>Firstly, you just need a web browser!  Regarding prior skills,  you will also need basic JavaScript programming knowledge to write the application and some basic HTML/CSS knowledge to create the frontend.<br /><br />Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to the Cloud IDE and Python runtimes that we offer as part of the IBM Skills Network Labs environment. <br /><br />This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.<br /><br />
</div><h1>Your Instructors</h1><div>Arjun Swani<br />Richard Ye, IBM</div>
</div>


Build a COVID dashboard with React 

Visualize COVID data as a dynamic single-page dashboard by utilizing front-end Javascript frameworks React.js, Chart.js, and COVID-19 Canada Open Data Working Group's API.

<div class="trix-content">
  <h1>Learn by Doing</h1><div>A guided project is a hands-on tutorial designed to help you learn a particular technology by doing a real project. It includes step-by-step instructions with explanations, examples and exercises that can be followed and practiced in a lab environment.  Hands-on skills are highly sought out by employers when determining job readiness.   Guided projects are </div><div>interactive, on-demand and will equip you with practical abilities that can be applied on the job!</div><div><br /></div><h1>A Look at the Project Ahead</h1><div>Have you ever wondered what techniques are used to make photo filters like the ones present in applications such as Adobe Photoshop? Well the answer is computer vision and image processing techniques! Or wondered how your photographs would look like as art work? <br /><br />In this project you will learn how to use built-in OpenCV functions to transform your photographs into artistic paintings and sketches.  OpenCV  is a powerful and popular open source computer vision and image processing library that contains an extensive set of algorithms. We will be utilizing this library to transform your photographs into artwork!</div><div><br /></div><div>Express your creativity with technology using OpenCV in python, all while learning about some common and basic image processing and computer vision techniques!</div><div><br /></div><div>More specifically after this lab you will be able to:</div><ul>
<li>Convert colored images to grayscale</li>
<li>Apply the Gaussian filter to smooth an image</li>
<li>Detect the edges of an image using the Gradient Magnitude</li>
<li>Convert a grayscale image to a binary image</li>
<li>Create a pencil sketch of an image in OpenCV</li>
<li>Create a water painting of an image in OpenCV</li>
</ul><h1>What You’ll Need </h1><div>To do this project, you will need the following:</div><ul><li>A very basic knowledge of Python</li></ul><h1>Your Instructor</h1><div>Yasmine Hemmati, IBM</div>
</div>


Transform Photos to Sketches and Paintings with OpenCV

Have you ever wanted to transform your photographs into an artistic sketch or painting to showcase your creativity?  In this Guided Project you'll do just that! Transform your photographs to paintings, sketches and more using OpenCV in python.

<div class="trix-content">
  <h1>Why do this Guided Project</h1><div>This guided project will give you a high level introduction into how exactly logistic regression works using a simple example. We will also introduce simple NLP concepts in this guided project, giving you exposure and an opportunity to learn about how you can represent a document as a vector of terms (words). Using these tools, you'll be equipped to tackle many other machine learning projects and dive deeper into the NLP field and supervised classification.<br /><br />
</div><h1>A Look at the Project Ahead</h1><div>Tell your audience what they can expect to learn. Better yet, tell them what they will be able to do as a result of completing your project:</div><ul>
<li>Explain how logistic regression works</li>
<li>Create and utilize document-term/term-frequency matrices in NLP tasks</li>
<li>Perform various optimization techniques to improve model performance in any application</li>
</ul><div><br /></div><h1>What You'll Need</h1><div>Your curiosity! This lab will provide you with all the intuitive background you need in order to understand and learn the concepts we use. In terms of coding, just a simple understanding of Python and the Numpy/Pandas libraries is enough. If you aren't familiar with these libraries, Google is your best friend. Everything here is run through your browser in a Jupyter Notebook Environment, so no need to install anything!<br /><br />
</div><h1>Your Instructor</h1><div>Richard Ye is the author of this guided project. He studied Finance and Statistics at the University of Toronto, and has research experience in financial applications of NLP, and the educational impact of COVID and online learning.</div>
</div>


Classify recipe text to cuisine using NLP and Logistic Regression

Have you ever wondered why certain foods taste the way they do? Well, in this project, we will use NLP (Natural Language Processing) to determine the country of origin of recipes using the ingredients.

This project will introduce you to NLP and the logistic regression algorithm. NLP is a fantastic field with many applications, but we'll focus on a straightforward beginner project in this guided project. Here we will create a document term matrix (aka term-frequency matrix) using our recipes ingredients and plugging it into a logistic regression model to predict the county of origin.

<div class="trix-content">
  <div>Python has been around a while. Application of any language depends on its use case. The project is designed to help learners understand the implementation of language constructs of Python. It also enables them to think logically.<br /> <br /><strong>About the game:</strong><br />- Rock-Paper-Scissor is a hand game having its origin from China.<br />-  It is a game played between two players.<br />- Upon his turn, each player chooses one of the option between Rock, Paper and Scissor.<br />- This game is also used for choosing between two people similar to flipping coin or throwing dice.<br /><br /><strong>Rules:</strong> <br />The rules of the game are pretty simple. The outcome of the game is determined by only four simple rules...<br />1. Rock Vs Scissor - Rock Wins<br />2. Scissor Vs Paper - Scissor Wins<br />3. Paper Vs Rock - Paper Wins<br />4. If both the players select the same thing, it will be considered as a tie!<br /><br /><strong>Prerequisite:</strong> <br />Basic understanding of Python programming language constructs<br /><br /><strong>Your Instructor:</strong><br /><br />Dr. Chandrika M<br />Assistant Professor,<br />Department of Computer Applications,<br />Dayananda Sagar College of Engineering, Bangalore, India.<br /><br />As a computer science faculty who is interested in technology, it gives me immense pleasure to share my knowledge with others who are interested to learn. Computer was a fascinating magical box for me all my childhood. I always had curiosity about its working. As I grew, I became interested in web technologies and started learning them. The learning I got in my journey till date is "Technology is ever changing  - if you have passion towards it today, don't waste time just go ahead and start learning. You never know if that technology exist tomorrow".<br /><br />
</div>
</div>


Python Mini-Game Project

The project is a simulation of a popular game Rock-Paper-Scissor. This will help learners understand the usage of basic constructs of Python.

<div class="trix-content">
  <h1>About</h1><div>Javascript is a versatile programming language that found its roots in web development. However, using some interesting runtimes like NodeJS, JavaScript can be used for all kinds of applications. Today, JavaScript can be used both for dynamic web pages and non-web contexts. This could include anything from web apps to server applications to games.<br /><br />
</div><div>Being one of the core technologies of the world wide web, it is widely used for client-side web page behaviour. With several frameworks built on top of it and with continuous improvements, JavaScript continues to be one of the most widely adopted modern languages. By learning JavaScript not only do you gain access to several powerful libraries and frameworks but you also gain a deeper understanding of how the modern web works.<br /><br />
</div><div>In this hands-on project you will be recreating the game of Rock, Paper, Scissors using JavaScript in IBM's cloud based environment. Through the project, you will use JavaScript to manipulate the web page to simulate game play. In addition, You will employ functions, objects and event handlers to handle all the game logic behind the scenes and deliver a seamless experience.<br /><br />
</div><h1>Requirements</h1><div>For this lab, you will need to know basic HTML and CSS syntax. This lab assumes familiarity with fundamental programming concepts and only illustrates features/syntax unique to Javascript.<br /><br />
</div><h1>Authors</h1><ul><li>Arjun Swani</li></ul>
</div>


Fundamentals of Javascript through rock-paper-scissors

In this hands-on project you will be recreating the game of Rock, Paper, Scissors using the versatile and popular programming language, JavaScript.

<div class="trix-content">
  <h1><strong>A Look at the Project Ahead:</strong></h1><ol>
<li>Run a simple Node.js app on your localhost. </li>
<li>Learn what is NPM.</li>
<li>Add some styling to your app using HTML and CSS.</li>
</ol><h1><strong>Prerequisites:</strong></h1><ul><li>Any kind of experience with JavaScript language. </li></ul><h1><strong>What you will need:</strong></h1><ul>
<li>You will just need a computer to take do this Project on. Everything else is provided to you via the IBM Skill<br />s Network Labs environment.</li>
<li>Your willingness to learn. (Most important thing)</li>
</ul><h1>Your Instructor</h1><div>Jason Song, IBM</div>
</div>


Getting Started with Node.js

Have you heard of Node.js before? Node.js is one of the most popular technology to create server-side apps. Why? Because if you know JavaScript, you build both the frontend and the backend using JavaScript. Take this Guided Project to get started on Node.js.

<div class="trix-content">
  <h1>About</h1><div>Could you imagine using YouTube without the ability to search? Or having to spell the name of a person perfectly to find them on Facebook?<br /><br />These websites would be far less useful to us.</div><div>
<br />Great search is an essential feature that all of the best applications share. In fact, it is so common that people have come to expect it on every site and application they use. However, unlike YouTube and Facebook, most of us don't have the time or resources to build a fully-functional search engine from scratch. This doesn't mean our websites and applications can't have great search though!<br /><br />With the help of Algolia, we can implement efficient, flexible, and insightful search in our own applications while keeping time and monetary costs to a minimum.<br /><br />In this project, you will learn how to build a smart search form from scratch using Algolia. We'll store our searchable data in an index and build a simple NodeJS application that queries it through the Algolia API.<br /><br />More specifically, you will be able to:</div><ol>
<li>Setup an index in Algolia using the Algolia UI</li>
<li>Perform basic queries on an Algolia index using the Algolia Javascript Client</li>
<li>Create a form that displays search results in real-time using HTML and Javascript</li>
</ol><div>
<br />The dataset for this project contains a list of people categorized as "Billionaires of 2021" by Forbes in their 35th Annual World's Billionaires List. It has 7 fields: Name, Net worth, Country, Source, Rank, Age, and Industry.<br /><br />
</div><h1>Requirements</h1><div>To get the most out of this project, you should know:</div><ul>
<li>Basic HTML and CSS syntax</li>
<li>Basic Javascript syntax</li>
</ul><div>You will also need an Algolia account but don't worry, we'll create one together inside the project (it's free).<br /><br />
</div><h1>Author</h1><ul><li>Ben Honda</li></ul>
</div>


Build a Smart Search Form with Algolia

Great search is an essential feature that all of the best applications share. In this project, we'll leverage the power of Algolia to build our own smart search form from scratch.

<div class="trix-content">
  <div>
<strong>About</strong><br /> Sequential modelling is the process of forecasting a sequence of values from a set of input values. Input values can contain elements that are ordered into sequences like time-series, text streams, or DNA sequences. Lot of tasks can be modelled from these types of data. For example:</div><ul>
<li>text classification, e.g. spam email or not </li>
<li>language translation, e.g. French to English</li>
<li>time-series forecasting, e.g. stock prices prediction</li>
</ul><div>In this guided project, we will look into various common sequential data sets, and understand pre-processing techniques associated with each. Since we will be using simple methods, no prior knowledge in machine learning will be required to complete this guided project. </div><div> <br /> <strong>A Look at the Project Ahead</strong><br /> After completing this Guided Project, you will be able to:</div><ul>
<li>Describe various forms of sequential data, and common tasks that can be modelled using sequential data</li>
<li>Decompose a time-series and perform time-series imputation</li>
<li>Pre-process and vectorize a text stream and genetic dataset </li>
<li>Pre-process and visualize an audio dataset, and create spectrograms</li>
</ul><div>
<strong>What You'll Need</strong><br /> This course mainly uses Python and JupyterLabs. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.</div><div>
<br /> <strong>Frequently Asked Questions</strong>
</div><div>&gt; <em>Do I need to install any software to participate in this project?</em>
</div><div>Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.</div><div>&gt; <em>What web browser should I use?</em>
</div><div>The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.<br /><br /><strong>Your Instructor</strong><br /> <strong>Kopal Garg</strong><br /> I am a Data Scientist Intern at IBM, and a Masters student in computer science at the University of Toronto. I am passionate about building AI-based solutions that improve various aspects of human life. </div><div> </div>
</div>


Machine Learning for Sequential Data

In this project, we will analyze various sequential data types like text streams, audio clips, time-series data, and genetic data, and understand pre-processing techniques associated with each. 

<div class="trix-content">
  <h1><strong>About This Guided Project</strong></h1><div>In this project, you will learn the concepts of a relational model including the terms entity, attribute, relation, degree, and cardinality. Then apply the concepts you have learned to a real-world example of a database.<br /><br /><br />
</div><h1>Objectives</h1><div>After completing this project, you will be able to evaluate your knowledge of relational model concepts.</div><div><br /></div><h1>Requirements</h1><div>To complete this project you will not need access to any type of software.<br /><br /><br />
</div><h1>Project Staff</h1><div>
    <img src="https://author-workbench-prod.s3.us.cloud-object-storage.appdomain.cloud/kbaegs87bmgtzdxbao97rozmslz9" />





</div><div><strong>Sandip Saha Joy</strong></div><div>Cognitive Data Scientist<br />IBM<br /><br /><br />
</div><h1>Frequently Asked Questions</h1><div>
<strong>Do I need to install any software to participate in this project?<br /></strong><br />
</div><div>No.</div><div><br /></div><div><br /></div><div><br /></div><div><br /></div>
</div>


Relational Model Concepts

In this project, you will learn the concepts of a relational model including the terms entity, attribute, relation, degree, and cardinality. Then apply the concepts you have learned to a real-world example of a database.

<div class="trix-content">
  <div>Legit-Info offers a simple Content Management System, or CMS for short, that delivers legislation content to users. Legislation is automatically fetched from various sources as an asynchronous background task so that the website is not slowed down during search activities. IBM Watson Natural Language Understanding is used to minimize the amount of curation effort needed to provide meaningful title, summary, location and impact attributes.<br /><br />
</div><div>Technologies used in the lab:</div><ul>
<li>
<a href="https://www.python.org/">Python</a> - The programming language, along with pip and pipenv supporting tools</li>
<li>
<a href="https://www.djangoproject.com/">Django</a> - A framework for web applications written in Python</li>
<li>
<a href="https://getbootstrap.com/">Bootstrap</a> - A popular User Interface toolkit, providing "responsive UI" to handle different screen sizes across smartphones, tablets and desktops.</li>
<li>
<a href="https://pypi.org/project/pdfminer.six/">PDFminer</a> - Extracts text from PDF</li>
<li>
<a href="https://www.sqlite.org/index.html">SQlite</a> - A simple, local database</li>
<li>
<a href="https://www.postgresql.org/">Postgresql</a> - A robust, relational database</li>
<li>
<a href="https://gunicorn.org/">Gunicorn</a> - A WSGI HTTP webserver for Python</li>
<li>
<a href="https://github.com/nltk/nltk">NLTK</a> - Natural Language Toolkit</li>
<li><a href="https://www.ibm.com/watson/nlu">Watson Studio Natural Language Understanding</a></li>
</ul><div>
<br /><br /><br /><br /><br /><br /><br /><br /><br />
</div>
</div>


Legit Info - Call for Code for Racial Justice Open Source Project

Legit Info is a web-based application written in Python programming language, using the Django framework and Bootstrap user interface styling. Its primary goal is to help advocates find legislation of interest based on an advocate's preferences for impact areas and geographical location.

<div class="ace-line" id="magicdomid6">
<h2>About This Course</h2>
<span class="author-284586046 font-color-373a3c font-size-medium">This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, l</span>ook into how Statistical Modeling relates to Machine Learning, and do a comparison of each.</div>

<div class="ace-line" id="magicdomid8"><span class="author-284586046 font-color-373a3c font-size-medium">Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!</span></div>

<div class="ace-line" id="magicdomid9"><strong><span class="author-284586046 font-color-373a3c font-size-medium">Explore many algorithms and models:</span></strong></div>

<div class="ace-line" id="magicdomid10">
<ul>
	<li><span class="author-284586046 font-color-373a3c font-size-medium">Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.</span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid11">
<ul>
	<li><span class="author-284586046 font-color-373a3c font-size-medium">Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.</span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid12"><span class="author-284586046 font-color-373a3c font-size-medium">Get ready to do more learning than your machine!</span>

<hr />
<h2>Course Syllabus</h2>

<div class="ace-line" id="magicdomid23"><span class="author-284586046 b"><b>Module 1 -</b></span><span class="author-284586046 b font-color-000000 font-size-medium"><b> Supervised vs Unsupervised Learning</b></span></div>

<div class="ace-line" id="magicdomid25">
<ul>
	<li><span class="author-284586046">Machine Learning vs Statistical Modelling</span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid26">
<ul>
	<li><span class="author-284586046 font-color-373a3c font-size-medium">Supervised vs Unsupervised Learning </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid27">
<ul>
	<li><span class="author-284586046 font-color-373a3c font-size-medium">Supervised Learning Classification </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid28">
<ul>
	<li><span class="author-284586046 font-color-000000 font-size-medium">Unsupervised Learning </span></li>
</ul>
<span class="author-284586046 b font-color-373a3c font-size-medium"><b>Module 2 - Supervised Learning I</b></span></div>

<div class="ace-line list-start-number1" id="magicdomid34">
<ul>
	<li><span class="author-284586046 font-color-373a3c">K-Nearest Neighbors </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid35">
<ul>
	<li><span class="author-284586046 font-color-373a3c">Decision Trees </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid36">
<ul>
	<li><span class="author-284586046 font-color-373a3c font-size-medium">Random Forests</span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid37">
<ul>
	<li><span class="author-284586046 font-color-373a3c font-size-medium">Reliability of Random Forests </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid38">
<ul>
	<li><span class="author-284586046 font-color-373a3c font-size-medium">Advantages &amp; Disadvantages of Decision Trees </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid39"> </div>

<div class="ace-line"> <span class="author-284586046 b font-color-373a3c font-size-medium"><b>Module 3 - Supervised Learning II</b></span></div>

<div class="ace-line list-start-number1" id="magicdomid44">
<ul>
	<li><span class="author-284586046 font-color-373a3c font-size-medium">Regression Algorithms </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid45">
<ul>
	<li><span class="author-284586046 font-color-373a3c font-size-medium">Model Evaluation </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid46">
<ul>
	<li><span class="author-284586046 font-color-373a3c font-size-medium">Model Evaluation: Overfitting &amp; Underfitting</span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid47">
<ul>
	<li><span class="author-284586046 font-color-373a3c font-size-medium">Understanding Different Evaluation Models </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid48"> <span class="author-284586046 b font-color-373a3c font-size-medium"><b>Module 4 - Unsupervised Learning</b></span></div>

<div class="ace-line list-start-number1" id="magicdomid53">
<ul class="list-number1">
	<li><span class="author-284586046">K-Means Clustering</span><span class="author-284586046 font-color-000000 font-size-medium"> plus Advantages &amp; Disadvantages </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid54">
<ul class="list-number1">
	<li><span class="author-284586046 font-color-000000 font-size-medium">Hierarchical Clustering plus Advantages &amp; Disadvantages </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid55">
<ul class="list-number1">
	<li><span class="author-284586046">Measuring the Distances Between Clusters</span><span class="author-284586046 font-color-000000 font-size-medium"> - Single Linkage Clustering </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid56">
<ul class="list-number1">
	<li><span class="author-284586046">Measuring the Distances Between Clusters</span><span class="author-284586046 font-color-000000 font-size-medium"> - Algorithms for Hierarchy Clustering</span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid57">
<ul>
	<li><span class="author-284586046 font-color-000000 font-size-medium">Density-Based Clustering </span></li>
</ul>
<b>Module 5 - Dimensionality Reduction &amp; Collaborative Filtering</b></div>

<div class="ace-line list-start-number1" id="magicdomid63">
<ul class="list-number1">
	<li><span class="author-284586046 font-color-000000 font-size-medium">Dimensionality Reduction: Feature Extraction &amp; Selection </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid64">
<ul class="list-number1">
	<li><span class="author-284586046 font-color-000000 font-size-medium">Collaborative Filtering &amp; Its Challenges </span></li>
</ul>
</div>

<div class="ace-line" id="magicdomid69">
<hr />
<h2>Prerequisites</h2>

<ul>
	<li>Python for data science</li>
</ul>

<hr />
<h2>Recommended skills prior to taking this course</h2>

<ul>
	<li>You have to do hands-on lab for this course. The tool that you use for hands-on is called Jupyter and it is one of the most popular tools used by data scientists. If you are not familiar with Jupyter, I would recommend that you take our free <span class="s1">Data Science Hands-on with Open Source Tools.</span></li>

	<li>This hands-on lab requires that you have working knowledge of Python programming language as it applies to data analytics. If you don't feel you have sufficient skill in Data Analysis with Python, I recommend you take Data Analysis with Python courses.</li>
</ul>
</div>
</div>

<section class="course-staff">
<hr />
<h2>Course Staff</h2>

<div class="teacher-image"><img alt="Dr. Saeed Aghabozorgi, TensorFlow Course Instructor" class="alignnone wp-image-2254" height="133" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Saeed%20pic.png" title="Dr. Saeed Aghabozorgi, TensorFlow Course Instructor" width="133" /></div>

<p><a href="https://ca.linkedin.com/in/saeedaghabozorgi" rel="noopener" target="_blank">Saeed Aghabozorgi</a>, PhD is a Sr. Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.  </p>

<p> </p>

<article class="teacher">
<div class="teacher-image"><img alt="" class="alignnone wp-image-3934" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Kevin%20Wong%20.png" style="width: 133px; height: 133px; border-radius: 10px" /></div>

<h3>Kevin Wong</h3>

<p>Kevin Wong is a Technical Curriculum Developer. He enjoys developing courses that focuses on the education in the Big Data field. Kevin updates courses to be compatible with the newest software releases, recreates courses on the new cloud environment, and develops new courses such as Introduction to Machine Learning.Kevin is from the University of Alberta, where he has completed his third year of Computer Engineering Co-op.</p>

<p> </p>
</article>

<article class="teacher">
<div class="teacher-image"><img alt="" class="alignnone wp-image-3932" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Daniel%20Tran.jpg" style="width: 133px; height: 133px; border-radius: 10px" /></div>

<h3>Daniel Tran</h3>
Daniel Tran is an IBM Technical Curriculum Developer in Toronto, Ontario. He develops courses to improve the education of customers who seek knowledge in the Big Data field. He has also reworked previously developed courses, updating them to be compatible with the newest software releases, as well as work at the forefront of recreating courses on a newly developed cloud environment. Daniel is from the University of Alberta, where he has completed his third year of traditional Computer Engineering Co-op.</article>
</section>

Machine Learning with Python

Are the phrases ‘it is certain’, ‘yes you may rely on it’, ‘reply hazy try again’ common in your predictions? Make room on your shelf for your magic eight ball and take this opportunity to see how Machine Learning can be a beneficial tool for predicting future trends. All with a bit more than oil and blue die. All signs point to yes as an indicator you will benefit from this experience.

<section class="about"> <h2>About This Course</h2> <p> Get answers to fundamental questions such as: <strong>What is Big Data?</strong> How do we tackle Big Data? Why are we interested in it? How does Big Data add value to businesses? </p> </section> <section class="about"> <ul> <li>Gain insights on how to run better businesses and provide better services to customers</li> <li>Get recommendations on how to process big data on platforms that can handle the volume, velocity, variety and veracity of Big Data</li> <li>Learn why Hadoop is a great Big Data solution and why it's not the only Big Data solution</li> </ul> </section> <h2>Course Syllabus</h2> <ul> <li><strong>Module 1 - What is Big Data?</strong> <ol> <li>Characteristics of Big Data</li> <li>What are the V’s of Big Data?</li> <li>The Impact of Big Data</li> </ol> </li> </ul> <ul> <li><strong>Module 2 - Big Data - Beyond the Hype</strong> <ol> <li>Big Data Examples</li> <li>Sources of Big Data</li> <li>Big Data Adoption</li> </ol> </li> </ul> <ul> <li><strong>Module 3 - The Big Data and Data Science</strong> <ol> <li>The Big Data Platform</li> <li>Big Data and Data Science</li> <li>Skills for Data Scientists</li> <li>The Data Science Process</li> </ol> </li> </ul> <ul> <li><strong>Module 4 - BDUse Cases</strong> <ol> <li>Big Data Exploration</li> <li>The Enhanced 360 View of a Customer</li> <li>Security and Intelligence</li> <li>Operations Analysis</li> </ol> </li> </ul> <ul> <li><strong>Module 5 - Processing Big Data</strong> <ol> <li>Ecosystems of Big Data</li> <li>The Hadoop Framework</li> </ol> </li> </ul> <h2>General Information</h2> <ul>  <li>This course is self-paced.</li> <li>It can be taken at any time.</li> <li>It can be audited as many times as you wish.</li> </ul> <h2>Recommended skills prior to taking this course</h2> <ul> <li>None</li> </ul> <h2>Requirements</h2> <ul> <li>None</li> </ul> <h2>Course Staff</h2> <section class="course-staff"> <article class="teacher"> <div class="teacher-image"> <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Shingai.png" alt="Shingai Manjengwa, author of What is Big Data?" /> </div> <h3>Fireside Analytics Inc.</h3> <p> Shingai Manjengwa <a href="https://twitter.com/Tjido" target="_blank">(@Tjido)</a> is the Director of Insights and Analytics at <a href="http://www.firesideanalytics.com/" target="_blank">Fireside Analytics Inc</a>. An NYU Stern alum, she graduated from the Stern Business Analytics Masters program in 2014 and founded Fireside Analytics the following year. Fireside Analytics is a data analytics consulting company that makes data analytics and data science skills accessible to private sector companies, non-profits and education institutions. Fireside Analytics works with clients to build their data science capabilities and train their staff and stakeholders using customized case studies. Connect with us on <a href="https://www.facebook.com/firesideanalytics/" target="_blank">Facebook</a>, <a href="https://twitter.com/fireside_info" target="_blank">Twitter</a> and <a href="https://www.linkedin.com/company/fireside-analytics-inc-" target="_blank">LinkedIn</a>. </p> </article> <p> This course has been adapted from the original version created by Glen R.J. Mules (below), a Senior Instructor and Principal Consultant with IBM Information Management World-Wide Education. </p> <article class="teacher"> <div class="teacher-image"><img class="alignnone wp-image-3844 size-full" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Glen%20Mules.png" alt="Glen R.J. Mules" /></div> <h3>Glen R.J. Mules</h3> <p> <a href="https://www.linkedin.com/in/glenmules" target="_blank">Glen R.J. Mules</a> is a Senior Instructor and Principal Consultant with IBM Information Management World-Wide Education and works from New Rochelle, NY. Glen joined IBM in 2001 as a result of IBM's acquisition of Informix Software. He has worked at IBM, and previously at Informix Software, as an instructor, a course developer, and in the enablement of instructors worldwide. He teaches courses in BigData (BigInsights and Streams), Optim, Guardium, and DB2, and Informix databases. He has a BSc in Mathematics from the University of Adelaide, South Australia; an MSc in Computer Science from the University of Birmingham, England; and has just completed a Ph.D. in Education (Educational Technology) at Walden University. His early work life was as a high school teacher in Australia. In the 1970s he designed, programmed, and managed banking systems in Manhattan and Boston. In the 1980's he was a VP in Electronic Payments for Bank of America in San Francisco and New York. In the early 1990's he was an EVP in Marketing for a software development company and chaired the ANSI X12C Standards Committee on Data Security for Electronic Data Interchange (EDI). </p> </article> <article class="teacher"> <div class="teacher-image"> <img class="alignnone size-thumbnail wp-image-4107" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Leo%20Petrazickis.jpg" alt="Leons Petrazickis photo" /> </div> <h3>Leons Petrazickis</h3> <p> <a href="https://ca.linkedin.com/in/leonsp">Leons Petrazickis</a> was the Ombud for Hadoop content on Skills Network as well as the Platform Architect for Skills Network Labs. As a senior software developer at IBM, he uses Ruby, Python, and Javascript to develop microservices and web applications, as well as manage containerized infrastructure. </p> </article> </section>

Big Data 101

How big is big and why does big matter and what does Apache Hadoop have to do with it? In this course you will see the Big Data big picture and you will learn the terminology used in Big Data discussions.

<div class="trix-content">
  <div>DevOps is a software methodology that integrates application development and information technology (IT) operations. With a DevOps approach, both teams work together to support the software lifecycle from code design to deployment to production. DevOps enables continuous delivery, which reduces time to market and cost. With IBM Cloud Continuous Delivery you canbuild, test, and deliver applications by using DevOps practices and industry-leading tools.<br /><br />In this guided project you will discover how to create a Node.js application in IBM Cloud, use IBM Cloud Continuous Delivery to create a Git repository, set up a delivery pipeline, edit code in the Eclipse Orion Web integrated development environment (IDE), and build, deploy, and test your application in IBM Cloud. Node.js is a server-side programming framework that uses JavaScript as its programming language. You will work with the access management features that you can use to make the application available to other users. You will explore three useful ways to update your application from the Web IDE and see how Continuous Delivery automatically deploys updates from your Git repository.<br /><br />This guided project will give you a great start in using DevOps and Git to build, deploy, and update a cloud application.<br /><br /><strong>A Look at the Project Ahead</strong><br />Once you have completed this project, you'll be able to:</div><ul>
<li>Enable your application to use IBM Cloud Continuous Delivery</li>
<li>Create a Git repository to manage your source code</li>
<li>View and edit code in the Eclipse Orion Web integrated development environment (IDE)</li>
<li>Build and deploy code to IBM Cloud</li>
<li>Test the application in IBM Cloud</li>
</ul><div>
<br /><strong>What You’ll Need</strong><br />Just a web browser and an IBM Cloud account!<br /><br />Everything else is provided to you on IBM Cloud, where you will have access to the IBM Cloud Continuous Delivery service.<br /><br /><strong>Your Instructor</strong><br />Upkar Lidder</div>
</div>


Perform DevOps on IBM Cloud

In this guided project, you will explore DevOps services in IBM Cloud, focusing on IBM Cloud Continuous Delivery. You will explore how to build, test, and deliver applications in IBM Cloud by using DevOps practices and industry-leading tools. You will practice using the Eclipse Orion Web integrated development environment (IDE) to make changes to your application and deploy updates to IBM Cloud.

<section class="about">
<section>
<h2>ABOUT THIS COURSE</h2>

<p>Find out the truth about what Data Science is. Hear from real practitioners telling real stories about what it means to work in data science. This course was formerly named <strong>Data Science 101</strong>.</p>
</section>

<hr />
<h2>COURSE SYLLABUS</h2>

<h3>Module 1 - Defining Data Science</h3>

<ul>
	<li id="magicdomid21">What is data science?</li>
	<li id="magicdomid22">There are many paths to data science</li>
	<li id="magicdomid23">Any advice for a new data scientist?</li>
	<li id="magicdomid24">What is the cloud?</li>
	<li id="magicdomid25">"Data Science: The Sexiest Job in the 21st Century"</li>
</ul>

<h3>Module 2 - What do data science people do?</h3>

<ul>
	<li>A day in the life of a data science person</li>
	<li>R versus Python?</li>
	<li>Data science tools and technology</li>
	<li>"Regression"</li>
</ul>

<h3>Module 3 - Data Science in Business</h3>

<ul>
	<li>How should companies get started in data science?</li>
	<li>Tips for recruiting data science people</li>
	<li>"The Final Deliverable"</li>
</ul>

<h3>Module 4 - Use Cases for Data Science</h3>

<ul>
	<li>Applications for data science</li>
	<li>"The Report Structure"</li>
</ul>

<h3>Module 5 -Data Science People</h3>

<ul>
	<li>Things data science people say</li>
	<li>"What Makes Someone a Data Scientist?"</li>
</ul>

<hr />
<h2>GENERAL INFORMATION</h2>

<ul>
	
	<li>This course is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be audited as many times as you wish.</li>
</ul>

<hr />
<h2>RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE</h2>

<ul>
	<li>None</li>
</ul>

<hr />
<h2>REQUIREMENTS</h2>

<section>
<ul>
	<li>None</li>
</ul>
</section>

<section>
<section>
<hr />
<h2>COURSE STAFF</h2>
</section>

<section>
<article>
<p><img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Shingai.png" style="width: 150px; height: 185px; border-radius: 10px" /></p>

<h3>Fireside Analytics Inc.</h3>

<p>Shingai Manjengwa <a href="https://twitter.com/Tjido">(@Tjido)</a> is the CEO of  <a href="http://www.firesideanalytics.com/">Fireside Analytics Inc</a>., an ed-tech start-up that develops customized online and in-person courses to teach digital literacy, data science, data visualization, and coding to high school students, policymakers, senior executives, small business owners, and working professionals. Data Science courses by Fireside Analytics have over 300,000 registered learners on platforms like IBMs CognitiveClass.ai and Coursera.</p>

<p>An IBM Influencer and <a href="https://www.amazon.com/Computer-Cancelled-Music-Lessons-Analytics/dp/1999481828/">Author</a>, Shingai has a Masters in Business Analytics from NYU Stern. Shingai is also the founder of <a href="https://firesideanalytics.academy/">Fireside Analytics Academy</a>, a registered private high school (BSID: 886528) that teaches high school students to solve problems with data.</p>

<p>What is Data Science? Data Science is the process of ethically acquiring, engineering, analyzing, visualizing and ultimately monetizing data.</p>

<p>Connect with us on <a href="https://www.facebook.com/firesideanalytics/">Facebook</a>, <a href="https://twitter.com/fireside_info">Twitter</a> and <a href="https://www.linkedin.com/company/fireside-analytics-inc-">LinkedIn</a>.</p>
</article>
</section></section></section>

Data Science 101

The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do, this field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

<section class="about">
<h2>About This Course</h2>
"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.

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.
</section>

<section class="about">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 several data visualization libraries in Python, namely Matplotlib, seaborn, and Folium.<span class="author-244633955 b font-color-3c3c3c font-size-medium"> </span>
</section>

<h2>Course Syllabus</h2>
<strong>Module 1 - <span class="author-284865554 font-color-000000 font-size-medium">Introduction to Visualization Tools</span></strong>
<div id="magicdomid26" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Introduction to Data Visualization</span></li>
</ul>
</div>
<div id="magicdomid27" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Introduction to Matplotlib</span></li>
</ul>
</div>
<div id="magicdomid28" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Basic Plotting with Matplotlib</span></li>
</ul>
</div>
<div id="magicdomid29" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Dataset on Immigration to Canada</span></li>
</ul>
</div>
<div id="magicdomid30" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Line Plots</span></li>
</ul>
</div>
<strong>Module 2 - <span class="author-284865554 font-color-000000 font-size-medium">Basic Visualization Tools</span>
</strong>
<div id="magicdomid31" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Area Plots</span></li>
</ul>
</div>
<div id="magicdomid32" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Histograms</span></li>
</ul>
</div>
<div id="magicdomid33" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Bar Charts</span></li>
</ul>
</div>
<strong>Module 3 - <span class="author-284865554 font-color-000000 font-size-medium">Specialized Visualization Tools</span>
</strong>
<div id="magicdomid34" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Pie Charts</span></li>
</ul>
</div>
<div id="magicdomid35" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Box Plots</span></li>
</ul>
</div>
<div id="magicdomid36" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Scatter Plots</span></li>
</ul>
</div>
<div id="magicdomid37" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Bubble Plots</span></li>
</ul>
</div>
<strong>Module 4 - <span class="author-284865554 font-color-000000 font-size-medium">Advanced Visualization Tools</span>
</strong>
<div id="magicdomid38" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Waffle Charts</span></li>
</ul>
</div>
<div id="magicdomid39" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Word Clouds</span></li>
</ul>
</div>
<div id="magicdomid39" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium">Seaborn and Regression Plots</span></li>
</ul>
</div>
<strong>Module 5 - <span class="author-284865554 font-color-000000 font-size-medium">Creating Maps and Visualizing Geospatial Data</span>
</strong>
<div id="magicdomid40" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium"> Introduction to Folium</span></li>
</ul>
</div>
<div id="magicdomid41" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium"> Maps with Markers</span></li>
</ul>
</div>
<div id="magicdomid42" class="ace-line">
<ul class="list-bullet3">
 	<li><span class="author-284865554 font-color-000000 font-size-medium"> Choropleth Maps</span></li>
</ul>
</div>
<h2>General Information</h2>
<ul>

 	<li>This course is self-paced.</li>
 	<li>It can be taken at any time.</li>
 	<li>It can be audited as many times as you wish.</li>
</ul>
<h2>Requirements</h2>
<section class="prerequisites">
<ul>
 	<li>Python 101</li>
 	<li>Data Analysis with Python</li>
</ul></section>

Data Visualization with Python

Data visualization is the graphical representation of data in order to interactively and efficiently convey insights to clients, customers, and stakeholders in general. It is 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 interesting graphics and charts and customize them to make them more effective and more pleasing to your audience.

<h2>LEARN TO ANALYZE DATA WITH PYTHON</h2>

<p>Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!</p>

<p>You will learn how to:</p>

<ul>
	<li>Import data sets</li>
	<li>Clean and prepare data for analysis</li>
	<li>Manipulate pandas DataFrame</li>
	<li>Summarize data</li>
	<li>Build machine learning models using scikit-learn</li>
	<li>Build data pipelines</li>
</ul>

<p>Data Analysis with Python is delivered through lecture, hands-on labs, and assignments. It includes following parts:</p>

<ul>
	<li>Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.</li>
</ul>

<hr />
<h2>COURSE SYLLABUS</h2>

<h3>Module 1 - Importing Datasets</h3>

<ul>
	<li>Learning Objectives</li>
	<li>Understanding the Domain</li>
	<li>Understanding the Dataset</li>
	<li>Python package for data science</li>
	<li>Importing and Exporting Data in Python</li>
	<li>Basic Insights from Datasets</li>
</ul>

<p><strong>Module 2 - Cleaning and Preparing the Data</strong></p>

<ul>
	<li>Identify and Handle Missing Values</li>
	<li>Data Formatting</li>
	<li>Data Normalization Sets</li>
	<li>Binning</li>
	<li>Indicator variables</li>
</ul>

<h3>Module 3 - Summarizing the Data Frame</h3>

<ul>
	<li>Descriptive Statistics</li>
	<li>Basic of Grouping</li>
	<li>ANOVA</li>
	<li>Correlation</li>
	<li>More on Correlation</li>
</ul>

<h3>Module 4 - Model Development</h3>

<ul>
	<li>Simple and Multiple Linear Regression</li>
	<li>Model Evaluation Using Visualization</li>
	<li>Polynomial Regression and Pipelines</li>
	<li>R-squared and MSE for In-Sample Evaluation</li>
	<li>Prediction and Decision Making</li>
</ul>

<h3>Module 5 - Model Evaluation</h3>

<ul>
	<li>Model  Evaluation</li>
	<li>Over-fitting, Under-fitting and Model Selection</li>
	<li>Ridge Regression</li>
	<li>Grid Search</li>
	<li>Model Refinement</li>
</ul>

<hr />
<h2>GENERAL INFORMATION</h2>

<ul>
	
	<li>This course is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be audited as many times as you wish.</li>
	<li>Python programming, Statistics</li>
</ul>

<hr />
<h2>REQUIREMENTS</h2>

<section>
<ul>
	<li>Some Python experience is expected</li>
	<li>Python for Data Science</li>
</ul>

<hr />
<h2>COURSE STAFF</h2>

<article>
<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Joseph%20Santarcangelo%20pic.png" style="width: 150px; height: 147px; border-radius: 10px" /></p>

<h3>Joseph Santarcangelo Ph.D.</h3>

<p><a href="https://www.linkedin.com/in/joseph-s-50398b136/">Joseph</a> has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.</p>

<p> </p>
</article>

<article>
<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Mahdi%20Noorian%20pic.png" style="width: 150px; height: 147px; border-radius: 10px" /></p>

<h3>Mahdi Noorian Ph.D.</h3>

<p><a href="https://www.linkedin.com/in/mahdi-noorian-58219234/">Mahdi Noorian</a> is a Postdoctoral Fellow at the<a href="http://ls3.rnet.ryerson.ca/?page_id=13"> Laboratory for Systems, Software and Semantics (LS3)</a> of the Ryerson University. He holds a Ph.D degree in Computer Science from University of New Brunswick. As a Data Scientist, he is interested in application of machine learning, data mining, optimization, and semantic data analysis for big data to solve the real-world problems.</p>

<p> </p>
</article>
</section>

<h3>Other Contributors</h3>

<p>The following individuals also contributed to this course: <a href="https://www.linkedin.com/in/baharetalayian/">Bahare Talayian</a>, <a href="https://www.linkedin.com/in/fiorellawever/" rel="noopener" target="_blank">Fiorella Wenver, </a><a href="https://www.linkedin.com/in/ke-xing-1987a3b0/">Ke Xing</a> , <a href="https://www.linkedin.com/in/xiao-han-steven-dong-38128b4b/">Steven Dong</a> and <a href="https://www.linkedin.com/in/himavasudevan/">Hima Vasudevan</a></p>

<p> </p>

Data Analysis with Python

In this course you will learn about:  Data Acquisition How to Obtain Basic Insight From a Dataset Data 

<section class="about">
<h2>About This Course</h2>
Learn the fundamentals of <strong>Spark</strong>, the technology that is revolutionizing the analytics and big data world! Spark is an open source processing engine built around speed, ease of use, and analytics. If you have large amounts of data that requires low latency processing that a typical <strong>MapReduce </strong>program cannot provide, Spark is the way to go.

<ul>
	<li>Learn how it performs at speeds up to 100 times faster than Map Reduce for iterative algorithms or interactive data mining.</li>
	<li>Learn how it provides in-memory cluster computing for lightning fast speed and supports Java, Python, R, and Scala APIs for ease of development.</li>
	<li>Learn how it can handle a wide range of data processing scenarios by combining SQL, streaming and complex analytics together seamlessly in the same application.</li>
	<li>Learn how it runs on top of Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources such as HDFS, Cassandra, HBase, or S3.</li>
</ul>
</section>

<section class="course-syllabus">
<h2>Course Syllabus</h2>

<ul>
	<li><strong>Module 1 - Introduction to Spark - Getting started</strong>

	<ol>
		<li>What is Spark and what is its purpose?</li>
		<li>Components of the Spark unified stack</li>
		<li>Resilient Distributed Dataset (RDD)</li>
		<li>Downloading and installing Spark standalone</li>
		<li>Scala and Python overview</li>
		<li>Launching and using Spark’s Scala and Python shell ©</li>
	</ol>
	</li>
</ul>

<ul>
	<li><strong>Module 2 - Resilient Distributed Dataset and DataFrames</strong>

	<ol>
		<li>Understand how to create parallelized collections and external datasets</li>
		<li>Work with Resilient Distributed Dataset (RDD) operations</li>
		<li>Utilize shared variables and key-value pairs</li>
	</ol>
	</li>
</ul>

<ul>
	<li><strong>Module 3 - Spark application programming</strong>

	<ol>
		<li>Understand the purpose and usage of the SparkContext</li>
		<li>Initialize Spark with the various programming languages</li>
		<li>Describe and run some Spark examples</li>
		<li>Pass functions to Spark</li>
		<li>Create and run a Spark standalone application</li>
		<li>Submit applications to the cluster</li>
	</ol>
	</li>
</ul>

<ul>
	<li><strong>Module 4 - Introduction to Spark libraries</strong>

	<ol>
		<li>Understand and use the various Spark libraries</li>
	</ol>
	</li>
</ul>

<ul>
	<li><strong>Module 5 - Spark configuration, monitoring and tuning</strong>

	<ol>
		<li>Understand components of the Spark cluster</li>
		<li>Configure Spark to modify the Spark properties, environmental variables, or logging properties</li>
		<li>Monitor Spark using the web UIs, metrics, and external instrumentation</li>
		<li>Understand performance tuning considerations</li>
	</ol>
	</li>
</ul>
</section>

<section class="information">
<h2>General Information</h2>

<ul>
	
	<li>This course is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be audited as many times as you wish.</li>
</ul>
</section>

<section class="recommended skills prior to taking this course">
<h2>Recommended skills prior to taking this course</h2>

<ul>
	<li>Basic understanding of <a href="https://bigdatauniversity.com/courses/introduction-to-hadoop/">Apache Hadoop</a> and <a href="https://bigdatauniversity.com/courses/introduction-to-big-data/">Big Data</a>.</li>
	<li>Basic Linux Operating System knowledge.</li>
	<li>Basic understanding of the <a href="http://bigdatauniversity.com/learn/scala/" target="_blank">Scala</a>, <a href="https://bigdatauniversity.com/courses/introduction-python/">Python</a>, <a href="https://bigdatauniversity.com/courses/introduction-to-r/">R</a>, or Java programming languages.</li>
</ul>
</section>

<section class="course-staff">
<h2>Requirements</h2>

<ul>
	<li>Have taken the <a href="http://bigdatauniversity.com/courses/introduction-to-hadoop/" target="_blank">Hadoop 101</a> course on BDU.</li>
</ul>

<h2>Course Staff</h2>

<article class="teacher">
<div class="teacher-image"><img alt="Henry Quach, Instructor of" class="alignnone wp-image-2258 " src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Henry%20Quach%20pic.jpg" /></div>

<h3>Henry L. Quach</h3>

<p><a href="https://www.linkedin.com/in/henry-quach-87b60846" target="_blank">Henry L. Quach</a> is the Technical Curriculum Developer Lead for Big Data. He has been with IBM for 9 years focusing on education development. Henry likes to dabble in a number of things including being part of the original team that developed and designed the concept for the IBM Open Badges program. He has a Bachelor of Science in Computer Science and a Master of Science in Software Engineering from San Jose State University.</p>

<p> </p>
</article>

<article class="teacher">
<div class="teacher-image"><img alt="Alan Barnes" class="alignnone size-medium wp-image-2177" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Alan%20Barnes%20pic.png" /></div>

<h3>Alan Barnes</h3>
<a href="https://www.linkedin.com/in/alan-barnes-5b342330" target="_blank">Alan Barnes</a> is a Senior IBM Information Management Course Developer / Consultant. He has worked in several companies as a Senior Technical Consultant, Database Team Manager, Application Programmer, Systems Programmer, Business Analyst, DB2 Team Lead and more. His career in IT spans more than 35 years.</article>
</section>

Spark Fundamentals I

Ignite your interest in Spark with an introduction to the core concepts that make this general processor an essential tool set for working with Big Data.

<section class="about">
    <h2>About This Course</h2>
    Learn the basics of <strong>Apache Hadoop</strong>, a free, open source, Java-based programming framework. <strong>Why was it invented?</strong>
    <ul>
        <li>Learn about Hadoop's architecture and core components, such as MapReduce and the Hadoop Distributed File System (HDFS).</li>
        <li>Learn how to add and remove nodes from Hadoop clusters, how to check available disk space on each node, and how to
            modify configuration parameters.</li>
        <li>Learn about other Apache projects that are part of the Hadoop ecosystem, including Pig, Hive, HBase, ZooKeeper, Oozie,
            Sqoop, Flume, among others.  BDUprovides separate courses on these other projects, but we recommend you start
            here.</li>
    </ul>
</section>
<h2>Course Syllabus</h2>
<ul>
    <li><strong>Module 1 -  Introduction to Hadoop</strong>
        <ol>
            <li>Understand what Hadoop is</li>
            <li>Understand what Big Data is</li>
            <li>Learn about other open source software related to Hadoop</li>
            <li>Understand how Big Data solutions can work on the Cloud</li>
        </ol>
    </li>
</ul>
<ul>
    <li><strong>Module 2 - Hadoop Architecture</strong>
        <ol>
            <li>Understand the main Hadoop components</li>
            <li>Learn how HDFS works</li>
            <li>List data access patterns for which HDFS is designed</li>
            <li>Describe how data is stored in an HDFS cluster</li>
        </ol>
    </li>
</ul>
<ul>
    <li><strong>Module 3 - Hadoop Administration</strong>
        <ol>
            <li>Add and remove nodes from a cluster</li>
            <li>Verify the health of a clusterStart and stop a clusters components</li>
            <li>Modify Hadoop configuration parameters</li>
            <li>Setup a rack topology</li>
        </ol>
    </li>
</ul>
<ul>
    <li><strong>Module 4 - Hadoop Components</strong>
        <ol>
            <li>Describe the MapReduce philosophy</li>
            <li>Explain how Pig and Hive can be used in a Hadoop environment</li>
            <li>Describe how Flume and Sqoop can be used to move data into Hadoop</li>
            <li>Describe how Oozie is used to schedule and control Hadoop job execution</li>
        </ol>
    </li>
</ul>
<h2>General Information</h2>
<ul>
    
    <li>This course is self-paced.</li>
    <li>It can be taken at any time.</li>
    <li>It can be audited as many times as you wish.</li>
</ul>
<h2>Recommended skills prior to taking this course</h2>
<ul>
    <li>Knowledge about <a href="/courses/introduction-to-big-data/">Big Data concepts</a></li>
</ul>
<section class="prerequisites">
    <h2> Requirements</h2>
    <ul>
        <li>None</li>
    </ul>
</section>
<section class="course-staff">
    <h2>Course Staff</h2>
    <article class="teacher">
       
        <h3>Warren Pettit</h3>
        Warren has been with IBM for over 30 years. For the last 16 years, he has worked in Information Management education where
        he has been both an instructor and a course developer in the Data Warehouse and Big Data curriculums. For the nine
        years prior to his joining IBM, he was an application programmer and was responsible for developing a training program
        for newly hired programmers.
<p>&amp;nbsp</p>
      <p>&amp;nbsp</p>
    </article>
    <article class="teacher">
        <div class="teacher-image"><img class="alignnone wp-image-1996" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Asma%20Desai%20pic.png" alt="" /></div>
        <h3>Asma Desai</h3>
        <a href="https://www.linkedin.com/in/asmadesai" target="_blank">Asma Desai</a> just recently started with IBM. She
        has developed course content for introductory Java and graph theory. Prior to course development, she worked as a consultant
        using Big Data to fight fraud.
<p>&amp;nbsp</p>
      <p>&amp;nbsp</p>
    </article>
    <article class="teacher">
        <div class="teacher-image"><img class="alignnone wp-image-3371 size-thumbnail" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Daniel%20Tran.jpg" alt="" /></div>
        <h3>Daniel Tran</h3>
        <a href="https://www.linkedin.com/in/daniel-tran-84ab26b3" target="_blank">Daniel Tran</a> is an IBM Co-op Student
        working as a Technical Curriculum Developer in Toronto, Ontario. He develops courses to improve the education of
        customers who seek knowledge in the Big Data field. He has also reworked previously developed courses, updating them
        to be compatible with the newest software releases, as well as work at the forefront of recreating courses on a newly
        developed cloud environment. He has worked with various components that deal with Big Data, including Hadoop, Pig,
        Hive, HBase, MapReduce &amp; YARN, Sqoop, Oozie, and Phoenix. He has also worked on separate courses involving Machine
        Learning. Daniel is from the University of Alberta, where he has completed his third year of traditional Computer
        Engineering Co-op.
<p>&amp;nbsp</p>
      <p>&amp;nbsp</p>
    </article>
    <article class="teacher">
        <div class="teacher-image"><img class="alignnone size-thumbnail wp-image-4107" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Leo%20Petrazickis.jpg" alt="" /></div>
        <h3>Leons Petrazickis</h3>
        <a href="https://ca.linkedin.com/in/leonsp">Leons Petrazickis</a> is the Ombud for Hadoop content on IBM Big Data
        U as well as the Platform Architect for Big Data U Labs. As a senior software developer at IBM, he uses Ruby, Python,
        and Javascript to develop microservices and web applications, as well as manage containerized infrastructure.

    </article>
</section>

Hadoop 101

This free Apache Hadoop course introduces you to Big Data concepts, and teaches you how to perform distributed processing of large data sets with Hadoop.

<section class="about">
<h2>ABOUT THIS PYTHON COURSE</h2>

<p>This introduction to Python will kickstart your learning of <strong>Python for data science</strong>, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours.</p>

<p>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 free course is for you.</p>


<hr />
<h2>COURSE SYLLABUS</h2>

<h3><strong>Module 1 - Python Basics</strong></h3>

	<ul>
		<li>Your first program</li>
		<li>Types</li>
		<li>Expressions and Variables</li>
		<li>String Operations</li>
	</ul>
	
<h3><strong>Module 2 - Python Data Structures</strong></h3>

	<ul>
		<li>Lists and Tuples</li>
		<li>Sets</li>
		<li>Dictionaries</li>
	</ul>

<h3><strong>Module 3 - Python Programming Fundamentals</strong></h3>


	<ul>
		<li>Conditions and Branching</li>
		<li>Loops</li>
		<li>Functions</li>
		<li>Objects and Classes</li>
	</ul>

<h3><strong>Module 4 - Working with Data in Python</strong></h3>


	<ul>
		<li>Reading files with open</li>
		<li>Writing files with open</li>
		<li>Loading data with Pandas</li>
		<li>Working with and Saving data with Pandas</li>
	</ul>

<h3><strong>Module 5 - Working with Numpy Arrays and Simple APIs</strong></h3>


	<ul>
		<li>Numpy 1D Arrays</li>
               <li>Numpy 2D Arrays</li>
               <li>Simple APIs</li>
               <li>API Setup</li>
</ul>
<hr />
<h2>GENERAL INFORMATION</h2>

<ul>
	
	<li>This course is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be audited as many times as you wish.</li>
	<li>There is only <strong>ONE</strong> chance to pass the course, but multiple attempts per question</li>
</ul>

<hr />
<h2>RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE</h2>

<ul>
	<li> Basic Math</li>
</ul>

<hr />
<section>
<h2>COURSE STAFF</h2>
</section>

<article>
<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Joseph%20Santarcangelo%20pic.png" style="width: 150px; height: 150px; border-radius: 10px" title="Python for Data Science instructor Joseph Santarcangelo, PhD" /></p>
</article>

<p><a href="https://www.linkedin.com/in/joseph-s-50398b136/">Joseph</a> has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.</p>

<hr />
<h2>Other Contributors</h2>

<p>The following individual also contributed: <a href="https://www.linkedin.com/in/reevejamesd/">James Reeve</a>,  <a href="https://www.linkedin.com/in/daniel-rudnitski-266243143/">Daniel Rudnitski</a>, <a href="https://www.linkedin.com/in/johngerassimou/">John Gerassimou</a> and <a href="https://www.linkedin.com/in/agathacolangelo/">Agatha Colangelo</a>.</p>
</section>

Python  for Data Science

This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours. Upon its completion, you'll be able to write your own Python scripts. If you want to learn Python from scratch, this free course is for you.

<section class="about">
  <h2>About This Guided Project</h2>
  <p>You probably have heard Serverless Computing, Lambda, Kubernetes, Knative and other buzzwords thrown arround. By the time you complete this project in only 30 minutes you will not just learn how to play buzword bingo, you will actually deploy your first cloud native application in to a serverless infrastructure. You will see how your app will scale with user demand and how it can scale to zero so it costs you absolutely nothing when people are not actively using your app. You will understand how the new kind of serverless computing based on Kubernates and Knative is a real game changer for testing and experimenting with ideas and how invaluable it is when launching MVP (Minimum Viable Product).</p>

  <p>In this project you will be using IBM Cloud Code Engine to deploy a simple app that creates thumbnails for photo images. The goal of the project is not to develop such app but to learn how to deploy any type of an app in to a modern serverless environment powered by Kubernetes and Knative, specifically, IBM Cloud Code Engine.</p>
</section>

<section class="prerequisites">
  <h2>Requirements</h2>
  <p>You will need to create an IBM Cloud account and secure it with a credit card. The free resources allowed by the IBM Cloud Code Engine should be sufficient to complete this project and your credit card will not be charged if you stay within the free resource limits of your account.</p>
</section>

<section class="course-staff">
  <h2>Project Staff</h2>
  <article class="teacher">
    <div class="teacher-image">
      <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Doug_Davis.jpg" style="margin: 0 20px 0" alt="Doug Davis" />
    </div>

    <h3>Doug Davis</h3>
    <p>Delivering the next generation container-based hosting platform for IBM Cloud. As both a Technical Product Manager and Architect, it's a very unique role in which I get the opportunity to shape the direction of the project from multiple perspectives. This allows me to leverage my expertise in cloud computing, open-source technologies and leadership roles in external organizations (such as the CNCF) to provide very broad perspectives in both internal and external activities.</p>
  </article>

</section>

<section class="faq">
  <section class="responses">
    <h2>Frequently Asked Questions</h2>
    <article class="response">
      <h3>Do I need to install any software to participate in this project?</h3>
      <p>Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.</p>
    </article>

    <article class="response">
      <h3>What web browser should I use?</h3>
      <p>The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.</p>
    </article>

  </section>
</section>


Deploy a Serverless App for Image Processing

In this project you will learn about Serverless Computing, will practice deploying a real application to a serverless environment based on IBM Cloud Code Engine powered by Kubernetes and Knative open source projects. 

<section class="about">
<h2>ABOUT THIS COURSE</h2>

<p>This course introduces you to the IBM Cloud. You will learn about the many offerings and services on IBM Cloud that make it the most open and secure public cloud for developers and enterprises.</p>

<p>The course begins with an introduction to the IBM Cloud platform which covers topics such as data center locations and configuring identity and access management. You will discover the various Infrastructure-as-a-Service (IaaS) options available on IBM Cloud. Next, you will learn about the deployment options on IBM Cloud; this includes topics such as Containers, Kubernetes, and OpenShift. You will also become familiar with IBM Cloud services such as Databases, Artificial Intelligence and Watson, Blockchain, Internet of Things, and many others.</p>

<p>In addition to videos, you will also see demos of various IBM Cloud features and services in action, as well as perform hands-on labs to gain practical experience with IBM Cloud at no charge.</p>

<p>This course is of interest to anyone who wants to be a cloud practitioner and use Cloud skills as developers, architects, system engineers, network specialists, and many other roles. The material also serves the needs of those who perform the tasks of advising, building, moving, and managing cloud solutions. This course is also suitable for learners who want to prepare for IBM Cloud Foundations Certification.</p>

<hr />
<h2>LEARNING OBJECTIVES</h2>

<p>After completing this course, a learner will be able to:</p>

<ul>
	<li>Understand the different infrastructure services available on IBM Cloud.</li>
	<li>Access IBM Cloud using graphical interfaces, command line tools, and APIs.</li>
	<li>Discover appropriate IBM Cloud services available to deliver specific functionality.</li>
	<li>Articulate the different ways IBM Cloud delivers services to developers and operational teams.</li>
	<li>Summarize core groups of available database, integration, analytics, artificial intelligence, and DevOps services.</li>
	<li>Deploy an application on IBM Cloud using a Starter Kit.</li>
</ul>

<hr />
<h2>COURSE SYLLABUS</h2>

<p><strong>Module 1 - Introduction to IBM Cloud</strong></p>

<ul>
	<li>IBM Cloud Overview</li>
	<li>Locations, Regions, and Zones</li>
	<li>Account Types and Support Plans</li>
	<li>Billing and Usage</li>
	<li>Cost Estimator</li>
	<li>IAM</li>
</ul>

<p><strong>Module 2 - Infrastructure (Compute, Networking, and Storage)</strong></p>

<ul>
	<li>Virtual Servers and Bare Metal</li>
	<li>Block and File Storage</li>
	<li>Object Storage</li>
	<li>Network Services</li>
	<li>Virtual Private Cloud</li>
	<li>VMWare</li>
</ul>

<p><strong>Module 3 - Deploying Applications</strong></p>

<ul>
	<li>Containers and Kubernetes</li>
	<li>OpenShift</li>
	<li>Cloud Foundry</li>
	<li>Cloud Functions</li>
</ul>

<p><strong>Module 4 - Services on IBM Cloud</strong></p>

<ul>
	<li>Databases</li>
	<li>Integration</li>
	<li>Artificial Intelligence</li>
	<li>Analytics</li>
	<li>DevOps</li>
	<li>Blockchain</li>
	<li>Internet of Things</li>
	<li>Cloud Paks</li>
</ul>

<p><strong>Module 5 - Final Exam</strong></p>

<ul>
	<li>Final Exam</li>
</ul>

<hr />
<h2>GENERAL INFORMATION</h2>

<ul>
	
	<li>This course is self-paced.</li>
	<li>It can be taken at any time.</li>
</ul>

<hr />
<h2>RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE</h2>

<p>There are no hard requirements needed to be able to take the course, however a foundation level understanding of Cloud Computing will help, such as that obtained from the Introduction to Cloud course (CC0101EN)</p>

<hr />
<h2>REQUIREMENTS</h2>

<section>
<ul>
	<li>None</li>
</ul>

<hr />
<h2>COURSE STAFF</h2>
</section>

<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Steve%20Martinelli.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<h3>Steve Martinelli</h3>

<section><strong>Steve Martinelli</strong> is a a Senior Technical Staff Member (STSM) at IBM, where he's focused on delivering developer advocacy content. Prior to this role he worked on upstream OpenStack features; internal tools for generating compatibility reports across all IBM products; and developing WebSphere Application Server’s configuration tools. Aside from whipping up features and fixing bugs he tends to find himself in leadership roles where his knack for organization can be used for good.</section>

<section> </section>

<section>
<article>
<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Horea.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<h3>Horea Porutiu</h3>

<section><strong>Horea Porutiu </strong>is a software engineer interested in artificial intelligence, data science, and cloud. In my personal time, I enjoy playing tennis and surfing.</section>
</article></section></section>

IBM Cloud Essentials V3

Get hands-on experience with IBM Cloud, Cloud Foundry, and best practices for agile and test-driven development.

<h2>About This Course</h2>
<p>Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which consitutes the vast majority of data in the world.</p>

<p>TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.</p>

<p>In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.</p>

<h2>Course Syllabus</h2>

<strong>Module 1 – Introduction to TensorFlow</strong>
<ul>
 	<li>HelloWorld with TensorFlow</li>
 	<li>Linear Regression</li>
 	<li>Nonlinear Regression</li>
 	<li>Logistic Regression</li>
 	<li>Activation Functions</li>
</ul>
<strong>Module 2 – Convolutional Neural Networks (CNN)</strong>
<ul>
 	<li>CNN History</li>
 	<li>Understanding CNNs</li>
 	<li>CNN Application</li>
</ul>
<strong>Module 3 – Recurrent Neural Networks (RNN)</strong>
<ul>
 	<li>Intro to RNN Model</li>
 	<li>Long Short-Term memory (LSTM)</li>
 	<li>Recursive Neural Tensor Network Theory</li>
 	<li>Recurrent Neural Network Model</li>
</ul>
<strong>Module 4 - </strong><strong>Unsupervised Learning</strong>
<ul>
 	<li>Applications of Unsupervised Learning</li>
 	<li>Restricted Boltzmann Machine</li>
 	<li>Collaborative Filtering with RBM</li>
</ul>
<strong>Module 5 - Autoencoders</strong>
<ul>
 	<li>Introduction to Autoencoders and Applications</li>
 	<li>Autoencoders</li>
 	<li>Deep Belief Network</li>
</ul>
<h2>Recommended skills prior to taking this course</h2>
<ul>
 	<li>Neural Network</li>
</ul>
<h2>Requirements</h2>
<section class="prerequisites">
<ul>
 	<li>Python programming</li>
</ul>
<h2>Course Staff</h2>
  <section class="course-staff">
<article class="teacher">
<div class="teacher-image"><img class="alignnone wp-image-2254 size-medium" title="Dr. Saeed Aghabozorgi, TensorFlow Course Instructor" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Saeed%20pic.png" alt="Dr. Saeed Aghabozorgi, TensorFlow Course Instructor" /></div>
<a href="https://ca.linkedin.com/in/saeedaghabozorgi" target="_blank">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.

    </article>
<h3>Course Development Team</h3>
Thanks to course developement team, interns and all individuals contributed to the development of this course: Kiran Mantri, Shashibushan Yenkanchi, Jag Rangrej, Naresh Vempala, Walter Gomes, Anita Vincent, Gabriel Sousa, Francisco Magioli, Victor Costa, Erich Sato, Luis Otavio and Rafael Belo.
</section></section>

Deep Learning with TensorFlow

Majority of data in the world are unlabeled and unstructured data, for instance images, sound, and text data. Shallow neural networks cannot easily capture relevant structure in these kind of data, but deep networks are capable of discovering hidden structures within these data. In this course, you will use TensorFlow library to apply deep learning on different data types to solve real world problems.

<section class="about">
<h2>About This Course</h2>
Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. 
  
  <h2>Learning Objectives</h2>
  in this course, you will learn:

<ul>
	<li>The major steps involved in tackling a data science problem.</li>
	<li>The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.</li>
	<li>How data scientists think!</li>
</ul>
</section>


<h2>Course Syllabus</h2>

<p><strong>Module 1: From Problem to Approach</strong></p>

<ul>
	<li>Business Understanding</li>
	<li>Analytic Approach</li>
</ul>

<p><strong>Module 2: From Requirements to Collection </strong></p>

<ul>
	<li>Data Requirements</li>
	<li>Data Collection</li>
</ul>

<p><strong>Module 3: From Understanding to Preparation </strong></p>

<ul>
	<li>Data Understanding</li>
	<li>Data Preparation</li>
</ul>

<p><strong>Module 4: From Modeling to Evaluation</strong></p>

<ul>
	<li>Modeling</li>
	<li>Evaluation</li>
</ul>

<p><strong>Module 5: From Deployment to Feedback</strong></p>

<ul>
	<li>Deployment</li>
	<li>Feedback</li>
</ul>

<h2 style="padding-top: 15px">General Information</h2>

<ul>
	
	<li>This course is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be audited as many times as you wish.</li>
</ul>


<h2>Requirements</h2>

<section class="prerequisites">
<ul>
	<li>Data Science Hands-on with Open Source Tools(Tools for Datascience) (DS0105EN)</li>
</ul>

<h2>Recommended skills prior to taking this course</h2>

<ul>
	<li>Passion for Data Science</li>
</ul>
</section>


Data Science Methodology

Grab your lab coat, beakers, and pocket calculator ... wait what? Wrong path! Fast forward and get in line with emerging data science methodologies that are in use and are making waves or rather predicting and determining which wave is coming and which one has just passed.

In this hands-on Internet of Things (IoT) course, you'll build Node-RED flows (applications) running on the cloud and connect them to more flows and Swift applications. You'll use those applications to track the temperature of a Raspberry Pi CPU, store that data in a Cloudant NoSQL database, take pictures with the RaspCam, use AI to recognize objects in the pictures, send Twitter notifications, and send simple commands from your smartphone to a robot called iRobot Create 2.

You don't need to purchase the iRobot to be successful in this course. But playing with a programmable robot can be a lot more fun!

Robots are coming! Build IoT apps with Watson, Swift, and Node-RED

Learn how to program a simple robot (TJBot) to move its arm, recognize objects, flash a light, speak, and more. You can build simple apps to control TJBot by using Node.js or even Node-RED. If you don't want to purchase the TJBot, you use a simulator instead.

<div class="trix-content">
  <div>
<strong>About</strong><br />How do we tell apart cats and dogs? As humans, this is an easy task. Dogs don't have claws as sharp as cats. Dogs are social animals whereas cats <del>plot your demise while you sleep</del> prefer their own space. Computers observe these distinctions in a different way. Support Vector Machine is one of the ways that trains a machine to classify an image it sees as a cat or dog. Let's delve right in!<br /><br /><strong>A Look at the Project Ahead</strong><br />After completing this guided project you will be able to:</div><ul>
<li>Extract H.O.G. features from images</li>
<li>Train an SVM model on image inputs</li>
<li>Tune hyperparameters with Grid Search and evaluate model performance</li>
<li>Classify new images of cats and dogs with SVM</li>
</ul><div>
<br /><strong>What You'll Need</strong><br />This course mainly uses Python, specifically the OpenCV, sklearn, and numpy libraries. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.</div><div>
<br /><strong>Frequently Asked Questions</strong>
</div><div>&gt; <em>Do I need to install any software to participate in this project?</em>
</div><div>Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.</div><div>&gt; <em>What web browser should I use?</em>
</div><div>The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.</div><div>
<br /><strong>Your Instructor</strong>
</div><div>
<a href="https://www.linkedin.com/in/aije-egwaikhide/?utm_medium=Exinfluencer%5C&amp;utm_source=Exinfluencer%5C&amp;utm_content=000026UJ%5C&amp;utm_term=10006555%5C&amp;utm_id=NA-SkillsNetwork-Channel-cvstudio-2021-01-01"><strong>Aije Egwaikhide</strong></a><strong><br /></strong>Aije is a Data Scientist at IBM who holds a degree in Economics and Statistics from the University of Manitoba and a Post-grad in Business Analytics from St. Lawrence College, Kingston. She is currently pursuing her Masters in Management Analytics at Queens University. She is part of the IBM Developer Skills Network group where she brings her real-world experience to the courses she creates.<br /><br /><a href="https://www.linkedin.com/in/cindy-shih-ting-huang/?utm_medium=Exinfluencer&amp;utm_source=Exinfluencer&amp;utm_content=000026UJ&amp;utm_term=10006555&amp;utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMPY0181ENSkillsNetwork36484875-2022-01-01"><strong>Cindy Huang</strong></a><strong><br /></strong>Cindy is a data science associate of the Skills Network team. She has a passion for machine learning to improve user experience, especially in the area of computational linguistics.</div>
</div>


Classifying Cats & Dogs with Support Vector Machine

Classify images of cats and dogs by extracting Histogram of Oriented Gradients features from them for a Support Vector Machine model! In particular, you will be able to feed an image to the model yourself and get a prediction.

<div class="trix-content">
  <div>
<strong>About</strong><br />On the streets, you see someone wearing a T-Shirt you really like. You visit an online shopping site later that day, uploading an image of the shirt into the search bar and found an exact match. How is it possible that we don't need to search by words, but instead can use images now?  The "magic" behind this feature is a system for image querying. This guided project will help you build an image querying prototype. The project is split into two parts. The first part focuses on building the image encoding system and the second part focuses on building the image query system.</div><div>This application has many business use cases such as:</div><ol>
<li>Enabling customers to look for similar apparel, furniture, auto parts etc.</li>
<li>Help eliminate near duplicate images from databases or catalogues.</li>
<li>Enable image to be used as feature embedding for modeling tasks.</li>
<li>Build image based recommendation systems.</li>
</ol><div>
<br /><strong>A Look at the Project Ahead</strong><br />After completing this Guided Project, you will be able to:<br />- Setup an Image Encoding service that accepts input images and produces embeddings</div><div>- Explore techniques for generating embeddings</div><div>- Generate the embeddings for the dataset and save it on disc</div><div>- Import the embeddings dataset from previous notebook</div><div>- Generate embeddings for a query image</div><div>- Search the embeddings dataset for closest match for the given query image</div><div>
<br /><strong>What You'll Need</strong><br />This course mainly uses Python. Prior experience with libraries such as Tensorflow and PIL are recommended but not required.  System requirements for if you run the labs on a local machine include TensorFlow 2.x and 2GB of storage (for storing the dataset).</div><div>
<br /><strong>Frequently Asked Questions</strong>
</div><div>&gt; <em>Do I need to install any software to participate in this project?</em>
</div><div>Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.</div><div>&gt; <em>What web browser should I use?</em>
</div><div>The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.</div><div>
<br /><strong>Your Instructors</strong><br /><strong>Kai Niu</strong>
</div>
</div>


Building a Visual Search Engine

Build a visual search engine based on images. Search for visually close images from a database given a sample query image. 

<div class="trix-content">
  <div>In this hands-on project you will learn about the Monte Carlo method and calculate the ruin probability of an insurance company using Apache Spark by means of parallel Monte Carlo method.<br /><br />
</div><h1><strong> Learning Objectives</strong></h1><div><br /></div><div>1.  After completing this lab you will be able to:</div><ul>
<li>Understand and apply Monte Carlo method.</li>
<li>Get the basic idea about accuracy of the method</li>
<li>Calculate the "Pi" number by means of the method</li>
<li>Parallelize the method using Apache Spark</li>
</ul><div>2.  Understand the Ruin probability of an insurance company<br />3.  Calculate the Ruin probability using Monte Carlo method.</div>
</div>


Insurance Risk assessment with MonteCarlo method using Apache Spark.

Learn about the Monte Carlo method and calculate the ruin probability of an insurance company using Apache Spark.

<div class="trix-content">
  <div>
<strong>About</strong><br />One of the coolest parts about building machine learning models is being able to share them with others so they know what you have accomplished. This is exactly why we should learn how to deploy our models, so anyone can interact with them through a nice user interface. In this guided project, we will build a PyScript-based web application from scratch in order to deploy a linear regression model in our browser. Since we will be using a simple methodology, no prior knowledge in machine learning will be required to complete this guided project.<br /><br /><strong>A Look at the Project Ahead</strong><br />After completing this Guided Project, you will be able to:<br />- Describe the key components of PyScript front-end framework.<br />- Run machine learning models in your browser.<br />- Build a simple data visualization web page.<br /><br /><strong>What You'll Need</strong><br />This course mainly uses Python and HTML. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.</div><div>
<br /><strong>Frequently Asked Questions</strong>
</div><div>&gt; <em>Do I need to install any software to participate in this project?</em>
</div><div>Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.</div><div>&gt; <em>What web browser should I use?</em>
</div><div>The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.</div><div>
<br /><strong>Your Instructor</strong><br /><strong>Kopal Garg</strong><br />I am a Data Scientist Intern at IBM, and a Masters student in computer science at the University of Toronto. I am passionate about building AI-based solutions that improve various aspects of human life. </div><div><br /></div>
</div>


Use PyScript to build a dashboard in the browser

In this project you will practice building and deploying a machine learning-based application to a serverless environment. You will do this using PyScript which uses HTML and Python as the scripting language. 

<div class="trix-content">
  <div> Throughout 2020 and 2021, the easiest way to connect with others is through web conferencing software. Whether it's for school, work, or just hanging out with friends, there's no shortage of options online. One of those options is Discord! Though it was created initially for the video gaming community, Discord has become a fantastic option for all types of users. Along with the ability to voice chat, video call, instant message, and screen share, one of Discord’s key features is the ability to have bots in the channel. Discord bots are extremely flexible in what they can do. Just send a simple command to them, and they could do things like respond to you with information, play sounds in a voice channel, manage your users, and many more!</div><div> </div><div>There are so many bots out there that have become essential to a great Discord server. In this guided project, we’re going to show you how to set up a basic bot. Then, we’ll show you how to add Watson Assistant – an AI chatbot created by IBM. We’ll also give your bot the option to respond through voice channels. To top it all off, we’ll host it on IBM Code Engine so you don’t have to rely on your computer constantly being on to host your bot.</div><div> </div><h1><strong>Learn by Doing</strong></h1><div>A guided project is a hands-on tutorial designed to help you learn a particular technology by doing a real project. It includes step-by-step instructions with explanations, examples, and exercises that can be followed and practiced in a lab environment.</div><div>Hands-on skills are highly sought out by employers when determining job readiness. Guided projects are interactive, on-demand and will equip you with practical abilities that can be applied on the job!</div><div> </div><h1><strong>A Look at the Project Ahead</strong></h1><div>Once you have completed this project, you'll be able to: </div><ul>
<li>Create a Discord bot with Discord.js</li>
<li>Add Watson Assistant to your bot</li>
<li>Add Watson Text-to-Speech (TTS) to your bot</li>
<li>Host it for free* on IBM Code Engine</li>
</ul><div> *A credit card is required but you won't be charged until you reach a certain usage.<br /><br />
</div><h1><strong>What You’ll Need  </strong></h1><div>Just a web browser! Though some basic programming knowledge would be nice, it's not required!</div><div>Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to Node.js, IBM Cloud CLI, and Docker which are tools that we offer as part of the IBM Skills Network Labs environment. This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.<br /><br />
</div><h1><strong>Your Instructors</strong></h1><div>Aster Wu, IBM<br />Rohit Arora, IBM</div>
</div>


Deploy an AI-powered Discord Bot with a Voice

Discord is a fantastic way to connect with your friends or host a community and a bot is a perfect way to add features to your server. Not only will this guided project show you how to get started in creating a bot, but you'll also learn how to power it with an AI chatbot and give it a voice. Finally, you'll host it somewhere so that the bot is online without your computer being on. All of this for free!

<div class="trix-content">
  <h1>About</h1><div>An API, or Application Programming Interface is a way for different programs to communicate with one another. There are many different standard formats and architectures of APIs, a common one being a REST API. In general, the development of REST APIs can be complicated, luckily, we have libraries such as ExpressJS. This allows us to simplify the development process by abstracting away much of the technical and tedious details of setting up a server. In this guided project, you will get to try your hand at developing a REST API and appreciate the benefits of using Express. Reap the benefits of learning these valuable skills that make you a more effective programmer by taking this Guided Project today.<br /><br />
</div><h1>A Look at the Project Ahead</h1><div>After completing this guided project, you will be able to:<br />- Recognize Restful design constraints<br />- Use the Express framework to implement a server side application<br />- Demonstrate a high level understanding of HTTP requests and responses<br />- Implement an API while following good practices</div>
</div>


Building a RESTful API with Express

In this guided project, you will create a simple Restful API using Express. The emphasis of the project is on understanding the REST protocol and getting started with Express by creating an API that allows the client to handle information about chilies.

<section class="about">
<h2>About This Course</h2>
This chatbot course provides a practical introduction that will teach you everything you need to know to plan, build, and deploy your first chatbot.

Leveraging the cognitive computing power of Watson Assistant, you will be able to design your own chatbot without the need to write any code. You'll also learn how to quickly deploy your chatbot on WordPress-based sites.

If you want to learn how to create virtual assistants — and perhaps create one for your own business — this free chatbot course is for you.

</section>
<h2>Course Syllabus</h2>

 	
<strong>Module 1 - Introduction to Chatbots</strong>
<ul>
<li>What are chatbots?
</li><li>Chatbots are trending
</li><li>Leader in the Industry
</li><li>Lab 1: Create an instance of Watson Assistant
</li><li>Quizzes</li></ul>
<p></p>

<strong>Module 2: Working with Intents</strong><ul>
<li>How chatbots work
</li><li>Understanding Intents
</li><li>Lab 2: Create Dialog Skill and Intents
</li><li>Lab 3: Import Intents
</li><li>Quizzes
  </li><p></p></ul>
<strong>Module 3: Working with Entities</strong><ul>

<li>Understanding Entities
</li><li>Lab 4: Create Entities
</li><li>Lab 5: Import and Export Entities
</li><li>Quizzes</li><p></p></ul>
<strong>Module 4: Defining the Dialog</strong><ul>

<li>Putting it all together
</li><li>Building user-friendly chatbots
</li><li>Lab 6: Implement the Dialog
</li><li>Lab 7: Define Domain-Specific Intents
</li><li>Quizzes</li><p></p></ul>
<strong>Module 5: Deploying your Chatbot</strong><ul>

<li>Deploying to a WordPress site
</li><li>Lab 8 - Add a preview and retrieve your credentials
</li><li>Lab 9: Deploy your Chatbot
</li><li>Quizzes</li><p></p></ul>
<strong>Module 6: Advanced concepts – Part 1</strong><ul>

<li>Working with Context Variables and Slots
</li><li>Lab 10: Explore Context Variables
</li><li>Lab 11: Master Slots
</li><li>Quizzes</li><p></p></ul>
<strong>Module 7: Advanced concepts – Part 2</strong><ul>

<li>Understanding Digressions
</li><li>Lab 12: Enable Digressions
</li><li>Lab 13: Get to know the Analytics tab
</li><li>Lab 14: Create your own Chatbot
</li><li>Watson Assistant in the Private Cloud
</li><li>Quizzes</li><p></p></ul>
<strong>Summary</strong><ul>

<li>Conclusion
</li><li>IBM Offer for Chatbot makers
</li><li>Final Exam</li></ul>

<h2>Recommended skills prior to taking this course</h2>
<ul>
 	<li>None</li>
</ul>
<h2>Grading scheme</h2>
<ul>
 	<li>The minimum passing mark for the course is 70%, where the review questions are worth 50% and the final exam is worth
50% of the course mark.</li>
 	
</ul>
<section class="prerequisites">
<h2>Requirements</h2>
None

</section>
<section class="course-staff">
  <h2>Course Staff</h2>
  <article class="teacher">
    <p>
      <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Antonio%20Cangiano%20pic.png" alt="" />
    </p>
    <h3>Antonio Cangiano</h3>
    <p>Antonio is a Software Developer and Technical Evangelist with IBM. A published author with Wrox and The Pragmatic Bookshelf, he enjoys web programming, cutting edge technologies, and technical marketing. He is the Chief Cognitive Computing Evangelist for Cognitive Class.</p>
  </article>
</section>

Build Your Own Chatbot

Learn how to build chatbots without having to write any code by leveraging Watson Assistant. Then deploy your chatbot to a real site in less than five minutes.

<div class="trix-content">
  <div>OpenShift is a hybrid cloud Kubernetes application platform. OpenShift orchestrates containers and provides additional tooling around the complete lifecycle of applications, from build and continuous integration/continuous delivery (CI/CD) to monitoring and logging.<br /><br />In this guided project you will perform operations on an OpenShift project by using both the oc command line interface (CLI), which is the OpenShift CLI, and the web console. An OpenShift project is a Kubernetes namespace with additional administrative functions. The oc CLI includes a copy of kubectl, which is the Kubernetes CLI. You can run kubectl commands with oc along with additional OpenShift-specific objects and functionality. Here you will execute basic commands to list pods in a namespace, get specific objects, and view the OpenShift project.<br /><br />Then you will open and explore the OpenShift web console, which is a useful and powerful feature. You will see how OpenShift provide project details in an intuitive manner. You will look at operators, different Kubernetes objects, and OpenShift-specific objects.<br /><br />OpenShift supports different personas. You will view both the Administrator perspective and the Developer perspective, which you will use to deploy an application. You will see how you can get a vivid visual representation of applications in the Topology view. You will create a source-to-image application and use the quick links in the Topology view to get visuals of the pod, the service, and other important parts of your application. Then you will view the logs to see a history of key completed steps.<br /><br />This guided project will give you a great start in using OpenShift as an extension of Kubernetes.<br /><br /><strong>A Look at the Project Ahead</strong><br />Once you have completed this project, you'll be able to:</div><ul>
<li>Use the oc CLI</li>
<li>Use the OpenShift web console</li>
<li>Build and deploy an application using s2i</li>
<li>Inspect a BuildConfig and an ImageStream</li>
</ul><div>
<br /><strong>What You’ll Need</strong><br />Just a web browser and an IBM Cloud account!<br /><br />Everything else is provided to you via the IBM Skills Network Labs environment. This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.<br /><br />Knowledge of command line interface (CLI) usage will be helpful.<br /><br /><strong>Your Instructors</strong><br />Upkar Lidder<br />Alex Parker</div>
</div>


Getting Started with OpenShift

In this guided project, you will discover the power of OpenShift by using the web console to build a container image, push the image to a registry, and create a deployment that references that image. Then you will expose the application to the Internet with a hostname. You will also learn about some basic oc commands.

<section class="about">
  <h2>About This Course</h2>
<p>
Data is one of the most critical assets of any business. Data needs a database to store and process data quickly. SQL is a language used for a database to query data.
  </p>
  <p>
In this introductory course, you'll learn the basics of the SQL language and the relational databases. You'll start by learning about the relational model and relational model concepts and constraints. By the end of this course, you will have learned and used the five basic SQL statements, some advanced SQL syntax, and join statements.
  </p>
  <p>
    This isn't your typical textbook introduction. You're not just learning through lectures. At the end of each module there are assignments, hands-on exercises, review questions, and also a final exam. Successfully completing this course earns you a certificate. So let's get started!
  </p>
</section>
  <h2>Course Syllabus</h2>
  <ul>
    <li>Module 1 -SQL and Relational Databases 101</li>
<ul>
  <li>Introduction to SQL and Relational Databases</li>
  <li>Information and Data Models</li>
  <li>Types of Relationships</li>
  <li>Mapping Entities to Tables</li>
  <li>Relational Model Concepts</li>
    </ul>
  <li>
    Module 2 - Relational Model Constraints and Data Objects</li>
    <ul>
      <li>Relational Model Constraints Introduction</li>
  <li>Relational Model Constraints Advanced</li>
    </ul>
    <li>
      Module 3 - Data Definition Language (DDL) and Data Manipulation Language (DML)
    </li>
    <ul>
      <li>CREATE TABLE statement</li>
      <li>INSERT statement</li>
      <li>SELECT statement</li>
      <li>UPDATE and DELETE statements</li>
    </ul>
    <li>Module 4 - Advanced SQL</li>
    <ul>
      <li>String Patterns, Ranges, and Sets</li>
      <li>Sorting Result Sets</li>
      <li>Grouping Result Sets</li>
    </ul>
    <li>Module 5 - Working with multiple tables</li>
    <ul>
      <li>Join Overview</li>
      <li>Inner Join</li>
      <li>Outer Join</li>
    </ul>
</ul><h2>Recommended skills prior to taking this course</h2>
  <ul>
    <li>None</li>
  </ul>
<section class="prerequisites">
  <h2>Requirements</h2>
  <p>None.</p>
</section>

<section class="course-staff">
  <h2>Course Staff</h2>
  <article class="teacher">
    <div class="teacher-image">
      <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Rav.png" style="margin: 0 20 px 0" alt="Rav Ahuja" />
    </div>

    <h3>Rav Ahuja</h3>
    <p>Rav Ahuja is a Senior Manager with IBM Canada Lab specializing in AI, Data Science and Big Data analytics. He is part of the Emerging Technologies team and is involved in incubating solutions for Data Scientists and Analytics Professionals. Rav is also a co-founder of Cognitive Class (CC) and leads the work on CC partnerships and offerings for businesses. He presents at events worldwide and has authored numerous papers, articles, books and courses on subjects in managing and analyzing data. Rav holds B. Eng. from McGill University and MBA from Richard Ivey School of Business, University of Western Ontario.</p>
  </article>
</section>

SQL and Relational Databases 101

Are you ready to dive into the world of SQL and relational databases? In just a few hours, you'll be able to discuss SQL basics and explain various aspects of the relational database model. Try our hands-on exercises as we guide your first steps into SQL and relational databases.

<section class="about">
<h2>About This Course</h2>

<p>From social media to news articles to machine logs, text data is everywhere. This class will teach you about Information Extraction: how to extract structured data from text in order to derive valuable insights. You will learn about information extraction applications in various domains, such as social media, healthcare analytics, and financial risk analysis. You will explore common text analytics tasks, including entity, relation, and event extraction, as well as sentiment analysis. Finally, you will dive into "Declarative Information Extraction", a powerful method for doing high-performance and high-quality text analytics, and gain hands-on experience writing your own extractors.</p>
</section>

<hr />
<h2>What will I get after passing this course?</h2>

<ul>
	<li>You will receive a completion certificate.</li>
	<li>You will receive the <a href="https://cognitiveclass.ai/badges/text-analytics-level-1"><strong>Text Analytics - Level 1</strong></a> badge.</li>
</ul>

<hr />
<h2>Course Syllabus</h2>

<ul>
	<li>
	<h4><strong>Module 1</strong> - Getting to Know Information Extraction</h4>
	</li>
	<li>
	<h4><strong>Module 2</strong> - Limitations in Information Extraction</h4>
	</li>
	<li>
	<h4><strong>Module 3</strong> - Getting to Know SystemT</h4>
	</li>
	<li>
	<h4><strong>Module 4</strong> - Information Extraction with AQL</h4>
	</li>
	<li>
	<h4><strong>Module 5</strong> - AQL Basics</h4>
	</li>
	<li>
	<h4><strong>Module 6</strong> - Advanced AQL</h4>
	</li>
	<li>
	<h4><strong>Module 7</strong> - Declarative Information Extraction and the SystemT Optimizer</h4>
	</li>
	<li>
	<h4><strong>Module 8</strong> - Best Practices</h4>
	</li>
</ul>

<hr />
<h2>General Information</h2>

<ul>
	
	<li>This course is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be taken as many times as you wish.</li>
</ul>

<hr />
<h2>Recommended skills prior to taking this course</h2>

<ul>
	<li>None</li>
</ul>

<section class="prerequisites"> </section>

<section class="course-staff">
<hr />
<h2>Course Staff</h2>

<article class="teacher">
<div class="teacher-image"><img alt="Yunyao Li" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Yunyao%20Li%20Pic.png" style="float: left; height: 85px; width: 85px" /></div>

<h3><a href="https://www.linkedin.com/in/yunyao-li-979a337" rel="noopener" target="_blank">Yunyao Li</a> </h3>

<p><a href="https://www.linkedin.com/in/yunyao-li-979a337" rel="noopener" target="_blank">Yunyao Li</a> is a Principle Research Staff Member and Senior Research Manager at  <a href="http://www.almaden.ibm.com/">IBM Almaden Research Center</a> where she manages the <a href="https://researcher.watson.ibm.com/researcher/view_group.php?id=9820">Scalable Knowledge Intelligence</a> department. She is also a Master Inventor and a member of IBM Academy of Technology.  Her expertise is in the interdisciplinary areas of natural language processing, databases, human-computer interaction, and information retrieval.  She is a founding member of <a href="https://researcher.watson.ibm.com/researcher/view_group.php?id=1264">SystemT</a>, a state-of-the-art NLP system currently powering multiple IBM products, and numerous projects. She received her PhD and master degrees from  <a href="http://www.umich.edu/">the University of Michigan</a> Ann Arbor and undergraduate degrees from <a href="http://www.tsinghua.edu.cn/eng/index.jsp">Tsinghua University</a>, Beijing, China.  You can read about Yunyao's inspiring story from small-town China to Silicon Valley <a href="http://www.cbronline.com/news/internet-of-things/cognitive-computing/small-town-china-silicon-valley-giant-amazing-story-one-ibm-researcher" rel="noopener" target="_blank">here</a>.  Follow her on Twitter <a href="http://yunyao_li/">@yunyao_li.</a></p>

<p> </p>

<div class="teacher-image"><img alt="Laura Chiticariu" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Laura%20Chiticariu%20pic.png" style="float: left; height: 85px; width: 85px" /></div>

<h3><a href="https://www.linkedin.com/in/laura-chiticariu" rel="noopener" target="_blank">Laura Chiticariu</a>  </h3>

<p><a href="https://www.linkedin.com/in/laura-chiticariu" rel="noopener" target="_blank">Laura Chiticariu</a> is the Chief Architect of Watson Knowledge and Language Foundation, with technical leadership responsibilities over Watson Natural Language Understanding, Watson Knowledge Studio and Watson Knowledge Graph. Laura is a core member of the <a href="http://researcher.watson.ibm.com/researcher/view_group.php?id=1264">SystemT</a>, R&amp;D team, and strongly believes in the notion of "Transparent NLP": leveraging machine learning techniques, while ensuring that the NLP system remains transparent - easy to comprehend, debug and enhance. She holds a Ph.D. in Computer Science, and has been teaching NLP across universities within and outside the U.S.</p>

<p> </p>

<div class="teacher-image"><img alt="Marina Danilevsky" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Marina%20Danilevsky%20Pic.png" style="float: left; height: 85px; width: 85px" /></div>

<h3><a href="https://www.linkedin.com/in/marina-danilevsky-a083b63" rel="noopener" target="_blank">Marina Danilevsky</a></h3>

<p><a href="https://www.linkedin.com/in/marina-danilevsky-a083b63" rel="noopener" target="_blank">Marina Danilevsky</a> is a Research Staff Member in the <a href="https://researcher.watson.ibm.com/researcher/view_group.php?id=9820">Scalable Knowledge Intelligence</a> group at <a href="http://www.research.ibm.com/labs/almaden/index.shtml">IBM Almaden Research Center</a> and a core member of the <a href="http://researcher.watson.ibm.com/researcher/view_group.php?id=1264">SystemT</a> R&amp;D team. She works in the intersection of data analytics, text mining, natural language processing, information networks, and human-computer interaction. She holds a Ph.D. in Computer Science from the <a href="http://www.cs.illinois.edu/">University of Illinois at Urbana-Champaign</a> and a B.S. in <a href="http://www.math.uchicago.edu/">Mathematics</a> from the <a href="http://www.uchicago.edu/">University of Chicago</a>.</p>

<p> </p>

<div class="teacher-image"><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Huaiyu%20Zhu%20pic.png" style="float: left; height: 90px; width: 85px" /></div>

<h3><a href="https://www.linkedin.com/in/huaiyu-zhu-075608134">Huaiyu Zhu</a>  </h3>

<p><a href="https://www.linkedin.com/in/huaiyu-zhu-075608134">Huaiyu Zhu</a> is a Research Staff Member in the <a href="https://researcher.watson.ibm.com/researcher/view_group.php?id=9820">Scalable Knowledge Intelligence</a> group at <a href="http://www.research.ibm.com/labs/almaden/index.shtml">IBM Almaden Research Center</a>. His main research focus is on text analytics, natural language processing, machine learning and statistical information processing.</p>

<p> </p>

<p> </p>

<div class="teacher-image"><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Atsushi%20Ono%20pic.png" style="float: left; height: 88px; width: 85px" /></div>
<h3><a href="https://www.linkedin.com/in/atsushi-ono-38a62a13/">Atsushi Ono</a>  </h3>


<p><a href="https://www.linkedin.com/in/atsushi-ono-38a62a13/">Atsushi Ono</a> is a software engineer at Tokyo Software &amp; Systems Development Lab (TSDL), IBM Japan. After several years of experience on business partner technical enablement missions, he has been working as a front-end developer on various projects, including contributing to the open source <a href="https://dojotoolkit.org/reference-guide/dojox/mobile.html">Dojo Mobile</a> project. He has worked on the development of <a href="https://www.ibm.com/cloud/watson-knowledge-studio">IBM Watson Knowledge Studio</a> since the project’s inception.</p>

<p> </p>

<div class="teacher-image"><img alt="Yuka Nomura" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Yuka%20Nomura%20pic.png" style="float: left; height: 85px; width: 85px" /></div>

<h3><a href="https://www.linkedin.com/in/yukanomura">Yuka Nomura</a>  </h3>

<p><a href="https://www.linkedin.com/in/yukanomura">Yuka Nomura</a> is a software engineer working on front-end development of <a href="https://www.ibm.com/watson/services/knowledge-studio/">IBM Watson Knowledge Studio</a> at Tokyo Software &amp; Systems Development Lab (TSDL), IBM Japan. She has contributed to user interface design and product development from her very first project start-up. She also specializes in robot application programming that runs on communication robots such as <a href="https://www.softbankrobotics.com/emea/en/pepper">Pepper</a>.</p>

<p> </p>

<div class="teacher-image"><img alt="Chikako Oyanagi" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Chikako%20Oyanagi%20Pic.png" style="float: left; height: 85px; width: 85px" /></div>

<h3>Chikako Oyanagi</h3>

<p>Chikako Oyanagi is a front-end software developer of <a href="https://www.ibm.com/watson/services/knowledge-studio/">IBM Watson Knowledge Studio</a> at Tokyo Software &amp; Systems Development Lab (TSDL), IBM Japan.</p>

<p> </p>

<p> </p>

<div class="teacher-image"><img alt="Teruki Tauchi" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Teruki%20Tauchi%20Pic.png" style="float: left; height: 84px; width: 85px" /></div>

<h3>Teruki Tauchi</h3>

<p>Teruki Tauchi is a front-end software developer of <a href="https://www.ibm.com/cloud/watson-knowledge-studio">IBM Watson Knowledge Studio</a> at Tokyo Software &amp; Systems Development Lab (TSDL), IBM Japan. He joined IBM after obtaining a Master of Engineering degree in Computer Science from University College London in 2015.</p>

<p> </p>
</article>
</section>

Text Analytics 101

From social media to news articles to machine logs, text data is everywhere. This class will teach you about Information Extraction: how to extract structured data from text in order to derive valuable insights.

<div class="trix-content">
  <div>GitHub is a popular online hosting service for repositories, which are copies of an entire project, including its revision history. All files in a repository reside in branches, or separate lines of development. Teams designate one branch as the main branch, the one that stores the finished, deployable code. Users create separate branches to work on new features or edit existing code, then integrate these changes into the main branch.<br /><br />
</div><div>In this guided project, you will set up a GitHub account and create a repository on your GitHub page. Then you will explore ways to modify your repository, including editing an existing file, creating a new file, and uploading a file from your computer. You will discover how to commit, or save, each change.<br /><br />
</div><div>You will then discover how to use GitHub’s branching system to update a repository. You will add a new branch to your repository. This branch starts as an exact copy of the repository’s main branch. You will add a file to the new branch, then commit this change to the branch. Next, you will issue a pull request (PR), a mechanism by which you notify others that your changes are ready for review. After reviewing these changes, you will merge them into your main branch.<br /><br />
</div><div>Completing this guided project will prepare you to create and update repositories on GitHub and to explore this software’s more complex collaboration features.<br /><br /><strong>A Look at the Project Ahead<br /></strong>Once you have completed this project, you'll be able to:</div><ul>
<li>Describe GitHub</li>
<li>Create a GitHub account</li>
<li>Add a repository to GitHub</li>
<li>Create and edit a file in a GitHub repository</li>
<li>Upload and commit a file to a GitHub repository</li>
<li>Create a branch of a GitHub repository</li>
<li>Commit changes to a child branch</li>
<li>Open a pull request</li>
<li>Merge a pull request into the main branch</li>
</ul><div>
<br /><strong>What You’ll Need</strong>
</div><div>Just a web browser and a personal email account! Everything else is provided to you on GitHub.<br /><br /><strong>Your Instructors</strong>
</div><ul>
<li>Romeo Kienzler</li>
<li>Malika Singla </li>
</ul>
</div>


Getting Started with GitHub and Creating Branches

In this guided project, you will set up a GitHub account, then create and modify a repository on your GitHub page. You will also create and edit a new branch of this repository, then merge the changes into your main branch.

<div class="trix-content">
  <div>In this guided project, you will explore basic cloud container operations and discover how containers support modern cloud application development. A container is a software package that contains application code including libraries and dependencies. Containers are very portable, running on desktops, on-premises servers, or in the cloud.<br /><br />You will work with Docker images, which are files with code that you execute to build containers, and run an image as a container on IBM Cloud. Queries will provide you with information about the instance. You will export your namespace and use it to execute several commands.<br /><br />This guided project will prepare you to use containers with microservices architecture as a foundation for DevOps cloud application development.<br /><br /><strong>A Look at the Project Ahead</strong><br />Once you have completed this project, you'll be able to:</div><ul>
<li>Pull an image from Docker Hub</li>
<li>Run an image as a container using docker</li>
<li>Build an image using a Dockerfile</li>
<li>Push an image to IBM Cloud Container Registry</li>
<li>Use the Docker command line interface (CLI)</li>
</ul><div>
<br /><strong>What You’ll Need</strong><br />Just a web browser and an IBM Cloud account!<br /><br />Everything else is provided to you via the IBM Skills Network Labs environment. This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.<br /><br />Knowledge of command line interface (CLI) usage will be helpful.<br /><br /><strong>Your Instructor</strong><br />Upkar Lidder</div>
</div>


Working with Containers, Docker, and the IBM Cloud Container Registry

In this guided project, you will run a Docker image as a container and explore the exciting world of cloud container applications.

<section class="about">
<h2>ABOUT THIS COURSE</h2>

<p>Containers and Cloud Native are the most significant invention in IT since the introduction of virtualization. Everyone from a small startup to a large multinational corporation is transitioning to this technology and they are looking for people who have the skills.</p>

<p>After completing this course, you will be able to build applications the Cloud Native way and be able to deploy your applications at a scale that will make Google envious.</p>

<p>This course introduces you to containers and explains how containers differ from virtual machines.  It also covers the importance of containers in cloud computing as well as the emerging ecosystem of related technologies such as Docker, Kubernetes, OpenShift, and Istio.</p>

<p>This course is of interest to anyone who wants to be a cloud practitioner and use container skills as developers, architects, system engineers, network specialists and many other roles. The material also serves the needs of those who perform the tasks of advising, building, moving and managing cloud solutions.</p>

<hr />
<h2>LEARNING OBJECTIVES</h2>

<p>After completing this course you will be able to:</p>

<ul>
	<li>Understand the benefits of containers</li>
	<li>Build and run a container image</li>
	<li>Understand Kubernetes architecture</li>
	<li>Write a YAML deployment file</li>
	<li>Expose deployment as a service</li>
	<li>Manage applications with Kubernetes</li>
	<li>Use ReplicaSets, auto-scaling, rolling updates and service bindings</li>
	<li>Understand the benefits of OpenShift, Istio and other important tools</li>
</ul>

<hr />
<h2>COURSE SYLLABUS</h2>

<p><strong>Module 1</strong></p>

<ul>
	<li>Introduction to containers</li>
	<li>Introduction to Docker</li>
	<li>Building container images</li>
	<li>Using container registries</li>
	<li>Running containers</li>
</ul>

<p><strong>Module 2</strong></p>

<ul>
	<li>Understanding container orchestration</li>
	<li>Understanding Kubernetes architecture</li>
	<li>Introduction to Kubernetes objects</li>
	<li>Using basic Kubernetes objects</li>
	<li>Using the kubectl command</li>
	<li>Leveraging Kubernetes</li>
</ul>

<p><strong>Module 3</strong></p>

<ul>
	<li>Using ReplicaSets</li>
	<li>Using autoscaling</li>
	<li>Understanding rolling updates</li>
	<li>Understanding ConfigMaps and secrets</li>
	<li>Using service bindings</li>
</ul>

<p><strong>Module 4</strong> - The Kubernetes ecosystem</p>

<ul>
	<li>The Kubernetes Ecosystem</li>
	<li>Introduction to Red Hat OpenShift</li>
	<li>Red Hat OpenShift and Kubernetes</li>
	<li>Builds</li>
	<li>Operators</li>
	<li>Istio</li>
</ul>

<p><strong>Final exam</strong></p>

<hr />
<h2>REQUIREMENTS</h2>

<p>There are no hard requirements needed to be able to take the course, however a foundation level understanding of Cloud Computing will help, such as that obtained from the <a href="https://cognitiveclass.ai/courses/introduction-to-cloud">Introduction to Cloud (CC0101EN)</a> and <a href="https://cognitiveclass.ai/courses/ibm-cloud-essentials">IBM Cloud Essentials (CC0103EN)</a>.</p>

<hr />
<h2>COURSE INSTRUCTORS</h2>

<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Alex%20Parker%20pic.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<p><strong>Alex Parker</strong> is an offering manager for IBM Cloud Kubernetes Service, Red Hat OpenShift on IBM Cloud, and IBM Cloud Container Registry, with many years of experience as a software engineer. He creates articles, tutorials and education materials to equip technical professionals to become successful with cloud solutions. Alex is known for his ability to drive technical teams toward excellence. He currently resides in North Carolina, USA.</p>

<p> </p>

<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Upkar%20Lidder.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<p><strong>Upkar Lidder</strong> is a senior software engineer with 10+ years experience in IT development including team management, functional and technical leadership roles with a deep experience in full-stack technology. Currently focused on Machine Learning and Artificial Intelligence, he can be seen speaking at various conferences and participating in local tech groups and meetups. Upkar went to graduate school in Canada and currently resides in the United States.</p>

<p> <br />
<img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/heathernelson-300x300.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<p><strong>Heather Nelson</strong> is a senior program manager for IBM Garage Practitioner Activation with many years of experience designing and developing education that rapidly accelerates the growth of new skills required for emerging technology. She is well known for her ability to identify and overcome learning roadblocks and implementing techniques for effective learning. She currently resides in the greater Boston area in the United States.</p>

<hr />
<h2>FREQUENTLY ASKED QUESTIONS</h2>

<article>
<h3>What web browser should I use?</h3>

<p>The Open edX platform works best with current versions of Chrome, Firefox, or Safari, or with Internet Explorer version 9 and later.</p>

<p>See our <a href="http://edx.readthedocs.org/projects/open-edx-learner-guide/en/latest/front_matter/browsers.html">list of supported browsers</a> for the most current information.</p>
</article>
</section>

Introduction to Containers, Kubernetes, and OpenShift V2

This course introduces the core concepts of Containers and Kubernetes and explains how containers differ from virtual machines. It also covers the importance of containers in cloud computing as well as the emerging ecosystem of related technologies such as Docker, Kubernetes, OpenShift, Istio and Knative.

<section class="about">
<h2>About this course</h2>

<p>Containers are a standard way to package an application and all its dependencies so that the application can be moved between environments and run without changes. Containers work by isolating the application inside the container so that everything outside the container can be standardized.</p>

<p>Docker has provided a set of tools to simplify the use of containers. This has led to massive adoption of containers by developers and operators. In this course, you will focus mostly on Docker container technologies.</p>

<p>In this course, you'll follow a series of hands-on labs that demonstrate how to use containers for your applications. You'll start with the basics: creating and running your first Docker containers. By the end of the course, you'll get a brief introduction to running containers in production with the and how to solve problems of advanced orchestration such as high availability, service discovery, and reconciliation.</p>
</section>

<section>
<h2>Course syllabus</h2>

<p><strong>Set up Docker</strong></p>

<p><strong>Lab 1: Run your first container</strong></p>

<ul>
	<li>What are containers?</li>
	<li>1. Run a container</li>
	<li>2. Run multiple containers</li>
	<li>3. Remove the containers</li>
</ul>

<p><strong>Lab 2: Add CI/CD value with Docker images</strong></p>

<ul>
	<li>Docker images</li>
	<li>1. Create a Python app (without using Docker)</li>
	<li>2. Create and build the Docker image</li>
	<li>3. Run the Docker image</li>
	<li>4. Push to a central registry</li>
	<li>5. Deploy a change</li>
	<li>6. Understand image layers</li>
	<li>7. Remove containers</li>
</ul>

<p><strong>Lab 3. Orchestrate applications with Docker Swarm</strong></p>

<ul>
	<li>Container orchestration overview</li>
	<li>1. Create your first swarm</li>
	<li>2. Deploy your first service</li>
	<li>3. Scale your service</li>
	<li>4. Apply rolling updates</li>
	<li>5. Reconcile problems with containers</li>
	<li>6. Determine how many nodes you need</li>
</ul>

<ul>
	<li>Final exam</li>
</ul>
</section>

<section class="prerequisites">
<h2>Requirements</h2>

<p>You don't need to know anything about containers to take this course, and no extensive programming experience is required. However, you must know how to run command-line tools from your workstation.</p>

<p>If you don't have Docker already, you need install Docker Community Edition (CE) or use the use <a href="http://labs.play-with-docker.com/" target="_blank">Play-with-Docker</a> for all the labs in this course. Docker installations are available from <a href="https://store.docker.com/" target="_blank">store.docker.com</a> for your Mac, Windows, or Linux machine.</p>

<p>This course shows you how to install Docker.</p>

<p>You must also have a Docker Hub account to complete Lab 3. If you don't already have one, go to <a href="http://labs.play-with-docker.com/" target="_blank">Play-with-Docker</a> and follow the prompts to create an account.</p>
</section>

<section class="course-staff">
<h2>Course instructor</h2>

<article class="teacher">
<div class="teacher-image"><img alt="John Zaccone" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/John%20Zaccone%20pic.png" /></div>

<h3> </h3>

<h3> </h3>

<h3> </h3>

<h3> </h3>

<h3><strong>John Zaccone</strong></h3>
Developer Advocate, IBM Digital Business Group

<p> </p>
</article>
</section>

<section class="course-staff">
<h2>Course staff</h2>

<h3><strong>Michelle Carey</strong></h3>
IBM Courseware Developer

<h3><strong>Cesar Canuta</strong></h3>

<p>IBM Courseware Developer</p>
</section>


Docker Essentials: A Developer Introduction

Learn how to use containers for your applications. Create and run your first Docker container. Then, learn how to run containers in production and solve problems of orchestration such as high availability, service discovery, and reconciliation.

<section class="about">
  <h2>About This Course</h2>

Learn about privacy from 5 prominent cases of <strong>data breaches</strong> from 2012 to 2015, which highlight vulnerabilities in typical organizations.
<ul>
 	<li>Get an outline of data privacy laws.</li>
 	<li>Walk away with some guiding principles for how to stay out of trouble.</li>
 	<li>Knowing how to hack a colleague's password will raise some ethical questions. Justify your decisions in the course exam.</li>
</ul>
  
</section>
  
  <h2>Course Syllabus</h2>
<ul>
 	<li><strong>Module 1:</strong>
<ul>
 	<li><span style="font-size: 1em;line-height: 1.6em">An overview of privacy laws in Canada </span></li>
 	<li><span style="line-height: 1.6"><span style="line-height: 25.6px">Case Study:</span> </span><span style="font-size: 1em;line-height: 1.6em">Student loans data breach (Canada) -</span><strong style="line-height: 1.5"> </strong><span style="font-size: 1em;line-height: 1.6em">A breach involving the personal information of about more than half a million clients of Human Resources and Skills Development Canada (HRSDC) and 250 departmental employees</span></li>
</ul>
<strong>Module 2:</strong>
<ul>
 	<li><span style="font-size: 1em;line-height: 1.6em">An overview of the Personal Information Protection and Electronic Documents Act (PIPEDA)</span></li>
 	<li><span style="line-height: 1.6">Case Study: </span><span style="font-size: 1em;line-height: 1.6">Target Corp. (USA) - A data breach involving information on 40 million payment cards (i.e., credit, debit, and ATM cards) and personally identifiable information (PII) on 70 million customers</span></li>
</ul>
<strong>Module 3</strong>
<ul>
 	<li>Dr. Ann Cavoukian's 7 Foundational Principles of 'Privacy by Design'</li>
 	<li><span style="font-size: 1em;line-height: 1.6"><span style="line-height: 25.6px">Case Study:</span> </span><span style="font-size: 1em;line-height: 1.6em">Think W3 (UK) - A d</span><span style="font-size: 1em;line-height: 1.6em">ata breach involving 1.2 million credit and debit card details</span></li>
 	<li><span style="font-size: 1em;line-height: 1.6">Case Study: </span><span style="font-size: 1em;line-height: 1.6">Doritex Corp. (USA) - A d</span><span style="font-size: 1em;line-height: 1.6">ata breach exposed the social security numbers of over 500 job applicants</span></li>
</ul>
<strong>Module 4  </strong>
<ul>
 	<li>Data breaches and passwords</li>
 	<li><span style="font-size: 1em;line-height: 25.6px">Case Study: </span><span style="font-size: 1em;line-height: 1.6em">Home Depot (USA)</span><span style="font-size: 1em;line-height: 1.6em"> - A data breach estimated to have put payment card information at risk for approximately 56 million unique payment cards</span></li>
 	<li>Class Participation Questionnaire</li>
</ul>

</li>
</ul>
  
	<h2>Recommended skills prior to taking this course</h2>
  <ul>
    <li>None</li>
  </ul>
  <h2>Grading scheme</h2>
  <ul>
    <li>The minimum passing mark for the course is 60%, where the review questions are worth 40% and the final exam is worth 60% of the course mark.</li>
    <li>You have 1 attempt to take the exam with multiple attempts per question.</li>
  </ul>


<section class="prerequisites">
  <h2>Requirements</h2>
  <p>None.</p>
</section>

<section class="course-staff">
  <h2>Course Staff</h2>
  <article class="teacher">
    <div class="teacher-image">
      <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Shingai.png" style="margin: 0 20 px 0" alt="Shingai Manjengwa" />
    </div>

    <h3>Shingai Manjengwa</h3>
    <p>Shingai Manjengwa (@Tjido) is the Director of Insights and Analytics at Fireside Analytics Inc. An NYU Stern alum, she graduated from the Stern Business Analytics Masters program in 2014 and founded Fireside Analytics the following year. Fireside Analytics is a data analytics consulting company that makes data analytics and data science skills accessible to private sector companies, non-profits and education institutions. Fireside Analytics works with clients to build their data science capabilities and train their staff and stakeholders using customized case studies. </p>
  </article>
</section>


Data Privacy Fundamentals

Learn about the fundamentals of Data Privacy.

<div class="trix-content">
  <div>Have you ever wanted to create an image detector that would tell you whether or not a picture is of a certain object? Look no further because we have you covered!<br /><br />
</div><div>In this project, we will be utilising  simple learning rules to "teach" a neural network how to recognize hotdogs an image and solve the big question: hotdog or not hotdog?<br /><br />
</div><div>Let's find out!<br /><br />
</div><h1><strong>A Look At the Project Ahead</strong></h1><div>Once you have completed, this project, you’ll be able to:</div><ul>
<li>Create Python functions with libraries to preview, load and train your model to recognize hotdogs with PyTorch</li>
<li>Transform a dataset of images to prepare it for training</li>
<li>Train a state of the art image classifier by using transfer learning: training a model to solve one problem and applying that training to a related problem</li>
</ul><h1>What You'll Need</h1><div>Before starting this lab, it'll be helpful to be familiar with the following:</div><ul>
<li>Basic Python knowledge</li>
<li>Basic knowledge about image classification and neural networks, provided in optional readings</li>
</ul><div>If you're interested in learning more about image classification and neural networks you can take a look at the following courses: Computer Vision and Image Processing Fundamentals course on <a href="https://www.coursera.org/learn/introduction-computer-vision-watson-opencv?utm_medium=Exinfluencer&amp;utm_source=Exinfluencer&amp;utm_content=000026UJ&amp;utm_term=10006555&amp;utm_id=NA-SkillsNetwork-Channel-SkillsNetworkQuickLabsbuildahotdognothotdogclassifierguidedproject28954781-2021-01-01">Coursera</a> or <a href="https://www.edx.org/course/computer-vision-and-image-processing-fundamentals">edX</a>.<br /><br />
</div><h1>Instructor</h1><div>Kathy An, IBM<br />Weiqing Wang, IBM<br /><br />
</div><h1>Other Contributors</h1><div>Joseph Santarcangelo, IBM</div>
</div>


Train a Hotdog Image Recognition Model with Python

We all know that machines can do a lot these days, including recognizing whether or not an image has a certain object in it. But did you know that you, too, can train a model to do just that? In this guided project, you’ll learn how to train a model in Python with PyTorch, a machine learning library, to detect if a picture has a hotdog in it. This process can be repeated with any object, whether it is a bird, a plane or even Superman!

<div class="trix-content">
  <h1><strong>About</strong></h1><div>
<strong> </strong>In this laboratory, we will explore how land use types can be classified based on aerial and satellite photographs of the earth's surface. We will learn to build our own photo masks based on ready-made masks and display them on the screen, we will learn how to upload photos, transform pixels and colors into a data set. Then we will learn how to build a classifier and predict land use masks based on it.<br /><br />
</div><div>In this lab, we will learn the basic methods of images transformation classification. The laboratory consists of four stages:</div><ul>
<li>Download and visualization of images</li>
<li>DataSet creation</li>
<li>Classification model creation</li>
<li>Create your own mask of land use</li>
</ul><div><br /></div><h1><strong>After completing this lab, you will be able to:</strong></h1><ul>
<li>Download and transform images.</li>
<li>Create a DataSet of the colors of images.</li>
<li>Build a classification model.</li>
<li>Build land use masks based on a classifier.</li>
</ul><div> <br /><br />
</div>
</div>


Data Science in Agriculture. Land use classification

In this lab, we will learn the basic methods of images transformation classification.

<section class="about"> <h2>About This Course</h2> <p> Many Big Data use cases have one thing in common - the use of Apache Kafka somewhere in the mix. Whether the distributed, partitioned, replicated commit log service is being used for messaging, website activity tracking, stream processing or more, there's no denying it is a hot technology. In this course, you will learn how Kafka is used in the real world and its architecture and components. You will quickly get up and running, producing and consuming messages using both the command line tools and the Java APIs. You also will get hands on experience connecting Kafka to Spark, and working with Kafka Connect.
  </p> </section><section class="course syllabus">
<h2>Course Syllabus</h2> <ul><h3>Lesson 1 - Introduction to Apache Kafka</h3><p><ul>

<li>What Kafka is and why it was created</li>
<li>The Kafka Architecture</li>
<li>The main components of Kafka</li>
<li>Some of the use cases for Kafka</li></ul></p></ul>

<h3>Lesson 2 - Kafka Command Line</h3><p><ul>

<li>The contents of Kafka's /bin directory</li>
<li>How to start and stop Kafka</li>
<li>How to create new topics</li>
<li>How to use Kafka command line tools to produce and consume messages</li></ul></p>

<h3>Lesson 3 - Kafka Producer Java API</h3><p><ul>

<li>The Kafka producer client</li>
<li>Some of the KafkaProducer configuration settings and what they do</li>
<li>How to create a Kafka producer using the Java API and send messages both synchronously and asynchronously</li></ul></p>
  
<h3>Lesson 4 - Kafka Consumer Java API</h3><ul>

<li>The Kafka consumer client</li>
<li>Some of the KafkaConsumer configuration settings and what they do</li>
<li>How to create a Kafka consumer using the Java API</li></ul>

  <h3>Lesson 5 - Kafka Connect and Spark Streaming</h3><ul><p>

<li>Kafka Connect and how to use a pre-built connector</li>
<li>Some of the components of Kafka Connect</li>
<li>How to use Kafka and Spark Streaming together</li> </p></ul> </section><section class="prerequisites">
<h2>Prerequisites</h2> <p>


<ul><li>Have taken the Hadoop 101 course.</li>
<li>Recommended skills prior to taking this course</li>
<li>Basic understanding of Apache Hadoop and Big Data.</li>
<li>Basic Linux Operating System knowledge.</li>
<li>Basic understanding of the Scala, Python, R, or Java programming languages.</li></ul>

</p> </section>

Simplifying data pipelines with Apache Kafka

When you hear the terms, producer, consumer, topic category, broker, and cluster used together to describe a messaging system, something is brewing in the pipelines. Get connected and learn what that is, and what it means!

<div class="trix-content">
  <div><br /></div><h1>Learn by Doing</h1><div>A guided project is a hands-on tutorial designed to help you learn a particular technology by doing a real project. It includes step-by-step instructions with explanations, examples and exercises that can be followed and practiced in a lab environment.<br /><br />
</div><div>Hands-on skills are highly sought out by employers when determining job readiness. </div><div>
<br />Guided projects are interactive, on-demand and will equip you with practical abilities that can be applied on the job!<br /><br />
</div><h1>A Look at the Project Ahead</h1><div>Once you have completed this project, you'll be able to:</div><ul>
<li>Create lightweight, standardized apps using Docker containers</li>
<li>Use the IBM Cloud command-line interface</li>
<li>Upload your containers to IBM Cloud Container Registry</li>
<li>Deploy your containers on IBM Code Engine</li>
</ul><h1>What You’ll Need </h1><div>To do this project, you will need the following:</div><ul>
<li>An IBM Cloud account (<a href="https://cloud.ibm.com/registration">sign up for a free account</a>)</li>
<li>A very basic knowledge of Python</li>
</ul><h1>Your Instructor</h1><div>Arnav Shah, IBM</div>
</div>


Deploy a Web Server Using Python and IBM Code Engine

In this guided project, you'll learn how to take any project you've built, containerize it, and easily deploy it to a Code Engine server. 

<section class="about">
<h2>About This Course</h2>

<p>Get hands-on experience and learn how to convert speech to text, analyze tone, translate text into different languages, send tweets through Twitter, add audio and video capabilities, and set up a chat bot using the IBM Watson Assistant (formerly Conversation) service and Facebook Messenger. And you don't even need any programming experience!</p>

<p>Node-RED, which is built on Node.js, is a browser-based flow editor that makes it easy to wire together devices, APIs, and online services by simply dragging and dropping nodes on a palette. You then connect, or wire, the nodes to create flows that can be then deployed to the lightweight Node.js runtime with a single click.</p>
</section>

<section class="prerequisites">
<hr />
<h2>Requirements</h2>


<p>No programming experience is required for this course. You will work with basic HTML and JavaScript, but the course labs will provide that code.</p>

<p>You'll need the following accounts:</p>

<ul>
	<li><a href="https://developer.ibm.com/sso/bmregistration?lang=en_US&amp;ca=dw-_-nodered-_-watson-_-course">IBM Cloud account</a></li>
	<li>Twitter account</li>
	<li>Facebook account</li>
	<li>A browser that supports <a href="https://webrtc.org">WebRTC</a></li>
</ul>

<p>This course includes four labs. Each lab has a short video introduction, the lab instructions, and lastly a solution video. Watch the solution videos if you have a problem completing the lab or want to check your work. The solution video shows the steps required to complete the lab.</p>

<p><strong>Important:</strong> If you have an IBM Cloud Lite account, you will only be able to create one Cloud Foundry application. This is enough to complete this course, but if you are already using an application, then you might need to upgrade your account</p>

<h3>Related links</h3>

<p><a href="https://nodered.org/docs/">Node-RED documentation</a></p>

<p><a href="https://console.bluemix.net/docs/starters/Node-RED/nodered.html#nodered">Node-RED starter application information</a> (IBM Cloud docs)</p>
</section>

<section class="course-staff">
<hr />
<h2>Course Staff</h2>

<article class="teacher">
<div class="teacher-image"><img alt="Course Staff Image #1" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Soheel%20Chughtai%20pic.png" style="height: 150px; width: 150px; border-radius: 10px" /></div>

<h3>Soheel Chughtai</h3>

<p>Early Program Manager, Watson Developer Cloud<br />
IBM Systems</p>
</article>

<article class="teacher">
<div class="teacher-image"><img alt="Course Staff Image #2" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Emma%20Dawson%20pic.png" style="height: 150px; width: 150px; border-radius: 10px" /></div>

<h3>Emma Dawson</h3>

<p>Early Program Manager<br />
IBM Systems</p>
</article>
</section>

Node-RED: basics to bots

Create cognitive web apps with Node-RED to translate text, analyze tone, and send tweets. Build a chat bot with Watson Conversation and Facebook Messenger.

<section>
<hr />
<h2>ABOUT THIS COURSE</h2>

<p>In this course, you'll learn about Data Science tools like Jupyter Notebooks, RStudio IDE, and Watson Studio. You will learn what each tool is used for, what programming languages they can execute, their features and limitations and how data scientists use these tools today.</p>

<p>With the tools hosted in the cloud, you will be able to test each tool and follow instructions to run simple code in Python or R. To complete the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio on Cloud and demonstrate your proficiency in preparing a notebook, writing Markdown, and sharing your work with your peers.</p>

<p>This hands-on course will get you up and running with some of the latest and greatest data science tools.</p>

<p><strong>You will Learn:</strong></p>

<ul>
	<li>How to use various data science and data visualization tools hosted on Skills Network Labs</li>
	<li>How to use Jupyter Notebooks including its features and why it's so popular</li>
	<li>Popular tools used by R Programmers including RStudio IDE</li>
	<li>IBM Watson Studio including its features and capabilities</li>
	<li>How to create and share a Jupyter Notebook</li>
</ul>
</section>

<hr />
<h2>COURSE SYLLABUS</h2>
<ul>
<p><strong>Module 0 - Welcome and Course Introduction</strong></p>

<p><strong>Module 1 - Languages of Data Science</strong></p>

<p><strong>Module 2 - Data Science Tools</strong></p>

<p><strong>Module 3 - Packages, APIs, Data Sets and Models</strong></p>

<p><strong>Module 4 - GitHub</strong></p>

<p><strong>Module 5 - Jupyter Notebooks and JupyterLab</strong></p>

<p><strong>Module 6 - RStudio IDE</strong></p>

<p><strong>Module 7 - Watson Studio</strong></p>
</ul>
<hr />
<h2>GENERAL INFORMATION</h2>

<ul>
	
	<li>This course is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be audited as many times as you wish.</li>
</ul>

<hr />
<h2>RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE</h2>

<ul>
	<li>None</li>
</ul>

<hr />
<h2>REQUIREMENTS</h2>

<section>
<ul>
	<li>None</li>
</ul>

<hr />
<h2>COURSE STAFF</h2>
</section>

<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Romeo%20K.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<h3>Romeo Kinsler</h3>

<section>
<p><strong>Romeo Kienzler</strong> holds a M. Sc. (ETH) in Information Systems, Bioinformatics &amp; Applied Statistics (Swiss Federal Institute of Technology). He has nearly two decades of experience in Software Enineering, Database Administration and Information Integration. Since 2012 he works as a Data Scientist for IBM. He published several works in the field with international publishers and on conferences. His current research focus is on massive parallel data processing architectures. Romeo also contributes to various open source projects.</p>

<p> </p>
</section>

<section> </section>

<section>
<article>
<p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Svetlana.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>

<h3>Svetlana Levitan</h3>

<section>
<p><strong>Svetlana Levitan</strong> is a Senior Developer Advocate with IBM Center for Open Data and AI Technologies, Svetlana has been a software engineer and technical lead for SPSS for many years. She works on open standards for machine learning model deployment PMML and ONNX. She holds PhD in Applied Math and MS in CS from University of Maryland, College Park. Svetlana loves to learn new technologies, share her expertise, and to encourage women in STEM.</p>

<p> </p>
</section>

<section> </section>
</article>

<article><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/maureen%20McElaney%20pic.png" style="width: 150px; height: 150px; border-radius: 10px" />
<h3>Maureen McElaney</h3>

<p><strong>Maureen McElaney</strong> is a Developer Advocate at IBM Center of Open Source Data and Ai Technologies. She is on the LF AI Trusted AI Committee underneath the Linux Foundation. She is an organizer for Women in Machine Learning and Data Science and on the board of the Vermont Technology Alliance. She is an experienced community builder and is passionate about building diversity (of all kinds) in tech through education, mentorship, and advocacy.</p>
</article>
</section>

Data Science Tools

Learn about the most popular data science tools, including how to use them and what their features are.

<div class="trix-content">
  <h1>About</h1><div>This lab is dedicated to downloading, pre-preparing and making statistical analysis of Economic accounts for agriculture and to creating interactive maps showing the dynamics of the prices.<br /><br />
</div><div>In this lab, we will learn how to download data, perform basic statistical analysis, visualize and display it on dynamic maps. All data is obtained from Eurostat DataBase (<a href="https://ec.europa.eu/eurostat/data/database)">https://ec.europa.eu/eurostat/data/database)</a>. This lab consist of the following steps:<br /><br />
</div><ul>
<li>Data download - download and display data from a file</li>
<li>Data preparation - preliminary analysis of data structure, change of data types and table structure</li>
<li>Statistical analysis - basic statistical analysis</li>
<li>Data visualization - output of various data on graphs</li>
<li>Build a trend line - the basic method of building a trend line and a forecast based on it</li>
<li>Interactive maps - construction of interactive maps which show the data change over a period of time.</li>
</ul><div><br /></div><h1><strong>After completing this lab, you will be able to:</strong></h1><ul>
<li>Download a DataSet from *.csv files</li>
<li>Transform the table</li>
<li>Visualize data with pandas and seaborn</li>
<li>Expect<ul>
<li>Minimum and maximum value</li>
<li>Average</li>
<li>Quarters</li>
<li>Pivot tables</li>
<li>Build Interactive Maps</li>
</ul>
</li>
</ul><div><br /></div>
</div>


Data Science in Agriculture. Basic statistical analysis and Geo Visualisation

In this lab, we will learn how to download data, perform basic statistical analysis, visualize and display it on dynamic maps.

<section class="about">
  <h2>About This Course</h2>
  <p>Welcome to this machine learning course on Dimensionality Reduction. Dimensionality Reduction is a category of unsupervised machine learning techniques used to reduce the number of features in a dataset. Dimension reduction can also be used to group similar variables together.</p>
<p>
In this course, you will learn the theory behind dimension reduction, and get some hands-on practice using Principal Components Analysis (PCA) and Exploratory Factor Analysis (EFA) on survey data.</p>

<p>The code used in this course is prepared for you in R.</p>
</section>

<section class="prerequisites">
  <h2>Requirements</h2>
  <p>Basic knowledge of operating systems (UNIX/Linux).</p>
</section>

<h3>Course Syllabus</h3>
  <ul>
    <li>Introduction to Dimension Reduction</li>
    <li>Principal Component Analysis</li>
    <li>Exploratory Factor Analysis</li>
 </ul>

<section class="course-staff">
  <h2>Course Staff</h2>
  <article class="teacher">
    <div class="teacher-image">
      <img src="/static/Konstantin-Tskhay-300x300.jpg" style="margin: 0 20 px 0" alt="Konstantin" />
    </div>

    <p><a href="https://ca.linkedin.com/in/konstantin-tskhay-562556b2">Konstantin Tskhay</a></p>
    Konstantin is an analytic thinker and a Graduate Student Research Scientist (Ph. D.) at the University of Toronto with more than five years of quantitative and qualitative research experience in organizational behavior, impression formation, and leadership. Konstantin has been incredibly successful in academia, publishing a number of first-author papers, presenting at international conferences, and receiving several prestigious awards, but has decided to make a move into the private sector, applying his knowledge and skills within Deloitte’s Human Capital Consulting Practice, starting June 2016.

Konstantin holds a Master of Arts degree in Psychology from the University of Toronto and a Bachelor of Arts degree in Psychology from the University of California, Riverside. He is expected to receive the Doctor of Philosophy degree in Psychology with a focus on Charisma and Leadership from the University of Toronto on March 23rd, 2016.
    
  </article>

 
</section>

<section class="faq">
  <section class="responses">
    <h2>Frequently Asked Questions</h2>
    <article class="response">
      <h3>What web browser should I use?</h3>
      <p>The Open edX platform works best with current versions of Chrome, Firefox or Safari, or with Internet Explorer version 9 and above.</p>
      <p>See our <a href="http://edx.readthedocs.org/projects/docstrings/en/latest/front_matter/browsers.html">list of supported browsers</a> for the most up-to-date information.</p>
    </article>
  </section>
</section>

Machine Learning - Dimensionality Reduction

Welcome to this machine learning course on Dimensionality Reduction. Dimensionality Reduction is a category of unsupervised machine learning techniques used to reduce the number of features in a dataset. Dimension reduction can also be used to group similar variables together.  In this course, you will learn the theory behind dimension reduction, and get some hands-on practice using Principal Components Analysis (PCA) and Exploratory Factor Analysis (EFA) on survey data.  The code used in this course is prepared for you in R.

<section class="about">
<h2>About This Course</h2>

<p>Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.</p>

<p>You can use accelerated hardware such as Google’s Tensor Processing Unit (TPU) or Nvidia GPU to accelerate your convolutional neural network computations time on the Cloud. These chips are specifically designed to support the training of neural networks, as well as the use of trained networks (inference). Accelerated hardware has recently been proven to significantly reduce training time.</p>

<p>But the problem is that your data might be sensitiveand you may not feel comfortable uploading it on a public cloud, preferring to analyze it on-premise. In this case, you need to use an in-house system with GPU support. One solution is to use IBM’s Power Systems with Nvidia GPU and Power AI. The Power AI platform supports popular machine learning libraries and dependencies including Tensorflow, Caffe, Torch, and Theano.</p>

<p>In this course, you'll understand what GPU-based accelerated hardware is and how it can benefit your deep learning scaling needs. You'll also deploy deep learning networks on GPU accelerated hardware for several problems, including the classification of images and videos.</p>
</section>

<h2>What will I get after passing this course?</h2>

<ul>
	<li>You will receive a completion certificate.</li>
</ul>

<h2>What will you learn?</h2>

<ul>
	<li>Explain what GPU is, how it can speed up the computation, and its advantages in comparison with CPUs.</li>
	<li>Implement deep learning networks on GPUs.</li>
	<li>Train and deploy deep learning networks for image and video classification as well as for object recognition.</li>
</ul>

<h2>Course Syllabus</h2>

<ul>
	<li>Module 1 - Quick review of Deep Learning</li>
	<li>Module 2 - Hardware Accelerated Deep Learning</li>
	<li>Module 3 - Deep Learning in the Cloud</li>
	<li>Moduel 4 - Distributed Deep Learning</li>
</ul>

<h2>General Information</h2>

<ul>
	
	<li>This course is self-paced.</li>
	<li>It can be taken at any time.</li>
	<li>It can be taken as many times as you wish.</li>
</ul>

<h2>Recommended skills prior to taking this course</h2>

<p>None</p>

<h2>Grading scheme</h2>

<ul>
	<li>The minimum passing mark for the course is 70%, where the review questions are worth 50% and the final exam is worth 50% of the course mark.</li>
	<li>You have 1 attempt to take the exam with multiple attempts per question.</li>
</ul>

<section class="prerequisites">
<h2>Requirements</h2>

<p>None.</p>
</section>

Accelerating Deep Learning with GPUs

Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.

<div class="trix-content">
  <div><strong><br />A person makes a doctor's appointment, receives all the instructions, and no-show. Who to blame?</strong></div><div>
<br />In this notebook we will try to analyze why would some patients not show up for their medical appointment and whether there are reasons for that using the data we have.<br />We will try to find some correlation between the different attributes we have and whether the patient shows up or not.<br />The dataset we are going to use contains 110.527 medical appointments and its 14 associated variables ( PatientId, AppointmentID, Gender, ScheduledDay, AppointmentDay, Age, Neighbourhood, Scholarship, Hypertension, Diabetes, Alcoholism, Handcap', SMS_received, No-show )</div><div><strong><br />Questions to answer</strong></div><ul>
<li>What is the percentage of no-shows?</li>
<li>What factors are important for us to know in order to predict if a patient will show up for their scheduled appointment?<ul>
<li>Is the time gender related to whether a patient will show or not?</li>
<li>Are patients with scholarships more likely to miss their appointment?</li>
<li>Are patients who don't receive SMS more likely to miss their appointment?</li>
<li>Is the time difference between the scheduling and appointment related to whether a patient will show?</li>
<li>Does age affect whether a patient will show up or not?</li>
<li>What is the percentage of patients missing their appointments for every neighborhood?<br /><br />
</li>
</ul>
</li>
</ul><div>Authors</div><ul>
<li><a href="https://www.linkedin.com/in/mostafa-abdelaleem/">Mostafa Abdelaleem</a></li>
<li><a href="https://www.linkedin.com/in/mridul-bhandari/">Mridul Bhandari</a></li>
</ul>
</div>


Medical Appointment Data Analysis

In this notebook we will try to analyze why would some patients not show up for their medical appointment and whether there are reasons for that using the data we have.

<div class="trix-content">
  <div>Django is a high-level web framework for Python, which is an open source general purpose programming language.<br /><br />In this guided project you will use a command line interface (CLI) in Django to create a simple web page. You will start by installing Django packages and creating a Django project to create a Django app. You will explore the fundamental configuration and setting files in the project scaffold. Next you will discover how to perform migrations to create your database tables and start a development server hosting app. Then you will add a view to receive HTTP requests and return HTTP responses. When you configure its URL, you can launch your app as a simple HTML page.<br /><br />This guided project will give you a great start in using CLI to create Django projects and configure and launch Django apps.<br /><br /><strong>A Look at the Project Ahead</strong><br />Once you have completed this project, you'll be able to:</div><ul>
<li>Create your first Django project and app using command line utils</li>
<li>Create your first Django view to return a simple HTML page</li>
</ul><div>
<strong>What You’ll Need</strong><br />Just a web browser and an IBM Cloud account!<br /><br />Everything else is provided to you via the IBM Skills Network Labs environment. This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.<br /><br />Knowledge of command line interface (CLI) usage will be helpful.<br /><br /><strong>Your Instructor</strong><br />Yan Luo</div>
</div>


Develop a Django App

In this guided project you will create your first Django app with command line utilities. You will discover how to create a Django view to return a simple HTML page.

<div class="trix-content">
  <div>Kubernetes, the leader in container orchestration, manages large clusters of containers. Kubernetes provides a way to schedule, deploy, and scale containers.<br /><br />In this guided project, you will explore the container world with Kubernetes and expand your repertoire of database management skills. Using the Kubernetes command line interface (CLI), you will discover how to get cluster information. You will create a Kubernetes pod and execute a variety of commands to extract information about it. You will practice imperative object configuration, which lets you create objects by specifying the action to take (like create, update, and delete) while using a configuration file. Then you will create a pod by using a declarative command, which is ideal for a production environment. You will see how Kubernetes handles load balancing with your new application.<br /><br />This guided project will prepare you to gain expertise with Kubernetes database as a service (DBaaS) management.<br /><br /><strong>A Look at the Project Ahead</strong><br />Once you have completed this project, you'll be able to:</div><ul>
<li>Use the kubectl CLI</li>
<li>Create a Kubernetes Pod</li>
<li>Create a Kubernetes Deployment</li>
<li>Create a ReplicaSet that maintains a set number of replicas</li>
<li>Witness Kubernetes load balancing in action</li>
</ul><div>
<br /><strong>What You’ll Need</strong><br />Just a web browser and an IBM Cloud account!<br /><br />Everything else is provided to you via the IBM Skills Network Labs environment. This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.<br />Knowledge of command-line interface (CLI) usage will be helpful.<br /><br /><strong>Your Instructors</strong><br />Upkar Lidder<br />Alex Parker</div>
</div>


Create and Deploy Your First Kubernetes Pod

In this guided project, you will explore Kubernetes container management by creating and deploying a pod and seeing how Kubernetes handles load balancing.

<div class="trix-content">
  <h1><strong>A Look At the Project Ahead</strong></h1><div>Once you have completed, this project, you’ll be able to:</div><ul>
<li>Load data from a spreadsheet to Python</li>
<li>Use Pandas, a Python library, to perform spreadsheet operations such as subsetting, filtering and calculating column mean, median and max</li>
<li>Export your data table to an Excel spreadsheet </li>
</ul><h1>What You'll Need</h1><div>Before starting this project, it'll be helpful to have some basic Python knowledge, such as the concept of a list, dictionary, function, class, instance variable, method, etc.<br /><br />Otherwise, all you'll need is a web browser!<br /><br />Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to JupyterLab. This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.</div><div><br /></div><h1>Instructor</h1><div>Weiqing Wang, IBM<br /><br />
</div><h1>Other Contributors</h1><div>Kathy An, IBM<br />Yasmine Hemmati, IBM</div>
</div>


Analyzing Spreadsheet Data with Python

We all know how hard it can be to sift through spreadsheets sometimes. With hundreds upon hundreds of rows and columns, there’s a lot of data to get through! But not to worry, we have some good news for you: all that data can be analyzed in Python with just a few lines of code. No longer do you have to scroll through your lagging spreadsheets! With only a few keystrokes, you can cherry pick the data that you need. Get your spreadsheets ready because with this project, you’ll be able to launch your journey into the world of data analysis!

<div class="trix-content">
  <div>In this guided project, you will learn how to start MongoDB. You will create your first database and gain hands-on experience listing and creating collections. You will explore basic document management functions and run your own commands in an actual terminal<br /><br />
</div><div>
<strong>Why should you learn to use MongoDB?</strong><br /> This popular non-relational database is easy to access by indexing and supports various data types, including dates and numbers. Use MongoDB to effortlessly query, manipulate, and extract powerful insights from your collected data. Working with schemaless MongoDB is easy because you can focus on the data you are writing and how you are going to read it.<br /><br /><strong>A Look at the Project Ahead</strong><br />Once you have completed this project, you'll be able to:</div><ul>
<li>Access the MongoDB server using the command line interface</li>
<li>Describe the process of listing and creating collections, which contain documents, and databases, which contain one or more collections</li>
<li>Perform basic operations on a collection such as inserting, counting, and listing documents</li>
</ul><div>
<br /><strong>What You’ll Need</strong><br />Just a web browser!<br /><br />Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to the MongoDB service that we offer as part of the IBM Skills Network Labs environment. This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.<br /><br />
</div><div>
<strong>Your Instructor</strong><br />Ramesh Sannareddy</div>
</div>


Create Your First MongoDB Database

In this guided project, you will get started with MongoDB by creating your first database, working with collections, and doing basic document management. 

<section class="about">
  <h2>About This Course</h2>
  <p>Data Science is like triathlon. Programming is cycling, by far the biggest investment is required in hardware and software. Running is domain expertise and communication skills and, swimming is mathematics, statistics and modelling. There are competitions in each of these disciplines cycling, running and swimming (and there always will be), but the need for super athletes who can do all 3 is growing. An athlete who is brilliant at one discipline can learn the other two and succeed in triathlon. </p>

  <p>No matter your core discipline(s), this course will take you through a short triathlon. You will define a business problem, establish the data required to solve it, you will write scripts in R programming language to build a model to give you insights and you will learn to present your findings in a business format to a business audience. </p>
</section>

<section class="prerequisites">
  <h2>Requirements</h2>
  <p>  Students will benefit from having a basic understanding of Statistics.
    
    Students with a basic knowledge of how Search Engines work may have a better appreciation for the case study context however, this is not a requirement.
</p>
</section>

<section class="course-staff">
  <h2>Course Staff</h2>
  <article class="teacher">
    <p><img alt="" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Shingai.png" style="width: 150px; height: 150px; border-radius: 10px" /></p>
  
    <h3>Fireside Analytics Inc.</h3>
    <p>Shingai Manjengwa (@Tjido) is the Director of Insights and Analytics at Fireside Analytics Inc. An NYU Stern alum, she graduated from the Stern Business Analytics Masters program in 2014 and founded Fireside Analytics the following year. Fireside Analytics is a data analytics consulting company that makes data analytics and data science skills accessible to private sector companies, non-profits and education institutions. Fireside Analytics works with clients to build their data science capabilities and train their staff and stakeholders using customized case studies. Connect with us on Facebook, Twitter and LinkedIn. </p>
  </article>



<section class="faq">
  <section class="responses">
    <h2>Frequently Asked Questions</h2>
    <article class="response">
      <h3>What web browser should I use?</h3>
      <p>The Open edX platform works best with current versions of Chrome, Firefox or Safari, or with Internet Explorer version 9 and above.</p>
      <p>See our <a href="http://edx.readthedocs.org/en/latest/browsers.html">list of supported browsers</a> for the most up-to-date information.</p>
    </article>

    <article class="response">
      <h3>Will I have to install new software in this course?</h3>
      <p>All the software you will need can be accessed through your browser at www.datasicentistworkbench.com. You will need to register and create an account. If you choose to not use Data Scientist Workbench, you may need to install R on your local computer.</p>
    </article>
  </section>
</section></section>

Digital Analytics & Regression

This course uses a case study approach to take you through the end to end process of identifying a business objective, designing the model to address it, sourcing the data and ultimately arriving at the insights. When you complete this course, you can apply these methods and principles in a variety of contexts, with big, medium or small data.

<section class="about"> <h2>About This Course</h2> <p>This course introduces you to the strange world of quantum computing. You’ll learn about quantum concepts including superposition, entanglement, and uncertainty. You’ll also perform “labs” to get a first-hand look at these concepts. At the end, there’s a quiz to test your knowledge.</p> </section><h2>Course Syllabus</h2><ul>
  <li>The Hall of Wonders</li>
  <li>The Superposition Lab</li>
  <li>The Entanglement Lab</li>
  <li>The Uncertainty Lab</li>
  <li>Return to the Hall of Wonders</li>
  <li>Show what you know!</li>
 </ul>

<h2>Recommended skills prior to taking this course</h2>
<p>This is a course for beginners. You do not need any special skills to succeed with this course.</p><h2>Grading scheme</h2>
<ul>
 <li>The minimum passing mark for the course is 80%, the final exam is worth 100% of the course mark.</li>
 <li>You have 1 attempt to take the exam with multiple attempts per question.</li></ul><section class="prerequisites"> </section> <section class="course-staff"><h2>Course Contributors</h2> <article class="teacher"> <div class="teacher-image"> <img src="/static/_AY_3435.jpg" style="margin: 0 20 px 0" alt="" /> </div>
<ul>
  <li>Katie Pizzolato, IBM</li>
  <li>Bob Suter, IBM</li>
  <li>John Preskill</li>
  <li>Spiros Michalakis</li>
</ul></article></section>

Introduction to Quantum Computing

This course introduces you to the strange world of quantum computing.

<section>
 <h2>About This Course</h2>
<p>
In this course, we will cover the core concepts and practices of building and running Cloud Native applications and how to run these applications in a multicloud environment. We will cover technologies and practices including; microservices, DevOps, CI/CD, Docker, Kubernetes, and OpenShift.
<p>
  <section>
<h2>
 Learning Objectives<h2>
 
After completing this course you will be able to:
  <ul>
    <li> Understand the core principles and practice of build Cloud Native applications</li>
 <li>How to modernize existing applications to be Cloud Native</li>
 <li>How to deconstruct and monolithic application in a microservice architecture</li>
 <li>Build and deploy containers to a Kubernetes cluster</li>
 <li>Understand the guiding principles and benefits of a CI/CD pipeline</li>
 <li> How to build a CI/CD pipeline</li>
 <li>The benefits of adopting a hybrid-cloud and multicloud architectures</li>
</ul>
   <p>
     <p>
  <section>
 <h2> Syllabus </h2>
  <ul>
     <p>
 <strong>Module 1: Cloud Native and Multicloud Concepts and Goals <strong>
   <ul>
<li>Course Welcome Video</li>
<li>Module Introduction and Learning Objectives</li>
<li>What is Cloud Native</li>
<li>What are Cloud Native Applications?</li>
<li>Achieving Agility</li>
<li>Maximizing Operability with DevOps</li>
<li>Ensuring Observability for DevSecOps</li>
<li>Increasing Resiliency</li>
<li>Module Summary</li>
<li>Practice Quiz</li>
<li>Graded Quiz</li>
</ul>
<p>
 <p><strong>Module 2: Migrating Apps to Advantage Cloud Infrastructure <strong>
  <ul>
  <li>Module Introduction and Learning Objectives</li>
<li>Considerations and benefits of adopting Cloud Native</li>
<li>Paths for Application Modernization</li>
<li>Application Migration using Containers</li>
<li>Data Migration to Cloud</li>
<li>Demo: Application Migration Planning</li>
<li>Demo: Data Migration (Aspera on IBM Cloud)</li>
<li>Lab</li>
<li>Module Summary</li>
<li>Practice Quiz</li>
<li>Graded Quiz</li>
  </ul>
<p>
  <p>
<strong>Module 3: Modernizing applications to be CN <strong>
  <ul>
  <li>Module Introduction and Learning Objectives</li>
<li>What is Architectural Modernization and its Benefits?</li>
<li>Adopting a Microservices Architecture</li>
<li>Reactive programming with Serverless</li>
<li>Why are Containers so important?</li>
<li>Leveraging Container Orchestration Platforms</li>
<li>Using the IBM Garage Method to Modernize</li>
<li>Demo: Using the IBM Garage Method to Modernize</li>
<li>Managing Cloud Native Applications on Kubernetes</li>
<li>Module Summary</li>
<li>Practice Quiz</li>
<li>Graded Quiz</li>
    </ul>
<p>
<p>
<strong>Module 4: Applying CI/CD to CN applications <strong>
   <ul>
  <li>Module Introduction and Learning Objectives</li>
<li>What are the Benefits of employing CI/CD</li>
<li>Automating Validation</li>
<li>Configuring Infrastructure using GitOps</li>
<li>Improving Observability through CI/CD</li>
<li>Building and Deploying to the Cloud</li>
<li>Demo: IBM Toolchains (IBM Cloud with Tekton)</li>
<li>Building Pipelines with Tekton</li>
<li>Module Summary</li>
<li>Practice Quiz</li>
<li>Graded Quiz</li>
    </ul>
<p>
<p>
<strong>Module 5: Managing Applications in Multicloud Deployments <strong>
  <ul>
  <li>Module Introduction and Learning Objectives</li>
<li>Understanding HC, MC, and Hybrid MC</li>
<li>What is MCM and its use cases?</li>
<li>What are the capabilities of MCM?</li>
<li>Utilizing Hybrid Cloud Platforms to enable MCM</li>
<li>Automating MC apps</li>
<li>Demo: Cloud Pak for MultiCloud Manager</li>
<li>Exploring the OpenShift Advanced Web Console</li>
<li>Module Summary</li>
<li>Practice Quiz</li>
<li>Graded Quiz</li>
    </ul>
<p>
<p>
<strong>Module 6: Final Exam<strong>
  <ul>
  <li>Final Exam</li>
</ul>
<p>
   <section>
<h2>General Information</h2>
<ul>

 <li>This course is self-paced.</li>
 <li>It can be taken at any time.</li>
 <li>It can be audited as many times as you wish.</li>
 <li>There is only <strong>ONE</strong> chance to pass the course, but multiple attempts per question</li>
</ul>
     <p>
   <section>
<h2>Recommended skills prior to taking this course</h2>
<ul>
 <li> Basic Math</li>
</ul>
<section>
</section>
<h2>Course Staff</h2>
<article>
</article>
  <img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Matt%20pic.png" alt="Building Cloud Native and Multicloud Applications Instructor" width="214" height="214" />
  <div><strong>Matt Rutkowski</strong><p>
</p>&amp;nbsp <p>An STSM and Master Inventor at IBM developing open infrastructure and industry standards along with open source for over 20 years in areas including Banking, Digital Media and Entertainment, Government and specializing in Cloud for the last 9 years. Most recently, he is the IBM lead for and a Committer to the Apache OpenWhisk Serverless computing project at Apache Software Foundation (ASF) serving on its Project Mgmt. committee and as a Committer. In addition, he has worked on Cloud Orchestration, Security, Audit and Compliance standards. Specifically, he has Chaired and been Lead Editor for such standards as OASIS Topology Orchestration for Cloud Applications (TOSCA), OASIS CloudID and DMTF Cloud Auditing (CADF) which he founded. Furthermore, he has contributed to implementations of these standards within communities such as OpenStack.</p></div><p>
</p>
&amp;nbsp
<p>
</p>
<img src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/billy_korando%20(1).png" alt="Building Cloud Native and Multicloud Applications Instructor" width="214" height="214" />
  <div><strong>Billy Korando </strong><p>
</p>&amp;nbsp <p>Java Spring developer with over nine years of experience. I have been involved in the initial conception to the design, coding, and push to production of enterprise web applications as well as major updates to existing web applications. I enjoy learning about software development, improving team development practices, and mentoring junior developers.</p></div></section></p></section></p></strong></strong></p></p></strong></strong></p></p></strong></strong></p></p></strong></strong></p></p></strong></strong></p></p></strong></strong></p></ul></section></p></p></h2></h2></section></p></p></section>

Building Cloud Native and Multicloud Applications

In this course, we will cover the core concepts and practices of building and running Cloud Native applications and how to run these applications in a multicloud environment. We will cover technologies and practices including; microservices, DevOps, CI/CD, Docker, Kubernetes, and OpenShift.

<div class="trix-content">
  <div>HyperText Markup Language (HTML) is the basic markup language for websites. HTML provides a website’s structure, content, and formatting through document components called elements. To specify how elements should appear, developers use a stylesheet language called Cascading Style Sheets (CSS). <br /><br />In this guided project, you will learn how to create a simple web page using HTML. You will create a HTML document. Next, you will add a head element to define the web page’s metadata, which is data about the web page such as its title and publication date. You will create a body element. Then you will practice adding other basic structural elements such as headings, paragraphs, images, lists, tables, and links.<br /><br />You will then explore several ways to use CSS to modify the appearance of elements. You will change an element’s font family, font size, font color, and background color. You will also add borders to tables and discover how to make tables more appealing and readable.<br /><br />Completing this guided project will prepare you to create and style simple websites and to start learning more complex web development skills.<br /><br /><strong>A Look at the Project Ahead</strong><br />Once you have completed this project, you'll be able to:<br /><br />
</div><ul>
<li>Create a new HTML document that includes HTML metadata</li>
<li>Define the body of a web page by adding a body element</li>
<li>Add paragraphs and headings to a web page</li>
<li>Add images, lists, tables, and links to a web page </li>
<li>Modify the font family and font size for HTML text by using CSS</li>
<li>Modify the color of HTML elements by using CSS</li>
<li>Apply borders to HTML elements by using CSS</li>
</ul><div>
<br /><strong>What You’ll Need</strong><br />Just a web browser!<br /><br />Everything else is provided to you via the IBM Skills Network Labs environment. This environment works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.<br /><br /><strong>Your Instructor</strong><br />Ramesh Sannareddy</div>
</div>


Create and Style a Simple Web Page with HTML and CSS

In this guided project, you will use HyperText Markup Language (HTML) to create a simple web page and Cascading Style Sheets (CSS) to modify a web page’s appearance.

<div class="trix-content">
  <div>In this guided project you will learn how to build a mathematical model of an insurance company using Risk Process and to do risk and revenue assessment<br /><br />
</div><h1>Learning Objectives</h1><div>After completing this guided project you will be able to:</div><ul>
<li>Build the model of an Insurance Company with Risk Process.<ul><li>Understand Classical Risk Process</li></ul>
</li>
<li>Understand basic actuarial approximations and their limitations <ul><li>Use Actuarial approximations for Risk Assessment of the Classical Risk Process</li></ul>
</li>
<li>Calculate the Ruin Probability of the Classical Risk Process using Monte Carlo method and compare it to theoretical results</li>
</ul>
</div>


Insurance Business Modelling and Basic Actuarial Calculations

In this guided project you will learn how to build a mathematical model of an insurance company using Risk Process and to do risk and revenue assessment.

<section class="about">
<section>
<h2>ABOUT THIS COURSE</h2>

<p>Istio is an open platform to connect, secure, and manage a network of microservices, also known as a service mesh, on cloud platforms such as Kubernetes in the IBM Cloud Kubernetes Service. With Istio, You can manage network traffic, load balance across microservices, enforce access policies, verify service identity on the service mesh, and more.
</p><p>
In this course, you learn how to install Istio alongside microservices for a simple mock application called Guestbook. When you deploy Guestbook's microservices into an IBM Cloud Kubernetes Service cluster where Istio is installed, you inject the Istio Envoy sidecar proxies in the pods of each microservice.
</p><p>
  <strong>Important: </strong>Some configurations and features of the Istio platform are still under development and are subject to change based on user feedback. Allow a few months for stabilization before you use Istio in production.</p>
  
  <h2>Learning Objectives</h2>

<p>After you complete this course, you'll be able to:</p>
<ul>
  <li>Download and install Istio in your cluster</li>
<li>Deploy the Guestbook sample app</li>
<li>Set up the Istio Ingress controller</li>
<li>Use metrics, logging, and tracing to observe services</li>
<li>Perform simple traffic management, such as A/B tests and canary deployments</li>
<li>Secure your service mesh</li>
  <li>Enforce policies for your microservices</li>
</ul></section>

<hr />
<h2>COURSE SYLLABUS</h2>

  <ul><li><strong>Lab 1: </strong>Observe service telemetry: metrics and tracing </li>

<li><strong>Lab 2: </strong>Expose the service mesh with the Istio Ingress controller </li>

<li><strong>Lab 3: </strong>Manage traffic </li>

<li><strong>Lab 4: </strong>Secure your service mesh </li>

<li><strong>Lab 5:</strong> Enforce policies for microservices
  </li></ul>
<hr />
<h2>GENERAL INFORMATION</h2>

		<ul>
			
			<li>This course is self-paced.</li>
			<li>It can be taken at any time.</li>
			<li>It can be audited as many times as you wish.</li>
		
</ul>

<hr />
<h2>PREREQUISITES</h2>

<ul><p>
You should have a basic understanding of containers, microservices, Istio, and IBM Cloud Kubernetes Service. If you have no experience with those, take the following courses:
  </p><p>
<ol>
  
  <li>Container and Kubernetes Essentials with IBM Cloud</li>
<li>Getting started with Microservices with Istio and IBM Cloud Kubernetes Service</li></ol>
You must have an IBM Cloud account to complete this course.
  </p></ul></section>

Beyond the Basics: Istio and IBM Cloud Kubernetes Service

Start managing your microservices with Istio on IBM Cloud Kubernetes Service. This course shows you how to better  control traffic to services, observe service health, and secure the service mesh.

<section class="about">
  <h2>About this course</h2>
  <p>In this course, you learn about the twelve-factor app methodology, microservices, and Istio foundational technologies. These cloud-native technologies are the essentials as you build new apps for the cloud. Microservices are the building blocks for your cloud architecture. Then, you layer on Istio to help you connect, manage, and secure those microservices. All of those are then put together in IBM Cloud Kubernetes Service.</p>
  <h2>What will I get after passing this course?</h2>
  <ul>
    <li>
      You will receive a completion certificate.
    </li>
    </ul>
  
  <h2>Course syllabus</h2>
  <ul>
    <li>Twelve-factor app
    <ul>
      <li>Twelve-factor methodology</li>
      <li>The twelve factors</li>
      <li>Codebase</li>
      <li>Dependencies</li>
      <li>Configuration</li>
      <li>Backing services</li>
      <li>Build, release, run</li>
      <li>Processes</li>
      <li>Port binding</li>
      <li>Concurrency</li>
      <li>Disposability</li>
      <li>Development and production parity</li>
      <li>Logs</li>
      <li>Admin processes</li>
      <li>Summary of the twelve-factor app</li>
      </ul>
      </li>
    </ul>
  <ul><li>Microservices
      <ul>
        <li>Unmaintainable, monolithic apps</li>
        <li>What are microservices?</li>
        <li>Microservices: Making developers more efficient</li>
        <li>Microservices architecture</li>
        <li>Example app that uses microservices</li>
        <li>Key tenets of a microservices architecture</li>
        <li>Comparing monolithic and microservices architectures</li>
        <li>Emergence of microservices from modern development tools and processes</li>
        <li>Operational requirements for microservices</li>
        <li>Summary: Advantages of microservices</li>
        </ul>
    </li></ul>
      <ul>
        <li>Application architecture evolution: From SOA to microservices
           <ul>
            <li>Layered application architecture</li>
            <li>SOA stack</li>
            <li>Microservices and SOA</li>
            <li>Monolithic architecture versus microservices architecture</li>
            <li>Microservices and IBM Cloud Kubernetes Service</li>
          </ul>
          </li>
  </ul>
                  
              
      <ul>
    <li>Microservices component architecture
      <ul>
            <li>Microservices architecture mapped to the SOA stack</li>
            <li>Microservices types hierarchy</li>
            <li>Language decisions</li>
            <li>Backend for frontend (BFF)</li>
            <li>Business service microservices dependencies: Typical</li>
            <li>Business service microservices dependencies: Death Star</li>
                  </ul> </li>
  </ul>
  <ul><li>Microservices integration
    <ul>
      <li>Communication among services</li>
       <li>Synchronous versus asynchronous communication</li>
  <li>Microservices intercommunication</li>
  <li>Microservices communication in IBM Cloud Kubernetes Service</li>
  <li>IBM Message Hub service</li>
    </ul>
    </li>
     </ul>
  <ul><li>Service mesh
    <ul>
  <li>Comparison of operations for monolithic and microservice architectures</li>
  <li>A service mesh can help</li>
  <li>Service registry</li>
  <li>Service discovery and service proxy</li>
  <li>Client-side discovery</li>
  <li>Server-side discovery</li>
  <li>Automated testing</li>
  <li>Circuit breaker</li>
      <li>Bulkhead</li>
          </ul>
    </li>
  </ul>
  <ul><li>Istio: An intelligent service mesh for microservices
    <ul>
      <li>Service mesh implementation</li>
      <li>How Istio works</li>
      <li>Istio mesh request flow</li>
      <li>What Istio provides for microservices architectures</li>
      <li>Discovery and load balancing</li>
      <li>Handling failures</li>
      <li>Fault injection</li>
      <li>Mutual TLS Authentication</li>
         </ul>
    </li></ul>
  <ul><li>Final exam</li></ul>
  </section>

<section class="prerequisites">
  <h2>Requirements</h2>
  <p>You should have a basic understanding of containers. It’s also helpful to understand Kubernetes. Be sure to complete the course "Getting Started with Containers on IBM Cloud."</p>
  <p>You must have a Pay-As-You-Go or Subscription <a href="http://cocl.us/istio1_reg" target="_blank">IBM Cloud account</a> so that you can create a free cluster.</p>
</section>

<section>
<h2>Course instructors</h2>
    
<h3><strong>Lin Sun</strong></h3>
	<p>STSM and Master Inventor, Istio and IBM Watson and Cloud Platform</p>
    

   <h3><strong>Nilesh Patel</strong></h3>
   <p>Offering Manager - IBM Cloud Container Service and Istio, IBM Watson and Cloud Platform</p>
      
</section>
<section>
<h2>Course staff</h2>
  
<h3><strong>Cesar Cantua</strong></h3>
	<p>IBM Courseware Developer</p>
    

    <h3><strong>Michelle Carey</strong></h3>
   <p>IBM Courseware Developer</p>
   
</section>


Getting started with Microservices with Istio and IBM Cloud Kubernetes Service

Discover how microservices and Istio pair together for cloud-native apps. Learn how Istio and IBM Cloud Kubernetes Service help you securely and seamlessly deploy containers and apps.

<div class="trix-content">
  <div>You will be using the Django web framework to build a full-stack movie recommender app.  In this app, you can save your movie watch history, and review the recommended movies from more than 40,000 movies. The recommendation algorithm included in this project is very simple but effective so you do not even need any prior Machine Learning knowledge to understand and complete this project.<br /><br /><br />
</div><h1>Learn by Doing</h1><div>A guided project is a hands-on tutorial designed to help you learn a particular technology by doing a real project. It includes step-by-step instructions with explanations, examples, and exercises that can be followed and practiced in a lab environment.</div><div>Hands-on skills are highly sought out by employers when determining job readiness. Guided projects are interactive, on-demand and will equip you with practical abilities that can be applied on the job!</div><div>
<br /><br />
</div><h1>A Look at the Project Ahead</h1><div>After completing this project, you will be able to<br />-  Describe the key components of the Django web framework <br />-  Describe the basic principles of a recommendation system <br />-  Build a simple but effective personal movie recommender with Django<br /><br /><br />
</div><h1>
<strong>What You’ll Need  </strong> </h1><div>Firstly, you just need a web browser!  Regarding prior skills,  you will also need basic Python programming knowledge to write the backend and some basic HTML/CSS knowledge to create the frontend. For the recommendation algorithm part, you only need to have a basic understanding of the Set data structure<br /><br />Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to the Cloud IDE and Python runtimes that we offer as part of the IBM Skills Network Labs environment. <br /><br />This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.<br /><br /><br />
</div><h1>Your Instructors</h1><div>Yan Luo, IBM<br /><br />
</div>
</div>


Build Your Movie Recommender with Django

Choosing a good movie to watch on a weekend evening is always hard.  It would be great if there is a 
handy movie recommender to help you make a good decision.  In this project, you will be building a personal movie recommender using the popular Django web framework.

When multiple participants, such as farms, manufacturers, processing plants, trucks, ports, ships, distribution centers, and consumer retail outlets, are involved in the safe shipment and payment of cargo, you can use a blockchain network to record immutable transactions as the cargo shipment progresses through its delivery journey.

In this course, you'll do the following main tasks:

- Configure an asset tracker IoT device (or use the simulator)
- Build a blockchain business network in Hyperledger Composer
- Build an IoT application to visualize and analyze the data from the asset tracker:
-- Create an IoT starter app in IBM Cloud
-- Create Node-RED flows to control and receive events from Particle.io, write events to the blockchain network, and load blockchain transaction history
-- Use Node-RED to build an asset tracking dashboard

Build an IoT Blockchain Network for a Supply Chain

Learn how to use a an IoT Asset Tracking device, build a blockchain network, and configure a Node-RED dashboard to monitor a perishable network supply chain. You can also use a simulated tracker if you don't want to purchase and set up the hardware.

<section class="about">
<h2>About This Course</h2>

<p>This Machine Learning with R course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.</p>

<p>Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!</p>

<p><strong>Explore many algorithms and models:</strong></p>

<ul>
	<li>Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.</li>
</ul>

<ul>
	<li>Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.</li>
</ul>

<p>Get ready to do more learning than your machine!</p>

<hr />
<h2>COURSE SYLLABUS</h2>

<p><b>Module 1 - Machine Learning </b><b>vs Statistical Modeling &amp; Supervised vs Unsupervised Learning</b></p>

<ul>
	<li>Machine Learning Languages, Types, and Examples </li>
</ul>

<ul>
	<li>Machine Learning vs Statistical Modelling</li>
</ul>

<ul>
	<li>Supervised vs Unsupervised Learning </li>
</ul>

<ul>
	<li>Supervised Learning Classification </li>
</ul>

<ul>
	<li>Unsupervised Learning </li>
</ul>

<p><b>Module 2 - Supervised Learning I</b></p>

<ul>
	<li>K-Nearest Neighbors </li>
</ul>

<ul>
	<li>Decision Trees </li>
</ul>

<ul>
	<li>Random Forests</li>
</ul>

<ul>
	<li>Reliability of Random Forests </li>
</ul>

<ul>
	<li>Advantages &amp; Disadvantages of Decision Trees </li>
</ul>

<p> <b>Module 3 - Supervised Learning II</b></p>

<ul>
	<li>Regression Algorithms </li>
</ul>

<ul>
	<li>Model Evaluation </li>
</ul>

<ul>
	<li>Model Evaluation: Overfitting &amp; Underfitting</li>
</ul>

<ul>
	<li>Understanding Different Evaluation Models </li>
</ul>

<p> <b>Module 4 - Unsupervised Learning</b></p>

<ul>
	<li>K-Means Clustering plus Advantages &amp; Disadvantages </li>
</ul>

<ul>
	<li>Hierarchical Clustering plus Advantages &amp; Disadvantages </li>
</ul>

<ul>
	<li>Measuring the Distances Between Clusters - Single Linkage Clustering </li>
</ul>

<ul>
	<li>Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering</li>
</ul>

<ul>
	<li>Density-Based Clustering </li>
</ul>

<p><b>Module 5 - Dimensionality Reduction &amp; Collaborative Filtering</b></p>

<ul>
	<li>Dimensionality Reduction: Feature Extraction &amp; Selection </li>
</ul>

<ul>
	<li>Collaborative Filtering &amp; Its Challenges </li>
</ul>

<hr />
<h2>REQUIREMENTS</h2>

<section>
<ul>
	<li>R programming</li>
</ul>
</section>

<hr />
<h2>RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE</h2>

<ul>
	<li>You have to do hands-on lab for this course. The tool that you use for hands-on is called Jupyter and it is one of the most popular tools used by data scientists. If you are not familiar with Jupyter, I would recommend that you take our free Data Science Hands-on with Open Source Tools.</li>
	<li>This hands-on lab requires that you have working knowledge of R programming language as it applies to data analytics. If you don't feel you have sufficient skill in Data Analysis with R, I recommend you take Data Analysis with R courses.</li>
</ul>
</section>

<section class="course-staff">
<hr />
<h2>Course Staff</h2>

<article class="teacher">
<div class="teacher-image"><img alt="Saeed Aghabozorgi" src="https://skills-network-assets.s3.us.cloud-object-storage.appdomain.cloud/Saeed%20pic.png" style="height: 100px; width: 100px; border-radius: 10px" /></div>

<h3><a href="https://ca.linkedin.com/in/saeedaghabozorgi" rel="noopener" target="_blank">Saeed Aghabozorgi</a></h3>

<p><a href="https://ca.linkedin.com/in/saeedaghabozorgi" rel="noopener" target="_blank">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>

<h3> </h3>

<h3>Cognitive Class Course Development Team</h3>

<p>Thanks to the course contributors: <a href="https://www.linkedin.com/in/daniel-tran-84ab26b3" rel="noopener" target="_blank">Daniel Tran</a>, <a href="https://www.linkedin.com/in/kevin-wong-15982093" rel="noopener" target="_blank">Kevin Wong</a></p>
</article>
</section>

Machine Learning with R

Learn what machine learning is all about in this beginner-friendly course. Through videos and labs, learn how to apply different machine learning techniques such as classification, clustering, neural networks, regression, and recommender systems.

<div class="trix-content">
  <div>Python is a popular programming language for working with data. MongoDB is a popular non-relational database that supports various data types, including dates and numbers. Python is a great choice for a wide range of basic and advanced MongoDB operations.<br /><br />In this guided project, you will discover how you can interact with MongoDB databases by using Python. You will install PyMongo, a Python driver for MongoDB, and use it to access a database. After accessing the database, you will use Python to perform a variety of queries on documents in a collection. Then you will learn how to create a Python program that inserts a sample document and prints the documents in the collection.<br /><br />This guided project will prepare you for applying Python to advanced operations in MongoDB documents.<br /><br /><strong>A Look at the Project Ahead</strong><br />Once you have completed this project, you'll be able to:</div><ul>
<li>Access the MongoDB database from Python with the PyMongo driver</li>
<li>Perform basic operations such as selecting, inserting, and listing using Python</li>
<li>Create a Python program to run MongoDB operations</li>
</ul><div>
<br /><strong>What You’ll Need</strong><br />Just a web browser!<br /><br />Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to the MongoDB service that we offer as part of the IBM Skills Network Labs environment. This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.<br /><br /><strong>Your Instructor</strong><br />Ramesh Sannareddy</div>
</div>


Accessing MongoDB Using Python

In this guided project, you will discover how to use Python to perform a variety of queries on the MongoDB database.

<div class="trix-content">
  <div>Reinforcement Learning is a different style of machine learning different from supervised and unsupervised learning. It is learning what to do through trial and error. Reinforcement learning is also an excellent method used to train robots to play games. OpenAI Gym is a python library that standardizes the interaction between Agent/User/Robot and the environment so you can interact with a variety of Gym environments. Reinforcement Learning and OpenAI Gym is a great combination that can be used to create games with an AI aspect.<br /><br />In this Guided Project, you will learn how to interact with the OpenAI Gym environment. We will be working with a custom environment created to play TicTacToe so you will also learn how to install custom environments. Additionally, we will learn about Reinforcement Learning and the algorithm Temporal Difference Learning, and how to implement an Agent using Temporal Difference Learning to play TicTacToe. Finally, we will play TicTacToe with our trained agent and environment and see an example of a TicTacToe game with a graphical user interface.<br /><br />
</div><h1><strong>Learn by Doing</strong></h1><div>A guided project is a hands-on tutorial designed to help you learn a particular technology by doing a real project. It includes step-by-step instructions with explanations, examples, and exercises that can be followed and practiced in a lab environment.<br /><br />
</div><div>Hands-on skills are highly sought out by employers when determining job readiness. Guided projects are interactive, on-demand and will equip you with practical abilities that can be applied on the job! </div><div><br /></div><h1><strong>A Look at the Project Ahead</strong></h1><div>Once you have completed this project, you'll be able to:</div><ul>
<li>Install a custom OpenAI Gym environment</li>
<li>Work with an OpenAI Gym environment and the TicTacToe environment</li>
<li>Explain what Reinforcement Learning is</li>
<li>Explain what Temporal Difference Learning is</li>
<li>Create an agent that uses Temporal Difference Learning to play TicTacToe</li>
<li>Train and Test the agents using the TicTacToe environment</li>
<li>Play some games against the trained agent</li>
</ul><h1><strong>What You’ll Need  </strong></h1><div>Just a web browser and Python programming knowledge are required!</div><div>Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to OpenAI Gym which is a tool that we offer as part of the IBM Skills Network Labs environment. This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.<br /><br />
</div><h1><strong>Your Instructor</strong></h1><div>Azim Hirjani, IBM</div>
</div>


Playing TicTacToe with Reinforcement Learning and OpenAI Gym

Learn how to create and teach an agent that never loses to play TicTacToe using a Reinforcement Learning algorithm called Temporal Difference Learning and Open AI Gym

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