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Machine Learning with Python: A Practical Introduction

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IntermediateCourse

Python has become one of the most widely used programming languages for machine learning due to its extensive libraries and frameworks and compatibility with other languages. This course will prepare you with essential Python skills.

4.5 (403 Reviews)

Language

  • English

Topic

  • Machine Learning

Industries

  • Information Technology

Enrollment Count

  • 127.95K

Skills You Will Learn

  • Machine Learning, Hierarchical Clustering, Classification, Regression, SciPy And scikit-learn

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 20 hours

Platform

  • edX

Last Update

  • July 9, 2025
About this Course
Python is recognized as one of the most widely used programming languages in machine learning (ML). For this reason, it is a required skill in many ML job listings. 

This course is suitable for machine learning practitioners looking to acquire essential Python skills that will help them stand out to employers. It focuses on core ML concepts and the iterative nature of model development. Using Python libraries such as Scikit-learn, you will be able to gain hands-on experience with tools used for real-world applications. You will also be able to build a strong foundation in statistical methods such as linear and logistic regression. 

With its extensive libraries, such as TensorFlow and Pandas, you'll discover supervised learning techniques. Its classification methods, such as decision trees, KNN, and SVM, and key concepts, such as the bias-variance tradeoff, will enable you to explore its power and versatility. Additionally, the course includes unsupervised learning, such as clustering and dimensionality reduction. 

Guidance on model evaluation, tuning techniques, and practical projects in Jupyter Notebooks will equip you with strong Python skills to power your ML journey. ENROLL TODAY to enhance your resume with in-demand expertise! 

 Learning Objectives

  • Job-ready foundational machine learning skills in Python in just 6 weeks, including how to use Scikit-learn to implement machine learning models. 
  • How to apply data preparation techniques and manage bias-variance tradeoffs to optimize model performance. 
  • How to build supervised learning models and implement core machine learning algorithms for classification and regression tasks, including linear regression, decision trees, and SVM. 
  • How to build unsupervised learning models and implement clustering models and dimension reduction algorithms. 

Course Syllabus

Module 1: Introduction to Machine Learning
Welcome to the Course
  • Video: Course Introduction and Welcome
  • Reading: Course Overview
  • Learning Objectives and Syllabus
  • Plugin/Reading: Helpful Tips for Course Completion
  • Grading Scheme
Machine Learning In Action
  • An Overview of Machine Learning
  • Machine Learning Model Lifecycle
  • A Day in the Life of a Machine Learning Engineer
  • Data Scientist vs AI Engineer
  • Tools for Machine Learning
  • Scikit-learn Machine Learning Ecosystem
  • Practice Quiz: Intro to Machine Learning
  • Module Summary and Highlights
  • Graded Quiz: Intro to Machine Learning
Module 2: Linear and Logistic Regression
  • Introduction to Regression
  • Simple Linear Regression
  • Lab: Simple Linear Regression
  • Multiple Linear Regression
  • Lab: Multiple Linear Regression
  • Non-Linear Polynomial Regression
  • Practice Quiz: Linear Regression 
  • Introduction to Logistic Regression
  • Logistic Regression Training
  • Lab: Logistic Regression
  • Practice Quiz: Regression
  • Module Summary
  • Cheatsheet
  • Graded Quiz: Regression
Module 3: Building Supervised learning models
  • Classification
  • Lab: Multiclass Prediction
  • Decision Trees
  • Lab: Decision Trees
  • Regression Trees
  • Lab: Regression Trees
  • Practice Quiz
  • Supervised Learning with SVMs
  • Lab: Credit Card Fraud Detection with Decision Trees and SVM 
  • Supervised Learning with KNN
  • Lab: KNN Classification
  • Bias, Variance, and Ensemble Models  
  • Lab: Random Forests and XGBoost
  • Practice Quiz: Other Supervised Learning Models
  • Module Summary
  • Cheatsheet
  • Graded Quiz
 Module 4: Building Unsupervised Learning Models 
  • Clustering Strategies and Real-World Applications
  • k-means and More on k-means
  • Lab: K-Means
  • DBSCAN and HDBSCAN
  • Lab: Comparing DBSCAN and HDBSCAN
  • Practice Quiz
  • Clustering, Dimension Reduction & Feature Engineering
  • Dimension reduction Algorithms
  • PCA Principle Component Analysis
  • t-SNE and UMAP
  • Practice Quiz: Advance Clustering Models
  • Module Summary
  • Cheatsheet
  • Graded Quiz
Module 5: Evaluating and Validating Machine Learning Models
  • Classification Metrics and Evaluation Techniques
  • Lab: Evaluating Classification Models
  • Regression Metrics and Evaluation Techniques
  • Lab: Evaluating random forest performance
  • Evaluating Unsupervised Learning Models: Heuristics and Techniques
  • Lab:Evaluating k-means clustering
  • Practice Quiz: Evaluating ML Models
  • Cross-Validation and Advanced Model Validation Techniques
  • Regularization in Regression and Classification
  • Lab: Regularization in Linear Regression
  • Data Leakage and other Pitfalls
  • Lab: Machine Learning Pipelines and GridSearchCV
  • Practice Quiz
  • Module Summary
  • Cheatsheet
  • Graded Quiz
Module 6: Course Summary and Final Project
  • Course Wrap up
  • Final Exam
  • Final Project Scenario
  • Final Project: Building a Rainfall Prediction Classifier
  • Final Project Submission and Evaluation
  • Congratulations and Next Steps
  • Thanks from the Course Team

Recommended Skills Prior to Taking this Course

To get the most out of this course, you should be comfortable with the following topics and technologies:
This course requires a working knowledge of Python and Python libraries, such as Pandas, NumPy, and more, to perform data preparation and data analysis. 

Instructors

Jeff Grossman

Founder, 617 Data Solutions Inc.

I create technical educational content and build stuff around data. I love kids, dogs, walking, bicycles and snow sports. I'm working on coming to terms with robots. I've been around long enough to witness an accelerating rate of change of technological advancement. We need all hands on deck to help protect ourselves from ourselves. Prosperity will come from efficiencies but will also require our acceptance of and cooperation with the change that is inevitable. Quoting Ray Kurzweil (via https://en.wikipedia.org/wiki/The_Singularity_Is_Near): "Kurzweil concedes that every technology carries with it the risk of misuse or abuse, from viruses and nanobots to out-of-control AI machines. He believes the only countermeasure is to invest in defensive technologies, for example by allowing new genetics and medical treatments, monitoring for dangerous pathogens, and creating limited moratoriums on certain technologies. As for artificial intelligence Kurzweil feels the best defense is to increase the "values of liberty, tolerance, and respect for knowledge and diversity" in society, because "the nonbiological intelligence will be embedded in our society and will reflect our values".

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IBM Skills Network

IBM Skills Network Team

At IBM Skills Network, we know how crucial it is for businesses, professionals, and students to build hands-on, job-ready skills quickly to stay competitive. Our courses are designed by experts who work at the forefront of technological innovation. With years of experience in fields like AI, software development, cybersecurity, data science, business management, and more, our instructors bring real-world insights and practical, hands-on learning to every module. Whether you're upskilling yourself or your team, we will equip you with the practical experience and future focused technical and business knowledge you need to succeed in today’s ever-evolving world.

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