Intermediate friendly Free

Machine Learning with Python Tutorial

Learn machine learning in depth with Python: data preparation, supervised and unsupervised models, evaluation, responsible AI, deployment, and monitoring.

  • 24lessons
  • 8modules
  • ~8hstudy time
  • Intermediatestart level
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What you will learn

  • ML Foundations
  • Data Preparation
  • Supervised Learning
  • Evaluation and Generalization
  • Improving Models
  • Unsupervised Learning
  • Responsible and Reliable ML
  • ML in Production

Course curriculum

8 modules · 24 lessons · follow them in order or jump to any topic

1 ML Foundations 3 lessons
  1. 1Machine Learning Introduction
  2. 2The End-to-End ML Workflow
  3. 3Statistics and Linear Algebra Essentials
2 Data Preparation 3 lessons
  1. 4NumPy Arrays and Vectorization
  2. 5Data Analysis with pandas
  3. 6Cleaning Data and Handling Missing Values
3 Supervised Learning 3 lessons
  1. 7Linear Regression
  2. 8Logistic Regression
  3. 9Decision Trees and Random Forests
4 Evaluation and Generalization 3 lessons
  1. 10Train, Validation, and Test Splits
  2. 11Cross-Validation
  3. 12Classification and Regression Metrics
5 Improving Models 3 lessons
  1. 13Overfitting and Regularization
  2. 14Feature Engineering and Preprocessing
  3. 15Hyperparameter Tuning
6 Unsupervised Learning 3 lessons
  1. 16Clustering with K-Means
  2. 17Dimensionality Reduction with PCA
  3. 18Anomaly Detection
7 Responsible and Reliable ML 3 lessons
  1. 19Data Leakage and Shortcut Learning
  2. 20Model Interpretation
  3. 21Fairness, Privacy, and Model Risk
8 ML in Production 3 lessons
  1. 22Reproducible Training Pipelines
  2. 23Serving Models and Monitoring Drift
  3. 24Capstone: Build a Churn Prediction System

About this course

This tutorial suits complete beginners as well as learners who already know some programming and want a structured reference. If Machine Learning with Python is new to you, start from the first lesson and work through each module in order. Experienced learners can use the curriculum above to jump directly to a specific concept.

How to get the most from it

  1. Read each lesson in order and run every example yourself.
  2. Change the examples to test your understanding instead of only copying them.
  3. Complete the exercises and review the common-mistake sections before moving on.
  4. Mark lessons complete as you go — your progress is saved in this browser.

Frequently asked questions

Is this Machine Learning with Python tutorial free?

Yes. Every lesson listed on this page is free to read and can be completed at your own pace.

Do I need previous experience?

No previous experience is required for the introductory lessons. Some advanced modules are easier if you first complete the fundamentals in this course.

How should I practise?

Type and run the examples, deliberately change their inputs, predict the result before execution and build a small project after completing each major module.