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Machine Learning Tutorial

Learn machine learning from your first model to real-world projects, step by step, in simple words. Every idea is shown with animations, real Python code and real results.

📚 85 lessons▶ 3:03:38+ of video💻 122 code examples🖼 92 screenshots
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The complete Machine Learning course, free on YouTubeWatch on YouTube ↗

What you will learn

  • How machines learn, and the full ML workflow
  • Linear and logistic regression, trees, forests and boosting
  • Clustering, PCA and working without labels
  • Evaluation, cross-validation, overfitting and metrics that matter
  • Feature engineering, pipelines and leakage
  • Explainability, tuning, deployment and a full capstone project
  • Quizzes, interview preparation and a cheat sheet

Course contents

Every lesson has its own page with the explanation, code you can copy, the real output, screenshots from the video, and a link that jumps straight to that moment in the video.

3. NumPy, pandas, and Charts

  1. NumPy in Ten Minutes14:56
  2. pandas for Real Data17:06
  3. Seeing the Data19:13

4. Linear Regression from Scratch

  1. The Simplest Model21:02
  2. Measuring Mistakes22:41
  3. Gradient Descent24:25

5. Real Prices, Real Metrics

  1. Many Features at Once28:13
  2. How Good Is It?29:52
  3. When the World Is Curved32:10

6. Overfitting and Honest Testing

  1. The Danger of Overfitting34:02
  2. Train, Test, and Honesty35:49
  3. Regularisation37:51

8. Cleaning Messy Data

  1. Missing Values46:17
  2. Outliers48:37
  3. Feature Scaling50:45

10. Classification and Metrics

  1. Logistic Regression1:00:05
  2. Why Accuracy Lies1:02:03
  3. Choosing the Threshold1:04:53

11. More Classifiers

  1. Rare Classes1:06:58
  2. More Than Two Classes1:09:07
  3. K-Nearest Neighbours1:10:42
  4. Naive Bayes1:12:47

12. Decision Trees and Forests

  1. Decision Trees1:15:19
  2. Random Forests1:18:12

13. Gradient Boosting

  1. Gradient Boosting1:20:33
  2. Tuning Boosting1:23:01
  3. XGBoost and LightGBM1:25:08

14. Support Vector Machines

  1. Support Vector Machines1:27:22
  2. The Kernel Trick1:29:10
  3. Choosing a Model1:31:22

15. Finding Groups

  1. K-Means Clustering1:33:12
  2. How Many Groups?1:35:00
  3. Hierarchical Clustering1:37:17
  4. DBSCAN1:39:09

16. Reducing Dimensions

  1. PCA1:41:23
  2. Seeing High Dimensions1:43:53
  3. Anomaly Detection1:45:35
  4. Recommendation Systems1:47:44

17. Tuning and Explaining

  1. Hyperparameter Tuning1:49:34
  2. Nested Cross Validation1:51:47
  3. Why Did It Decide That?1:53:28

18. Fairness, Time, and Text

  1. Fairness in Machine Learning1:56:25
  2. Time Series Basics1:58:37
  3. Forecasting with Features2:00:08
  4. Text as Data2:02:14

19. Shipping Your Model

  1. Saving Your Model2:04:02
  2. Serving with an API2:05:32
  3. A Demo App in Minutes2:07:35
  4. Why Models Decay2:09:07

20. The Maths You Actually Need

  1. Vectors and Matrices2:11:12
  2. Slopes and Derivatives2:12:51
  3. Probability and Statistics2:14:27

25. Real-World Situations

  1. Learning with Few Labels2:38:03
  2. AutoML and Big Data2:39:48
  3. Ten Costly Mistakes2:41:43

28. Myths, Jargon, and Your Roadmap

  1. Myths and Jargon3:02:11
  2. Your Roadmap3:03:38