This is the repo for the book "Grokking Machine Learning", available here.

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1. What is machine learning?
2. Types of machine learning
3. Drawing a line close to our points: Linear regression (code)
4. Optimizing the training process: Underfitting, overfitting, testing, and regularization (code)
5. Using lines to split our points: The perceptron algorithm (code)
6. A continuous approach to splitting points: Logistic classifiers (code)
7. How do you measure classification models?: Accuracy and its friends
8. Using probability to its maximum: The Naive Bayes model (code)
9. Splitting data by asking questions: Decision trees (code)
10. Combining building blocks to gain more power: Neural networks (code)
11. Finding boundaries with style: Support vector machines and the kernel method (code)
12. Combining models to maximize results: Ensemble learning (code)
13. Putting it all in practice: A real life example of data engineering and machine learning (code)