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Grokking Machine Learning Book Repository

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

Get it with a 40% discount code: serranopc

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Chapters:

  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)

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Repository for the book Grokking Machine Learning, by Manning Editors

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