Grokking Machine Learning

I am the author of Grokking Machine Learning, a book by Manning Editors. This is a book in which I explain the main algorithms and techniques of supervised learning in a very friendly and intuitive way that doesn’t require heavy mathematics or coding.

The book has code in Python, Numpy, Pandas, Scikit Learn, and PyTorch. You can find it in this repo.

Promo code!

With this promo code: serranoyt, you’ll get a 40% discount in the book!


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

%d bloggers like this:
search previous next tag category expand menu location phone mail time cart zoom edit close