This is the repo for the book "Grokking Machine Learning", available here.
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- What is machine learning?
- Types of machine learning
- Drawing a line close to our points: Linear regression (code)
- Optimizing the training process: Underfitting, overfitting, testing, and regularization (code)
- Using lines to split our points: The perceptron algorithm (code)
- A continuous approach to splitting points: Logistic classifiers (code)
- How do you measure classification models?: Accuracy and its friends
- Using probability to its maximum: The Naive Bayes model (code)
- Splitting data by asking questions: Decision trees (code)
- Combining building blocks to gain more power: Neural networks (code)
- Finding boundaries with style: Support vector machines and the kernel method (code)
- Combining models to maximize results: Ensemble learning (code)
- Putting it all in practice: A real life example of data engineering and machine learning (code)