I. The Fundamentals - ML

The first part of the book covers the following topics:

• What Machine Learning is, what problems it tries to solve, and the main categories and fundamental concepts of its systems

• The steps in a typical Machine Learning project

• Learning by fitting a model to data

• Optimizing a cost function

• Handling, cleaning and preparing data

• Selecting and engineering features

• Selecting a model and tuning hyperparameters using cross-validation

• The challenges of Machine Learning, in particular, underfitting and overfitting (the bias/variance trade-off)

• The most common learning algorithms: Linear and Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Ensemble methods

• Reducing the dimensionality of the training data to fight the “curse of dimensionality”

• Other unsupervised learning techniques, including clustering, density estimation, and anomaly detection