What this book covers
Chapter 1, The Fundamentals of Machine Learning, defines machine learning as the study and design of programs that improve their performance of a task by learning from experience. This definition guides the other chapters; in each, we will examine a machine learning model, apply it to a task, and measure its performance.
Chapter 2, Simple Linear Regression, discusses a model that relates a single feature to a continuous response variable. We will learn about cost functions and use the normal equation to optimize the model.
Chapter 3, Classification and Regression with K-Nearest Neighbors, introduces a simple, nonlinear model for classification and regression tasks.
Chapter 4, Feature Extraction, describes methods for ...
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