Chapter 1, Classical Statistical Analysis, helps you apply your knowledge of Python and machine learning to create data models and perform statistical analysis. You will learn about various statistical learning techniques and learn how to apply them in data analysis.
Chapter 2, Introduction to Supervised Learning, discusses what's involved in machine learning and what it is all about. We start by discussing the principles involved in machine learning, with a particular focus on binary classification. Then, we will look at various techniques used when training models. Finally, we will look at some common metrics that people use to judge how well an algorithm is performing.
Chapter 3, Binary Prediction Models, looks at various methods for classifying data, focusing on binary data. We will see how we can extend algorithms for binary classification to algorithms that are capable of multiclass classification.
Chapter 4, Regression Analysis and How to Use It, covers a different variant of supervised learning. It focuses on different modes of linear regression and how to apply them for various purposes.
Chapter 5, Neural Networks, talks about classification and regression using neural networks. We will learn about perceptrons. We will also discuss the idea behind neural networks, including the different types of perceptrons, and what a multilayer perceptron is. You will also learn how to train a neural network for various purposes.
Chapter 6, Clustering Techniques, goes into detail about unsupervised learning. You'll learn about clustering and various approaches to clustering. You'll also learn how to implement those approaches for various purposes, such as image compression.
Chapter 7, Dimensionality Reduction, focuses on dimensionality reduction techniques. You will learn about various techniques, such as PCA, SVD, and MDS.
