Chapter 1, Getting Started with Python and Machine Learning, is the starting point for someone who is looking forward to enter the field of ML with Python. You will get familiar with the basics of Python and ML in this chapter and set up the software on your machine.
Chapter 2, Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms, explains important concepts such as getting the data, its features, and pre-processing. It also covers the dimension reduction technique, principal component analysis, and the k-nearest neighbors algorithm.
Chapter 3, Spam Email Detection with Naive Bayes, covers classification, naive Bayes, and its in-depth implementation, classification performance evaluation, model selection and tuning, and cross-validation. Examples such as spam e-mail detection are demonstrated.
Chapter 4, News Topic Classification with Support Vector Machine, covers multiclass classification, Support Vector Machine, and how it is applied in topic classification. Other important concepts, such as kernel machine, overfitting, and regularization, are discussed as well.
Chapter 5, Click-Through Prediction with Tree-Based Algorithms, explains decision trees and random forests in depth over the course of solving an advertising click-through rate problem.
Chapter 6, Click-Through Prediction with Logistic Regression, explains in depth the logistic regression classifier. Also, concepts such as categorical variable encoding, L1 and L2 regularization, feature selection, online learning, and stochastic gradient descent are detailed.
Chapter 7, Stock Price Prediction with Regression Algorithms, analyzes predicting stock market prices using Yahoo/Google Finance data and maybe additional data. Also, it covers the challenges in finance and brief explanations of related concepts.
Chapter 8, Best Practices, aims to foolproof your learning and get you ready for production.
After covering multiple projects in this book, the readers will have gathered a broad picture of the ML ecosystem using Python.
