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.

The following are required for you to utilize this book:

You can use a 64-bit architecture, 2GHz CPU, and 8GB RAM to perform all the steps in this book. You will require at least 8GB of hard disk space.

This book is for anyone interested in entering data science with machine learning. Basic familiarity with Python is assumed.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The target_names key gives the newsgroups names."

Any command-line input or output is written as follows:

ls -1 enron1/ham/*.txt | wc -l 
3672
ls -1 enron1/spam/*.txt | wc -l
1500

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Heterogeneity Activity Recognition Data Set."

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