Book Image

Python Machine Learning By Example - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
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Setting up the working environment

Let's get started with setting up the working environment, including PyTorch as the main framework, and OpenAI Gym, the toolkit that gives you a variety of environments to develop your learning algorithms on.

Installing PyTorch

PyTorch ( is a trendy machine learning library developed on top of Torch ( by Facebook's AI lab. It provides powerful computational graphs and high compatibility to GPUs, as well as a simple and friendly interface. PyTorch is expanding quickly in academia and it has seen heavy adoption by more and more companies. The following chart (taken from shows the growth of PyTorch at top machine learning conferences:

Figure 14.1: Number of PyTorch papers in top machine learning conferences

In the past year, there have been more mentions of PyTorch than TensorFlow at those conferences. Hopefully, you are motivated enough to...