Preface

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).

With six new chapters, covering topics such as movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machines, 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 decision making, the book has been considerably updated for the latest enterprise requirements.

At the same time, the 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 such as TensorFlow and Keras.

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 natural language processing.

By the end of this 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 with Python to solve problems.