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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In this chapter, we continued working on the online advertising click-through prediction project. This time, we were able to train the classifier on the entire dataset with millions of records, with the help of the parallel computing tool Apache Spark. We discussed the basics of Spark, including its major components, the deployment of Spark programs, the programming essentials of PySpark, and the Python interface of Spark. Then, we programmed using PySpark to explore the click log data.

You learned how to perform one-hot encoding, cache intermediate results, develop classification solutions based on the entire click log dataset, and evaluate performance. In addition, I introduced two feature engineering techniques, feature hashing and feature interaction, in order to improve prediction performance. We had fun implementing them in PySpark as well.

Looking back on our learning journey, we have been working on classification problems since ...