You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Summary

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 ...