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

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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

Scaling Up Prediction to Terabyte Click Logs

In the previous chapter, we developed an ad click-through predictor using a logistic regression classifier. We proved that the algorithm is highly scalable by training efficiently on up to 1 million click log samples. In this chapter, we will further boost the scalability of the ad click-through predictor by utilizing a powerful parallel computing (or, more specifically, distributed computing) tool called Apache Spark.

This chapter will demystify how Apache Spark is used to scale up learning on massive data, as opposed to limiting model learning to one single machine. We will also use PySpark, which is the Python API, to explore click log data, to develop classification solutions based on the entire click log dataset, and to evaluate performance, all in a distributed manner. Aside from this, I will introduce two approaches to playing around with categorical features: one is related to hashing in computer science, while the other...