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

Programming in PySpark

This section provides a quick introduction to programming with Python in Spark. We will start with the basic data structures in Spark.

Resilient Distributed Datasets (RDD) is the primary data structure in Spark. It is a distributed collection of objects and has the following three main features:

Resilient: When any node fails, affected partitions will be reassigned to healthy nodes, which makes Spark fault-tolerant
Distributed: Data resides on one or more nodes in a cluster, which can be operated on in parallel
Dataset: This contains a collection of partitioned data with their values or metadata

RDD was the main data structure in Spark before version 2.0. After that, it was replaced by the DataFrame, which is also a distributed collection of data but organized into named columns. DataFrames utilize the optimized execution engine of Spark SQL. Therefore, they are conceptually similar to a table in a relational...