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

Learning the essentials of Apache Spark

Apache Spark is a distributed cluster computing framework designed for fast and general-purpose computation. It is an open-source technology originally developed by Berkeley's AMPLab at the University of California. It provides an easy-to-use interface for programming interactive queries and stream processing data. What makes it a popular big data analytics tool is its implicit data parallelism, where it automates operations on data in parallel across processors in the computing cluster. Users only need to focus on how they want to manipulate the data, without worrying about how it is distributed among all the computing nodes or which part of the data a node is responsible for.

Bear in mind that this book is mainly about machine learning. Hence, we will only briefly cover the fundamentals of Spark, including its components, installation, deployment, data structure, and core programming.