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Machine Learning with Apache Spark Quick Start Guide

You're reading from Machine Learning with Apache Spark Quick Start Guide Uncover patterns, derive actionable insights, and learn from big data using MLlib

Product type Paperback

Published in Dec 2018

Publisher Packt

ISBN-13 9781789346565

Length 240 pages

Edition 1st Edition

Languages

Java

Tools

Apache Spark

Concepts

Big Data

Author (1):

Jillur Quddus

View More author details

Table of Contents (10) Chapters

Preface

1. The Big Data Ecosystem FREE CHAPTER

2. Setting Up a Local Development Environment

3. Artificial Intelligence and Machine Learning

4. Supervised Learning Using Apache Spark

5. Unsupervised Learning Using Apache Spark

6. Natural Language Processing Using Apache Spark

7. Deep Learning Using Apache Spark

8. Real-Time Machine Learning Using Apache Spark

9. Other Books You May Enjoy

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Summary

In this chapter, we have installed, configured, and deployed a local analytical development environment consisting of a single-node Apache Spark 2.3.2 and Apache Kafka 2.0.0 cluster that will also allow us to interactively develop Spark applications using Python 3.6 via Jupyter Notebook.

In the next chapter, we will discuss some of the high-level concepts behind common artificial intelligence and machine learning algorithms, as well as introducing Apache Spark's machine learning library, MLlib!

The rest of the chapter is locked

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Authors (1)

Quddus

Quddus

Jillur Quddus is a lead technical architect, polyglot software engineer and data scientist with over 10 years of hands-on experience in architecting and engineering distributed, scalable, high-performance, and secure solutions used to combat serious organized crime, cybercrime, and fraud. Jillur has extensive experience of working within central government, intelligence, law enforcement, and banking, and has worked across the world including in Japan, Singapore, Malaysia, Hong Kong, and New Zealand. Jillur is both the founder of Keisan, a UK-based company specializing in open source distributed technologies and machine learning, and the lead technical architect at Methods, the leading digital transformation partner for the UK public sector.

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