Machine Learning with Apache Spark Quick Start Guide

By : Quddus

Buy this Book

Machine Learning with Apache Spark Quick Start Guide

By: Quddus

Buy this Book

Overview of this book

Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

The Big Data Ecosystem

A brief history of data

Big data ecosystem

Summary

Setting Up a Local Development Environment

CentOS Linux 7 virtual machine

Summary

Artificial Intelligence and Machine Learning

Artificial intelligence

Machine learning

Deep learning

NLP

Cognitive computing

Machine learning pipelines in Apache Spark

Summary

Supervised Learning Using Apache Spark

Linear regression

Logistic regression

Classification and Regression Trees

Summary

Unsupervised Learning Using Apache Spark

Clustering

Principal component analysis

Summary

Natural Language Processing Using Apache Spark

Feature transformers

Feature extractors

Case study – sentiment analysis

Summary

Deep Learning Using Apache Spark

Artificial neural networks

Summary

Real-Time Machine Learning Using Apache Spark

Distributed streaming platform

Distributed stream processing engines

Stream processing pipeline

Summary

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Distributed stream processing engines

Apache Kafka allows us to move real-time data reliably between systems and applications. But we still need some sort of processing engine to process and transform that real-time data in order ultimately to derive value from it based on the use case in question. Fortunately, there are a number of stream processing engines available to allow us to do this, including—but not limited—to the following:

Apache Spark: https://spark.apache.org/
Apache Storm: http://storm.apache.org/
Apache Flink: https://flink.apache.org/
Apache Samza: http://samza.apache.org/
Apache Kafka (via its Streams API): https://kafka.apache.org/documentation/
KSQL: https://www.confluent.io/product/ksql/

Though a detailed comparison of the available stream processing engines is beyond the scope of this book, you are encouraged to explore the preceding links...