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Math for Machine Learning

A machine learning user needs to have a fair understanding of machine learning concepts and algorithms. Familiarity with mathematics is an important aspect of machine learning. We learn to program by understanding the fundamental concepts and constructs of a language. Similarly, we learn machine learning by understanding concepts and algorithms using Mathematics, which is used to solve complex computational problems, and is a discipline for understanding and appreciating many computer science concepts. Mathematics plays a fundamental role in grasping theoretical concepts and in choosing the right algorithm. This chapter covers the basics of linear algebra and calculus for machine learning.

In this chapter, we will cover the following topics:

Linear algebra
Environment setup
- Setting up the Scala environment in Intellij
- Setting up the Scala environment on the command line
Fields
Vectors...

Key benefits

Get to the grips with the latest version of Apache Spark

Utilize Spark's machine learning library to implement predictive analytics

Leverage Spark’s powerful tools to load, analyze, clean, and transform your data

Description

This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.

What you will learn

Get hands-on with the latest version of Spark ML

Create your first Spark program with Scala and Python

Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2

Access public machine learning datasets and use Spark to load, process, clean, and transform data

Use Spark s machine learning library to implement programs by utilizing well-known machine learning models

Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models

Write Spark functions to evaluate the performance of your machine learning models

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

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Apache Spark 2.x Machine Learning Cookbook

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UJJAWAL SINHA Aug 15, 2017

Great Book! I recommend to buy it if you want to know practical guide on Machine Learning using spark 2.0 and scala .

Amazon Verified review

Amazon Customer Aug 07, 2017

Perfect book for someone who is just starting his adventure with Spark and Machine Learning. The author has very didactic approach that guides the reader step by step from understanding first the basics, installing the environment, implementing and training models, evaluating, etc. I strongly recommend this book.

PJG Mar 24, 2015

This book is a nice introduction to using the Apache Spark framework. It assumes no prior knowledge of either Hadoop, Spark or machine learning itself (although the latter is covered at quite a rapid pace in places so some background would likely be helpful!). The code examples are presented in Python and (mainly) Scala, with examples that are reasonably well-described.The overall tone of the book is clear and the chapters progress in a logical order, with a fairly rapid journey through the main machine learning techniques from a Spark perspective. Later chapters were particularly interesting, covering text mining and more complex methods (e.g. feature hashing).Some of the example data sets feel a little 'tired' (movie ratings data yet again - or perhaps I've just read too many machine learning books), but otherwise this is a good book and comes recommended.

Ebot Tabi Aug 24, 2015

This book gives a great introduction to using the Apache Spark framework, a goto for anyone who wishes to learn how to use Apache Spark framework. It assumes no prior knowledge of either Hadoop or Spark. The code examples are presented in Python and Scala are well-described.The overall the book is clearly structured and the chapters progress in a logical order, with a fairly rapid introduction to machine learning techniques from a Spark perspective.I definitely do recommend this book for beginners.

Mukesh Rao Apr 22, 2017

Did not find as useful as ML in R by Bret Lantz

Machine Learning with Spark: Develop intelligent, distributed machine learning systems , Second Edition

What do you get with Print?

Machine Learning with Spark

Math for Machine Learning

Linear algebra

Setting up the Scala environment in Intellij

Gradient descent

Prior, likelihood, and posterior

Calculus

Differential calculus

Plotting

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