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Machine Learning with Spark

You're reading from   Machine Learning with Spark Develop intelligent, distributed machine learning systems

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781785889936
Length 532 pages
Edition 2nd Edition
Languages
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Authors (2):
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Manpreet Singh Ghotra Manpreet Singh Ghotra
Author Profile Icon Manpreet Singh Ghotra
Manpreet Singh Ghotra
Rajdeep Dua Rajdeep Dua
Author Profile Icon Rajdeep Dua
Rajdeep Dua
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Up and Running with Spark FREE CHAPTER 2. Math for Machine Learning 3. Designing a Machine Learning System 4. Obtaining, Processing, and Preparing Data with Spark 5. Building a Recommendation Engine with Spark 6. Building a Classification Model with Spark 7. Building a Regression Model with Spark 8. Building a Clustering Model with Spark 9. Dimensionality Reduction with Spark 10. Advanced Text Processing with Spark 11. Real-Time Machine Learning with Spark Streaming 12. Pipeline APIs for Spark ML

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...
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