Packt+ | Advance your knowledge in tech

You're reading from Apache Spark 2.x Machine Learning Cookbook Over 100 recipes to simplify machine learning model implementations with Spark

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781783551606

Length 666 pages

Edition 1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Machine Learning

Authors (5):

Broderick Hall

Meenakshi Rajendran

Shuen Mei

Mohammed Guller

Siamak Amirghodsi

+1 more

View More author details

Table of Contents (14) Chapters

Preface

1. Practical Machine Learning with Spark Using Scala FREE CHAPTER

2. Just Enough Linear Algebra for Machine Learning with Spark

3. Spark's Three Data Musketeers for Machine Learning - Perfect Together

4. Common Recipes for Implementing a Robust Machine Learning System

5. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I

6. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II

7. Recommendation Engine that Scales with Spark

8. Unsupervised Clustering with Apache Spark 2.0

9. Optimization - Going Down the Hill with Gradient Descent

10. Building Machine Learning Systems with Decision Tree and Ensemble Models

11. Curse of High-Dimensionality in Big Data

12. Implementing Text Analytics with Spark 2.0 ML Library

13. Spark Streaming and Machine Learning Library

Introduction

Understanding how optimization works is fundamental for a successful career in machine learning. We picked the Gradient Descent (GD) method for an end-to-end deep dive to demonstrate the inner workings of an optimization technique. We will develop the concept using three recipes that walk the developer from scratch to a fully developed code to solve an actual problem with real-world data. The fourth recipe explores an alternative to GD using Spark and normal equations (limited scaling for big data problems) to solve a regression problem.

Let's get started. How does a machine learn anyway? Does it really learn from its mistakes? What does it mean when the machine finds a solution using optimization?

At a high level, machines learn based on one of the following five techniques:

Error based learning: In this technique, we search the domain space for a combination of parameter values (weights) that minimize the total error (predicted versus actual) over the training data.
Information...