Book Image

Large Scale Machine Learning with Spark

By : Md. Rezaul Karim, Md. Mahedi Kaysar
Book Image

Large Scale Machine Learning with Spark

By: Md. Rezaul Karim, Md. Mahedi Kaysar

Overview of this book

<p>Data processing, implementing related algorithms, tuning, scaling up and finally deploying are some crucial steps in the process of optimising any application.</p> <p>Spark is capable of handling large-scale batch and streaming data to figure out when to cache data in memory and processing them up to 100 times faster than Hadoop-based MapReduce.This means predictive analytics can be applied to streaming and batch to develop complete machine learning (ML) applications a lot quicker, making Spark an ideal candidate for large data-intensive applications.</p> <p>This book focuses on design engineering and scalable solutions using ML with Spark. First, you will learn how to install Spark with all new features from the latest Spark 2.0 release. Moving on, you’ll explore important concepts such as advanced feature engineering with RDD and Datasets. After studying developing and deploying applications, you will see how to use external libraries with Spark.</p> <p>In summary, you will be able to develop complete and personalised ML applications from data collections,model building, tuning, and scaling up to deploying on a cluster or the cloud.</p>

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Large Scale Machine Learning with Spark
Md. Rezaul Karim | Md. Mahedi Kaysar
Oct, 2016 | 476 pages
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Table of Contents (16 chapters)
Large Scale Machine Learning with Spark
Large Scale Machine Learning with Spark
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.Packtpub.com
www.Packtpub.com
Preface
Preface
Free Chapter
1
Introduction to Data Analytics with Spark
Introduction to Data Analytics with Spark
Spark overview
New computing paradigm with Spark
Spark ecosystem
Spark machine learning libraries
Installing and getting started with Spark
Packaging your application with dependencies
Running a sample machine learning application
References
Summary
2
Machine Learning Best Practices
Machine Learning Best Practices
What is machine learning?
Machine learning tasks
Practical machine learning problems
Most widely used machine learning problems
Large scale machine learning APIs in Spark
Practical machine learning best practices
Choosing the right algorithm for your application
Summary
3
Understanding the Problem by Understanding the Data
Understanding the Problem by Understanding the Data
Analyzing and preparing your data
Resilient Distributed Dataset basics
Dataset basics
Dataset from string and typed class
Spark and data scientists workflow
Deeper into Spark
Summary
4
Extracting Knowledge through Feature Engineering
Extracting Knowledge through Feature Engineering
The state of the art of feature engineering
Best practices in feature engineering
Feature engineering with Spark
Advanced feature engineering
Summary
5
Supervised and Unsupervised Learning by Examples
Supervised and Unsupervised Learning by Examples
Machine learning classes
Supervised learning with Spark - an example
Unsupervised learning
Recommender system
Advanced learning and generalizations
Summary
6
Building Scalable Machine Learning Pipelines
Building Scalable Machine Learning Pipelines
Spark machine learning pipeline APIs
Cancer-diagnosis pipeline with Spark
Cancer-prognosis pipeline with Spark
Market basket analysis with Spark Core
OCR pipeline with Spark
Topic modeling using Spark MLlib and ML
Credit risk analysis pipeline with Spark
Scaling the ML pipelines
Tips and performance considerations
Summary
7
Tuning Machine Learning Models
Tuning Machine Learning Models
Details about machine learning model tuning
Typical challenges in model tuning
Evaluating machine learning models
Validation and evaluation techniques
Parameter tuning for machine learning models
Hypothesis testing
Machine learning model selection
Summary
8
Adapting Your Machine Learning Models
Adapting Your Machine Learning Models
Adapting machine learning models
The generalization of ML models
Adapting through incremental algorithms
Adapting through reusing ML models
Machine learning in dynamic environments
Summary
9
Advanced Machine Learning with Streaming and Graph Data
Advanced Machine Learning with Streaming and Graph Data
Developing real-time ML pipelines
Time series and social network analysis
Movie recommendation using Spark
Developing a real-time ML pipeline from streaming
ML pipeline on graph data and semi-supervised graph-based learning
Summary
10
Configuring and Working with External Libraries
Configuring and Working with External Libraries
Third-party ML libraries with Spark
Using external libraries with Spark Core
Time series analysis using the Cloudera Spark-TS package
Configuring SparkR with RStudio
Configuring Hadoop run-time on Windows
Summary

Most widely used machine learning problems

You will find an extensive amount of examples of the use of machine learning related problems in daily life, since they solve the difficult parts of the available problems that are widely used techniques or algorithms. We often use many desktop or web-based applications that solve your problems out of the data even without knowing that what underlying techniques have been used. You will be wondered to know that many of them actually use widely used machine learning algorithms to make your life easier. There are many machine learning problems around. Here we will mention some example problems that really represent what machine learning is all about:

  • Spam detection or spam filtering: Given some e-mails in an inbox, the task is to identify those e-mails that are spam and those that are non-spam (often called ham) e-mail messages. Now the challenging part is to develop an ML application that can be applied so that it can identify only the non-spam e...

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