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Apache Mahout Essentials

By : Jayani Withanawasam
Book Image

Apache Mahout Essentials

By: Jayani Withanawasam

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Apache Mahout Essentials
Jayani Withanawasam
Jun, 2015 | 164 pages
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Table of Contents (13 chapters)
Apache Mahout Essentials
Apache Mahout Essentials
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Introducing Apache Mahout
Introducing Apache Mahout
Machine learning in a nutshell
Machine learning applications
Machine learning libraries
The story so far
Apache Mahout
How Apache Mahout works?
From Hadoop MapReduce to Spark
When is it appropriate to use Apache Mahout?
Summary
2
Clustering
Clustering
Unsupervised learning and clustering
Applications of clustering
Types of clustering
K-Means clustering
Distance measure
K-Means clustering with MapReduce
Additional clustering algorithms
Text clustering
Optimizing clustering performance
Summary
3
Regression and Classification
Regression and Classification
Supervised learning
Predictive analytics' techniques
Classification versus regression
Linear regression with Apache Spark
Logistic regression with SGD
The Naïve Bayes algorithm
Hidden Markov Model
Summary
4
Recommendations
Recommendations
Collaborative versus content-based filtering
User-based recommenders
Item-based recommenders
Matrix factorization-based recommenders
Singular value decomposition
Summary
5
Apache Mahout in Production
Apache Mahout in Production
Introduction
Apache Mahout with Hadoop
Setting up Hadoop
Monitoring Hadoop
Setting up Mahout with Hadoop's fully-distributed mode
Troubleshooting Hadoop
Optimization tips
Summary
6
Visualization
Visualization
The significance of visualization in machine learning
D3.js
A visualization example for K-Means clustering
Summary
Index
Index

About the Author

Jayani Withanawasam is R&D engineer and a senior software engineer at Zaizi Asia, where she focuses on applying machine learning techniques to provide smart content management solutions.

She is currently pursuing an MSc degree in artificial intelligence at the University of Moratuwa, Sri Lanka, and has completed her BE in software engineering (with first class honors) from the University of Westminster, UK.

She has more than 6 years of industry experience, and she has worked in areas such as machine learning, natural language processing, and semantic web technologies during her tenure.

She is passionate about working with semantic technologies and big data.

First of all, I would like to thank the Apache Mahout contributors for the invaluable effort that they have put in the project, crafting it as a popular scalable machine learning library in the industry.

Also, I would like to thank Rafa Haro for leading me toward the exciting world of machine learning and natural language processing.

I am sincerely grateful to Shaon Basu, an acquisition editor at Packt Publishing, and Nikhil Potdukhe, a content development editor at Packt Publishing, for their remarkable guidance and encouragement as I wrote this book amid my other commitments.

Furthermore, my heartfelt gratitude goes to Abinia Sachithanantham and Dedunu Dhananjaya for motivating me throughout the journey of writing the book.

Last but not least, I am eternally thankful to my parents for staying by my side throughout all my pursuits and being pillars of strength.

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