Book Image

Apache Mahout Essentials

By : Jayani Withanawasam
Book Image

Apache Mahout Essentials

By: Jayani Withanawasam

Overview of this book

Related Content you might be interested in

Current Title:

Small book image
Apache Mahout Essentials
Jayani Withanawasam
Jun, 2015 | 164 pages
No titles found
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

The story so far

The following timeline will give you an idea about the way machine learning has evolved and the maturity of the available machine learning libraries. Also, it is evident that even though machine learning was found in 1952, popular machine learning libraries have begun to evolve very recently.

Previous Section
End of Section 5
Next Section