Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Apache Mahout Essentials
  • Table Of Contents Toc
Apache Mahout Essentials

Apache Mahout Essentials

By : Jayani Withanawasam
3.7 (3)
Buy this Book
close
close
Apache Mahout Essentials

Apache Mahout Essentials

3.7 (3)
By: Jayani Withanawasam
Buy this Book

Overview of this book

If you are a Java developer or data scientist, haven't worked with Apache Mahout before, and want to get up to speed on implementing machine learning on big data, then this is the perfect guide for you.
Table of Contents (8 chapters)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. Introducing Apache Mahout
chevron up
Icon
1. Introducing Apache Mahout
Icon
Machine learning in a nutshell
Icon
Machine learning applications
Icon
Machine learning libraries
Icon
The story so far
Icon
Apache Mahout
Icon
How Apache Mahout works?
Icon
From Hadoop MapReduce to Spark
Icon
When is it appropriate to use Apache Mahout?
Icon
Summary
2
2. Clustering
Icon
2. Clustering
Icon
Unsupervised learning and clustering
Icon
Applications of clustering
Icon
Types of clustering
Icon
K-Means clustering
Icon
Distance measure
Icon
K-Means clustering with MapReduce
Icon
Additional clustering algorithms
Icon
Text clustering
Icon
Optimizing clustering performance
Icon
Summary
3
3. Regression and Classification
Icon
3. Regression and Classification
Icon
Supervised learning
Icon
Predictive analytics' techniques
Icon
Classification versus regression
Icon
Linear regression with Apache Spark
Icon
Logistic regression with SGD
Icon
The Naïve Bayes algorithm
Icon
Hidden Markov Model
Icon
Summary
4
4. Recommendations
Icon
4. Recommendations
Icon
Collaborative versus content-based filtering
Icon
User-based recommenders
Icon
Item-based recommenders
Icon
Matrix factorization-based recommenders
Icon
Singular value decomposition
Icon
Summary
5
5. Apache Mahout in Production
Icon
5. Apache Mahout in Production
Icon
Introduction
Icon
Apache Mahout with Hadoop
Icon
Setting up Hadoop
Icon
Monitoring Hadoop
Icon
Setting up Mahout with Hadoop's fully-distributed mode
Icon
Troubleshooting Hadoop
Icon
Optimization tips
Icon
Summary
6
6. Visualization
Icon
6. Visualization
Icon
The significance of visualization in machine learning
Icon
D3.js
Icon
A visualization example for K-Means clustering
Icon
Summary
7
Index
Icon
Index

Apache Mahout

In this section, we will have a quick look at Apache Mahout.

Do you know how Mahout got its name?

Apache Mahout

As you can see in the logo, a mahout is a person who drives an elephant. Hadoop's logo is an elephant. So, this is an indicator that Mahout's goal is to use Hadoop in the right manner.

The following are the features of Mahout:

  • It is a project of the Apache software foundation
  • It is a scalable machine learning library
    • The MapReduce implementation scales linearly with the data
    • Fast sequential algorithms (the runtime does not depend on the size of the dataset)
  • It mainly contains clustering, classification, and recommendation (collaborative filtering) algorithms
  • Here, machine learning algorithms can be executed in sequential (in-memory mode) or distributed mode (MapReduce is enabled)
  • Most of the algorithms are implemented using the MapReduce paradigm
  • It runs on top of the Hadoop framework for scaling
  • Data is stored in HDFS (data storage) or in memory
  • It is a Java library (no user interface!)
  • The latest released version is 0.9, and 1.0 is coming soon
  • It is not a domain-specific but a general purpose library

Note

For those of you who are curious! What are the problems that Mahout is trying to solve? The following problems that Mahout is trying to solve:

The amount of available data is growing drastically.

The computer hardware market is geared toward providing better performance in computers. Machine learning algorithms are computationally expensive algorithms. However, there was no framework sufficient to harness the power of hardware (multicore computers) to gain better performance.

The need for a parallel programming framework to speed up machine learning algorithms.

Mahout is a general parallelization for machine learning algorithms (the parallelization method is not algorithm-specific).

No specialized optimizations are required to improve the performance of each algorithm; you just need to add some more cores.

Linear speed up with number of cores.

Each algorithm, such as Naïve Bayes, K-Means, and Expectation-maximization, is expressed in the summation form. (I will explain this in detail in future chapters.)

For more information, please read Map-Reduce for Machine Learning on Multicore, which can be found at http://www.cs.stanford.edu/people/ang/papers/nips06-mapreducemulticore.pdf.

Setting up Apache Mahout

Download the latest release of Mahout from https://mahout.apache.org/general/downloads.html.

If you are referencing Mahout as a Maven project, add the following dependency in the pom.xml file:

<dependency>
  <groupId>org.apache.mahout</groupId>
  <artifactId>mahout-core</artifactId>
  <version>${mahout.version}</version>
</dependency>

If required, add the following Maven dependencies as well:

<dependency>
  <groupId>org.apache.mahout</groupId>
  <artifactId>mahout-math</artifactId>
  <version>${mahout.version}</version>
</dependency>
<dependency>
  <groupId>org.apache.mahout</groupId>
  <artifactId>mahout-integration</artifactId>
  <version>${mahout.version}</version>
</dependency>

Tip

Downloading the example code

You can download the example code files from your account at http://www.packtpub.com for all the Packt Publishing books you have purchased. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

More details on setting up a Maven project can be found at http://maven.apache.org/.

Follow the instructions given at https://mahout.apache.org/developers/buildingmahout.html to build Mahout from the source.

The Mahout command-line launcher is located at bin/mahout.

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Apache Mahout Essentials
End of Section 1
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon