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Apache Mahout Course

Apache Mahout is an Apache TLP project to build powerful scalable machine learning tools for use on analyzing big-data on distributed manner.

Apache Mahout

Apache Mahout Training Course Introduction

Apache Mahout is an Apache TLP project to build powerful scalable machine learning tools for use on analyzing big-data on distributed manner.

Machine learning is the discipline of artificial intelligence that enables to learn on data, spam filtering and natural language processing.

Apache mahout enables clustering, dimensionality reduction and miscellaneous, being practically applied by Facebook, LinkedIn and twitter.

Mahout training explains the key concepts Collaborative filtering, Clustering and Categorization and how to implement scalable machine learning technique using Apache mahout.

The Mahout training sessions, explain how to setup Apache mahout cluster, set up stack and grouping

Apache Mahout Training Curriculum

Introduction to Machine Learning and Mahout

In Mahout Training you will know what is machine learning, what is Apache mahout and what is clustering.

• Machine Learning Fundamentals
• Apache Mahout Basics
• History of Mahout
• Supervised and Unsupervised Learning techniques
• Mahout and Hadoop
• Introduction to Clustering and Classification.

Apache Mahout and Hadoop

• Myrrix is a recommendation engine based on mahout, therefore this module is designed for mahout training and myrrix.

• Mahout on Apache Hadoop
• Setup Mahout and Myrrix.

Recommendation Engine in Mahout Training

This module will focus on Recommendation algorithm and Mahout optimizations.
• Recommendations using Apache Mahout
• Introduction to Recommendation systems
• Content Based Mahout Optimizations.

Implementing a Recommender and Recommendation Platform

Understanding the various recommendations, implementing Recommendors, different types of similarities in Apache mahout.

• User based recommendation
• User Neighbourhood
• Item based Recommendation
• Implementing a Recommender using MapReduce Platforms
• Similarity Measures
• Manhattan Distance
• Euclidean Distance
• Cosine Similarity
• Pearson’s Correlation Similarity
• Log likelihood Similarity
• Tanimoto Evaluating
• Recommendation Engines (Online and Offline)
• Recommendors in Production.

Clustering

This module of Mahout training is designed to give you thoroughly over the clustering concepts.

• Clustering
• Common Clustering Algorithms in Apache mahout training
• K-means Canopy Clustering
• Fuzzy K-means and Mean Shift etc.
• Representing Data Feature Selection
• Vectorization in Apache Mahout training
• Representing Vectors
• Clustering documents through example TF-IDF and Implementing clustering in Hadoop Classification.

Classification

By end of this Mahout training module , you will be able to develop a classifier on your own.

• Basic Predictor variables and Target variables
• Common Algorithms
• SGD
• SVM
• Navie Bayes
• Random Forests
• Training and evaluating a Classifier
• Developing a Classifier

Apache Mahout and Amazon EMR

We’ll focus on Apache Mahout and Amazon EMR, have an overview on Weka, Octave Matlab and SAS.

• Mahout on Amazon
• EMR Mahout Vs R
• Introduction to tools like Weka, Octave, Matlab and SAS

Want to have a course urgently or on Fast track. We can arrange you for a specialised training aimed only for you. Please get in touch with us with your requirements by mail or just fill in the Batch Enquiry form. We will get in touch with you with the slot times and other details with in 24 hours

For Priority Training contact below
  • eITCafe: info@eitcafe.com
  • India: 040 6678 6677
  • US: 630-636-0198

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For further information, please email jobsupport@eitcafe.com our Support and Training Manager.

Job Preparation

• Assistance with learning job seeking skills
• Resume creation
• Master application completion
• Dressing for success
• Job interview preparation

Job Development

• Assistance with completing applications online or in person
• Job development online, on foot, networking events, job fairs and established employer relationships to locate available positions in your job goal
• Job leads and information on attending hiring events
• Follow-ups on applications placed to request interviews.

What is Apache Mahout?

Apache™ Mahout is a library of scalable machine-learning algorithms, implemented on top of Apache Hadoop® and using the MapReduce paradigm. Machine learning is a discipline of artificial intelligence focused on enabling machines to learn without being explicitly programmed, and it is commonly used to improve future performance based on previous outcomes.
Once big data is stored on the Hadoop Distributed File System (HDFS), Mahout provides the data science tools to automatically find meaningful patterns in those big data sets. The Apache Mahout project aims to make it faster and easier to turn big data into big information.

What does Apache Mahout do?

Mahout supports four main data science use cases:

  • Collaborative filtering – mines user behavior and makes product recommendations (e.g. Amazon recommendations)
  • Clustering – takes items in a particular class (such as web pages or newspaper articles) and organizes them into naturally occurring groups, such that items belonging to the same group are similar to each other
  • Classification – learns from existing categorizations and then assigns unclassified items to the best category
  • Frequent item-set mining – analyzes items in a group (e.g. items in a shopping cart or terms in a query session) and then identifies which items typically appear together

What is the History of Apache Mahout? When did it start?

The Mahout project was started by several people involved in the Apache Lucene (open source search) community with an active interest in machine learning and a desire for robust, well-documented, scalable implementations of common machine-learning algorithms for clustering and categorization. The community was initially driven by Ng et al.’s paper “Map-Reduce for Machine Learning on Multicore” (see Resources) but has since evolved to cover much broader machine-learning approaches. Mahout also aims to:

  • Build and support a community of users and contributors such that the code outlives any particular contributor’s involvement or any particular company or university’s funding.
  • Focus on real-world, practical use cases as opposed to bleeding-edge research or unproven techniques.
  • Provide quality documentation and examples.

How is it different from doing machine learning in R or SAS?

Unless you are highly proficient in Java, the coding itself is a big overhead. There’s no way around it, if you don’t know it already you are going to need to learn Java and it’s not a language that flows! For R users who are used to seeing their thoughts realized immediately the endless declaration and initialization of objects is going to seem like a drag. For that reason I would recommend sticking with R for any kind of data exploration or prototyping and switching to Mahout as you get closer to production.

What is the Roadmap for Apache Mahout version 1.0?

The next major version, Mahout 1.0, will contain major changes to the underlying architecture of Mahout, including:

  • Scala: In addition to Java, Mahout users will be able to write jobs using the Scala programming language. Scala makes programming math-intensive applications much easier as compared to Java, so developers will be much more effective.
  • Spark & h2o: Mahout 0.9 and below relied on MapReduce as an execution engine. With Mahout 1.0, users can choose to run jobs either on Spark or h2o, resulting in a significant performance increase.

Key Features

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Overview of Course and Learning analytic’s

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Learn from Certified and Expert Trainers

Customized Course as per your requirement

24/7 online support for the course learners

High Quality E-learning Content for learning

videos

Access to the Recorded Sessions and classes

Flexible Course timing and Payment terms

Live Practical Oriented Approach for learners

Course Curriculam

Apache Mahout Course Module

Apache Mahout is an open source project that is primarily used in producing scalable machine learning

algorithms. This brief tutorial provides a quick introduction to Apache Mahout and explains how it can

be applied to make recommendations and organize documents in more useable clusters.

 

A mahout is one who drives an elephant as its master. The name comes from its close association with

Apache Hadoop which uses an elephant as its logo.

Hadoop is an open-source framework from Apache that allows to store and process big data in a

distributed environment across clusters of computers using simple programming models.

Apache Mahout is an open source project that is primarily used for creating scalable machine

learning

Duration: 45 Days

Support: 24×7

Video: Yes

Apache Mahout Certified Professional