Apache Mahout Syllabus

Introduction to Apache Mahout

Apache Mahout is a machine learning library designed to provide scalable algorithms for data mining and data analysis. This module introduces Apache Mahout, covering its core features, architecture, and use cases in machine learning and data analytics.

Setting Up Apache Mahout

Learn how to install and configure Apache Mahout. This section covers system requirements, installation procedures, and initial setup. Explore how to integrate Mahout with Hadoop and other data processing frameworks.

Mahout Algorithms and Models

Discover the various algorithms and models provided by Apache Mahout. Learn about recommendation algorithms, clustering algorithms, and classification algorithms. Understand how to select and apply different algorithms based on your data and use cases.

Working with Mahout’s Data Structures

Gain insights into Mahout’s data structures, such as vectors, matrices, and data sets. Learn how to work with these structures to prepare and analyze data. Explore how Mahout’s data structures support scalable and efficient computations.

Building Recommendation Systems

Understand how to build recommendation systems using Apache Mahout. Learn about collaborative filtering, content-based filtering, and hybrid recommendation approaches. Explore how to evaluate and optimize recommendation models.

Clustering and Classification

Learn about clustering and classification techniques in Apache Mahout. Explore different clustering algorithms like K-means and hierarchical clustering, and classification algorithms like Naive Bayes and decision trees. Understand how to apply these techniques to real-world problems.

Integrating Mahout with Big Data Technologies

Discover how to integrate Apache Mahout with big data technologies like Apache Hadoop and Apache Spark. Learn about data ingestion, processing, and analysis in a distributed environment. Explore how Mahout can leverage these technologies for large-scale data analysis.

Performance Tuning and Optimization

Explore performance tuning and optimization techniques for Apache Mahout. Learn how to improve the efficiency of algorithms, manage resources effectively, and handle large datasets. Understand best practices for optimizing Mahout applications.

Best Practices and Case Studies

Learn best practices for using Apache Mahout and review case studies of successful implementations. Explore common challenges and solutions, and gain insights into how Mahout can be applied to various industries and use cases.

Apache Mahout Syllabus

Key Features

  • Explain the architecture of the Apache Mahout component.
  • Configure and use new functionalities in Apache Mahout.
  • Use the standard Apache Mahout Sub Modules.
  • Explain the Apache Mahout Controlling Configuration and Customization options.

Introduction to Machine Learning and Mahout

  • Understanding Machine Learning
  • Overview of Apache Mahout
  • History of Mahout
  • Supervised and Unsupervised Learning Techniques
  • Mahout and Hadoop Integration
  • Introduction to Clustering and Classification

Apache Mahout and Hadoop

  • Mahout on Apache Hadoop
  • Setting Up Mahout and Myrrix

Recommendation Engine in Mahout Training

  • Recommendations Using Apache Mahout
  • Introduction to Recommendation Systems
  • Content-Based Mahout Optimizations

Implementing a Recommender and Recommendation Platform

  • User-Based Recommendation
  • User Neighborhood
  • 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)
  • Recommenders in Production

Clustering

  • Overview of Clustering Concepts
  • Common Clustering Algorithms in Apache Mahout
    • K-means
    • Canopy Clustering
    • Fuzzy K-means
    • Mean Shift
  • Representing Data Feature Selection
  • Vectorization in Apache Mahout
  • Representing Vectors
  • Clustering Documents (e.g., TF-IDF) and Implementing Clustering in Hadoop

Classification

  • Developing a Classifier
  • Examples and Terminology
    • Basic Predictor Variables
    • Target Variables
  • Common Algorithms
    • SGD
    • SVM
    • Naive Bayes
    • Random Forests
  • Training and Evaluating a Classifier

Apache Mahout and Amazon EMR

  • Mahout on Amazon EMR
  • EMR Mahout vs R
  • Introduction to Tools: Weka, Octave, Matlab, and SAS

Training

Basic Level Training

Duration : 1 Month

Advance Level Training

Duration : 1 Month

Project Level Training

Duration : 1 Month

Total Training Period

Duration : 3 Months

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