Machine Learning with Mahout Certification Training Course Syllabus

Overview: This self-paced course provides hands-on experience with Apache Mahout on a real Hadoop environment, designed for big data professionals seeking to implement scalable machine learning solutions. The curriculum spans approximately 12 hours of structured learning, combining theory with practical implementation across eight focused modules. Learners will gain proficiency in deploying Mahout’s core algorithms for clustering, classification, and recommendation systems at scale, culminating in a complete pipeline deployment. Ideal for those transitioning into ML engineering roles with a Hadoop focus.

Module 1: Introduction to Apache Mahout

Estimated time: 1 hour

• Mahout history and evolution
• Understanding the Mahout ecosystem
• Core libraries and components
• Real-world use cases and applications

Module 2: Environment Setup & Data Ingestion

Estimated time: 1.5 hours

• Hadoop cluster fundamentals
• Installing and configuring Apache Mahout
• Interacting with HDFS for data storage
• Ingesting CSV data into Mahout workflows

Module 3: Data Preprocessing & Feature Engineering

Estimated time: 2 hours

• Text vectorization techniques
• Data normalization methods
• Handling sparse datasets
• Converting raw data into Mahout-compatible vector formats

Module 4: Collaborative Filtering

Estimated time: 2 hours

• User-based collaborative filtering
• Item-based collaborative filtering
• Similarity measures in Mahout
• Building and evaluating a movie recommendation engine

Module 5: Classification with Naive Bayes & Random Forest

Estimated time: 2.5 hours

• Probabilistic classification using Naive Bayes
• Random Forest for decision forests
• Training classifiers on large labeled datasets
• Model evaluation and performance metrics

Module 6: Clustering with K-Means & Canopy

Estimated time: 2 hours

• K-Means clustering algorithm
• Canopy clustering for initialization
• Selecting optimal number of clusters (k)
• Clustering product or user data and visualizing results

Module 7: Custom Algorithm Implementation

Estimated time: 1.5 hours

• Writing custom Mahout MapReduce jobs
• Extending Mahout APIs
• Implementing a custom mapper/reducer for a tailored algorithm

Module 8: Deployment & Optimization

Estimated time: 1.5 hours

• Tuning Mahout job performance
• Resource management in Hadoop YARN
• Monitoring and debugging Mahout workflows
• Deploying a full recommendation pipeline in production

Prerequisites

• Familiarity with Hadoop fundamentals
• Basic understanding of distributed computing concepts
• Experience with command-line and file system operations

What You'll Be Able to Do After

• Explain Mahout’s architecture and core components
• Implement scalable clustering, classification, and recommendation algorithms
• Perform large-scale data preprocessing and feature engineering
• Build and evaluate collaborative filtering and content-based recommenders
• Deploy and optimize Mahout jobs in a Hadoop YARN environment