Master Apache Mahout with ENCODE-IT’s Comprehensive Online Course

Unlock the potential of machine learning and data science with ENCODE-IT’s Apache Mahout
course! Apache Mahout is an open-source machine learning library built on top of Hadoop, designed
to provide scalable algorithms for clustering, classification, and collaborative filtering. Whether
you're a data scientist, machine learning engineer, or business analyst, this course will give you
hands-on experience with Mahout's powerful algorithms and tools for building machine learning
models on big data platforms.

Course Overview
Apache Mahout provides scalable machine learning algorithms for a variety of tasks, including
classification, regression, clustering, and recommendation. The course starts with an introduction to
the Mahout framework, its integration with Hadoop, and the core concepts behind building scalable
machine learning models. You’ll explore supervised and unsupervised learning techniques, diving
deep into Mahout’s implementations of algorithms like K-Means Clustering, Naive Bayes
Classification, and Collaborative Filtering.

By the end of this course, you’ll not only understand Mahout’s core capabilities but also be able to
implement and tune machine learning algorithms to solve complex problems in real-world scenarios.
You’ll gain hands-on experience in building end-to-end machine learning pipelines on big data
platforms.

Salary Scale in India
With the increasing demand for machine learning solutions in various industries, professionals
skilled in Apache Mahout can expect competitive salaries. Entry-level Data Scientists and Machine
Learning Engineers with expertise in Mahout typically earn between ₹6 lakhs and ₹12 lakhs
annually. Experienced professionals, such as Machine Learning Architects and Big Data Engineers,
can earn upwards of ₹18 lakhs per year. As more companies leverage big data technologies, the
demand for professionals skilled in Apache Mahout and related technologies continues to grow,
opening up lucrative career opportunities.

Placement Assistance & Certification in India
Upon completing the Apache Mahout course, ENCODE-IT offers placement assistance to help you
land your dream job in the field of machine learning and big data. You will also receive a Certificate
of Completion from ENCODE-IT, demonstrating your expertise in Apache Mahout and making you a
valuable asset to prospective employers. Our dedicated placement support ensures that you are
ready to take on machine learning roles in top tech companies and startups.

Course Curriculum

1. Introduction to Apache Mahout and Machine Learning
o What is Machine Learning? Understanding the Basics

o Overview of Apache Mahout and its Integration with Hadoop
o Mahout’s Role in the Big Data Ecosystem
o Introduction to Mahout’s Core Algorithms and Data Structures
o Setting Up Apache Mahout on a Hadoop Cluster

2. Data Preparation and Preprocessing for Machine Learning
o Importance of Data Cleaning and Preprocessing in Machine Learning
o Techniques for Data Normalization, Transformation, and Feature Scaling
o Handling Missing Data and Outliers in Big Data
o Using Mahout's Data Formats and Working with Sparse Matrices
o Data Ingestion into Mahout: Converting Data for Machine Learning Tasks

3. Supervised Learning with Apache Mahout
o Introduction to Supervised Learning: Classification and Regression
o Naive Bayes Classifier in Mahout: Building Text Classification Models
o Logistic Regression for Predictive Modeling with Mahout
o Decision Trees and Random Forests for Supervised Learning
o Model Evaluation and Validation: Cross-Validation and Confusion Matrices

4. Unsupervised Learning with Apache Mahout
o Introduction to Unsupervised Learning: Clustering and Dimensionality Reduction
o K-Means Clustering in Mahout: Grouping Similar Data Points
o Gaussian Mixture Models for Clustering in Mahout
o Principal Component Analysis (PCA) for Dimensionality Reduction
o Hierarchical Clustering and its Application in Mahout

5. Collaborative Filtering and Recommender Systems
o Introduction to Collaborative Filtering in Machine Learning
o Building Recommender Systems with Apache Mahout
o User-Based and Item-Based Collaborative Filtering Algorithms
o Matrix Factorization and Singular Value Decomposition (SVD) in Mahout
o Evaluating Recommender Systems: Precision, Recall, and RMSE

6. Dimensionality Reduction and Feature Selection
o Techniques for Reducing the Number of Features in Machine Learning Models
o Principal Component Analysis (PCA) for Feature Extraction

o Independent Component Analysis (ICA) and its Applications
o Using Mahout for Feature Selection and its Impact on Model Performance
o Advanced Techniques for Feature Engineering and Data Representation

7. Optimizing Mahout Models for Performance
o Understanding Model Tuning and Hyperparameter Optimization
o Grid Search and Random Search for Tuning Mahout Algorithms
o Parallelizing Mahout Models for Performance Boosts
o Working with Mahout’s Distributed Algorithms on Hadoop and Spark
o Best Practices for Scaling Mahout Algorithms on Large Datasets

8. Advanced Machine Learning Techniques with Apache Mahout
o Deep Learning Overview and its Integration with Mahout
o Working with Mahout's Spark-based Machine Learning Algorithms
o Understanding Mahout's support for Matrix Factorization and Matrix Decomposition
o Integrating Apache Mahout with Other Machine Learning Libraries
o Ensemble Learning Techniques in Apache Mahout

9. Deploying Machine Learning Models Built with Mahout
o Building and Saving Trained Models in Mahout
o Integrating Trained Models into Production Systems
o Real-Time Predictions and Inference with Mahout
o Monitoring and Maintaining Machine Learning Models
o Creating an End-to-End Machine Learning Pipeline with Mahout

10. Real-World Projects and Use Cases
o Building a Recommender System for an E-commerce Website
o Implementing K-Means Clustering on Customer Segmentation Data
o Developing a Spam Email Classifier using Naive Bayes with Mahout
o Building a Predictive Maintenance Model for Industrial Equipment
o Analyzing and Visualizing Data with Machine Learning Algorithms in Mahout

11. Final Project and Certification Exam
o Final Project: Building a Complete Machine Learning Solution using Apache Mahout
o Implementing Clustering, Classification, and Recommendation Models
o Final Exam: Comprehensive Assessment of Apache Mahout Skills

o Certification of Completion from ENCODE-IT and Job Placement Assistance

Why Choose ENCODE-IT for Apache Mahout Training?
ENCODE-IT offers a hands-on, in-depth approach to learning Apache Mahout, enabling you to
master the art of scalable machine learning. From basic algorithms to advanced techniques, our
expert instructors guide you through the practical implementation of Mahout on real-world data.
You will gain experience working with Mahout’s core machine learning algorithms, while also
learning the tools and techniques to optimize and scale your models for big data.

Our focus on real-world projects, placement assistance, and industry-relevant skills ensures that
you will be ready to take on the challenges of machine learning roles in top companies. Whether
you're looking to advance your career in data science, machine learning, or big data technologies,
ENCODE-IT’s Apache Mahout course will help you achieve your goals and open up lucrative career
opportunities in this fast-growing field.