Mahout Training
Specialize In Mahout That Bring Scalable Machine Learning Solutions with Best Practices
THE GOAL OF COURSE
The purpose of the Mahout Training Program is to enable participants to consolidate their knowledge in the their minds with experience sharing, example implementations and case studies, while providing detailed information on key concepts and techniques on Mahout
WHAT YOU WILL LEARN
- Learn the terminology and basic concepts behind Mahout Architecture and its components, selection criteria and best solution designs,
- Understand how you should prepare your Mahout solution to production,
- Specialize in the most appropriate cluster configurations and integrations with the hardware and systems within the data center,
- Learn the different types of Classification, Clustering, Vectorization and Feature Extraction Concepts in detail,
- Develop technical knowledge and competencies with workshops,
- Make a significant contribution to your development in Machine Learning and Big Data Analysis,
- Be able to find answers to your questions at the points where your head is installed while developing after the program
WHO BENEFITS
- Employees who are doing or planning to make a career in the Big Data place
- Leading teams in technology / IT functions
- Leaders responsible for Digital Transformation.
- Data Scientists and Data Analysts
- New Graduates and University Students
Become Master on Mahout Smartize Big Data Analysis, Learn Machine Learning Algoritms in Detail
Theories and practices you will see throughout the program will answer your questions on Mahout, also you will learn key techniques to develop the right strategy while implementing your projects
COURSE OUTLINE
- Module-1: Introduction to Mahout
- The History of Mahout
- Machine Learning Overview,
- Machine Learning Use Cases,
- Understanding Supervised and Unsupervised Learning Techniques,
- Introduction to Clustering and Classification,
- Similarity Metrics,
- Distance Measure Types: Euclidean, Cosine Measures, Manhattan etc.,
- Custom Distance Measure.
- Module-2: Clustering Techniques
- Clustering Use Cases,
- Types of Clustering Techniques,
- Understanding K-Means Clustering,
- Representing Data as a vector,
- Transforming Data into Vectors,
- Lucence Analyzer stop words,
- Understanding TF-IDF,
- N-gram Collocations,
- Normalization,
- K-Means Execution,
- Visualizing Clusters,
- Displaying K-Means with Cluster Dumper,
- Limitations of K-Means.
- Module-3 Advanced Clustering Techniques
- Introduction to Canopy Clustering,
- How Canopy Clustering Works,
- Clustering Techniques,
- Fuzzy K-Means Clustering Techniques,
- Topic Modeling and its Application,
- LDA and its Results,
- Clustering Quality Evaluation,
- Cluster Dumper,
- Distance Measure,
- Tuning Clustering Performance,
- Batch and Online Clustering.
- Lab: Canopy and Fuzzy K-Means Examples
- Module-4 Classification Techniques
- Introduction to Classification,
- Classification Applications and its Examples,
- Predictors and Target Variables,
- Feature Extraction,
- Vectorization,
- Classification Types,
- Entropy,
- Tuning the classifier,
- Implementing Algorithms,
- Naïve Bayers,
- K Nearest Neighbors,
- Random Forest,
- Decision Trees.
- Lab: Workshop with Naive Bayers, K Nearest Neighbors, Random Forest, Decision Trees Algoritms
- Module-5 Recommendation Systems
- Introduction to Recommendation,
- User – Based Recommendation,
- Item-Based Recommendation,
- Content –Based Recommendation,
- Creating a Recommender and it’s Applications,
- Recommender Evaluator,
- The Preference Object,
- Generic Data Models,
- Preference Array and Implementation,
- Selecting Compatible implementations.
- Module-6 Implementation of Recommender and Best Practices
- User-Based recommendation and its Examples,
- Exploring user neighborhoods & Threshold based neighborhood,
- Similarity Metrics,
- Pearson Correlation,
- Employing Weighting,
- Euclidean and Cosine similarities,
- Inferring preferences,
- Different Storage and Memory considerations,
- SVD –Singular Value Decomposition,
- Cluster based Recommendation,
- Recommender Comparisons,
- Recommender and its types,
- Creating web enabled Recommender,
- Co-Occurrence matrix ,
- Computing user vectors.
- Lab: You will learn Mahout in detail with lots of examples on sample datasets