This course is designed to help you master Apache Mahout, a robust open-source framework for building scalable machine learning models. It focuses on solving complex big data challenges and creating powerful recommendation systems, classification models, and clustering techniques. You will learn to harness the power of Mahout with Hadoop and Spark to process and analyze large datasets effectively.
Specialized in handling big data with Hadoop and Spark integration for ML models.
Focus on real-world scenarios, like recommendation systems and data clustering.
Learn to implement distributed ML algorithms for large-scale applications.
Overview of Apache Mahout
Features and benefits of Mahout in big data.
Setting up the Mahout environment.
Integrating Mahout with Hadoop and Spark.
Distributed data processing with Mahout.
Understanding Mahout’s architecture and algorithms.
Implementing classification using Mahout.
Building models for text categorization.
Evaluating classification models with metrics.
Clustering techniques: K-Means and Fuzzy K-Means.
Hierarchical clustering in Mahout.
Analyzing clusters with visualization tools.
User-based and item-based collaborative filtering.
Building recommendation engines with Mahout.
Real-world case studies: Movie recommendation systems.
Dimensionality reduction and feature engineering.
Matrix factorization and latent factor models.
Exploring Mahout’s integration with deep learning frameworks.
Optimizing ML models for large-scale data.
Deploying ML models in production using Mahout.
Best practices for scalability and performance tuning.
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