This list provides a comprehensive learning path for Apache Mahout, progressing from beginner to advanced levels. It covers various aspects of Mahout, including core concepts, algorithms, and practical applications.
I. Foundations & Getting Started (1-15)
- Welcome to Apache Mahout: An Introduction
- Setting Up Your Mahout Environment
- Understanding Mahout's Architecture
- Key Concepts in Mahout: Vectors, Matrices, and Algorithms
- Working with Data in Mahout: Input Formats and Preprocessing
- Introduction to MapReduce with Mahout
- Running Your First Mahout Job
- Understanding Mahout's Core Libraries
- Data Exploration and Visualization for Mahout
- Mahout's Machine Learning Paradigm
- Introduction to Recommender Systems
- Building a Simple Recommender with Mahout
- Evaluating Recommender Performance
- Introduction to Clustering with Mahout
- Your First Clustering Project: A Step-by-Step Guide
II. Collaborative Filtering & Recommendation (16-35)
- User-Based Collaborative Filtering
- Item-Based Collaborative Filtering
- Matrix Factorization for Recommendations
- Singular Value Decomposition (SVD) for Recommendations
- Alternating Least Squares (ALS) for Recommendations
- Building Hybrid Recommender Systems
- Content-Based Filtering for Recommendations
- Collaborative Filtering with Implicit Feedback
- Handling Cold Start Problems in Recommendations
- Scalable Recommendation with Mahout
- Mahout's Taste API: Building Custom Recommenders
- Advanced Recommender Techniques
- Evaluating and Tuning Recommender Algorithms
- Real-World Recommender System Design
- Implementing Recommenders with Mahout
- Performance Optimization for Recommender Systems
- Recommending Items to Groups
- Context-Aware Recommendations
- Personalized Recommendations
- Building a Recommendation Engine with Mahout
III. Clustering Algorithms (36-55)
- K-Means Clustering: A Detailed Look
- Fuzzy K-Means Clustering
- Canopy Clustering: Efficient Initializations
- Mean-Shift Clustering: Density-Based Approach
- DBSCAN Clustering: Discovering Clusters of Arbitrary Shape
- Hierarchical Clustering: Building Data Hierarchies
- Spectral Clustering: Using Eigenvectors for Clustering
- Choosing the Right Clustering Algorithm
- Evaluating Clustering Performance
- Clustering Large Datasets with Mahout
- Mahout's Clustering Implementations
- Understanding Cluster Evaluation Metrics
- Data Preprocessing for Clustering
- Feature Selection for Clustering
- Applying Clustering to Real-World Problems
- Clustering Text Data with Mahout
- Clustering Web Data
- Clustering Social Network Data
- Visualizing Clustering Results
- Advanced Clustering Techniques
IV. Classification Algorithms (56-70)
- Naive Bayes Classification
- Logistic Regression with Mahout
- Support Vector Machines (SVMs)
- Decision Tree Learning
- Random Forest Classification
- Building Classification Models with Mahout
- Evaluating Classification Performance
- Feature Engineering for Classification
- Text Classification with Mahout
- Spam Filtering with Mahout
- Image Classification with Mahout (if applicable)
- Mahout's Classification Implementations
- Handling Imbalanced Datasets
- Multi-Class Classification Strategies
- Ensemble Methods for Classification
V. Mahout Integration & Advanced Topics (71-85)
- Mahout and Hadoop: Working Together
- Mahout on Spark: Scalable Machine Learning
- Integrating Mahout with Other Big Data Tools
- Mahout's Distributed Computing Framework
- Performance Tuning and Optimization in Mahout
- Working with Large Datasets in Mahout
- Mahout's Linear Algebra Capabilities
- Dimensionality Reduction Techniques in Mahout
- Principal Component Analysis (PCA) with Mahout
- Singular Value Decomposition (SVD) in Detail
- Building Machine Learning Pipelines with Mahout
- Model Selection and Evaluation with Mahout
- Deploying Mahout Models
- Real-World Applications of Mahout
- Case Studies: Mahout in Action
VI. Deep Dives & Mastery (86-100)
- Advanced Mahout Algorithms
- Customizing Mahout Algorithms
- Contributing to the Mahout Project
- Mahout's Future and Development
- Best Practices for Mahout Development
- Common Pitfalls and Troubleshooting
- Mahout for Data Science Teams
- Building Scalable Machine Learning Systems
- MLOps with Mahout
- Monitoring and Maintaining Mahout Models
- Mahout and Deep Learning (if applicable)
- Mahout for Specific Industries (e.g., Finance, Healthcare)
- Building a Portfolio of Mahout Projects
- The Evolution of Mahout and its Ecosystem
- Mastering Mahout: A Comprehensive Guide.