Apache Spark

  1. Machine Learning with MLlib
    1. MLlib Overview
      1. Library Architecture
        1. RDD-Based API
          1. DataFrame-Based API
            1. Pipeline Integration
            2. Language Support
              1. Scala Implementation
                1. Java Bindings
                  1. Python Integration
                    1. R Interface
                    2. Comparison with Other Libraries
                      1. Scikit-learn Integration
                        1. TensorFlow Compatibility
                          1. Distributed vs Single-Machine
                        2. ML Pipeline Framework
                          1. Pipeline Components
                            1. Transformer Interface
                              1. Feature Transformation
                                1. Model Application
                                2. Estimator Interface
                                  1. Model Training
                                    1. Parameter Learning
                                    2. Pipeline Construction
                                      1. Stage Composition
                                        1. Parameter Passing
                                      2. Model Selection
                                        1. Cross-Validation
                                          1. K-Fold Validation
                                            1. Train-Validation Split
                                          2. Model Persistence
                                            1. Model Saving
                                              1. Model Loading
                                                1. Version Management
                                              2. Feature Engineering
                                                1. Feature Extraction
                                                  1. Text Processing
                                                    1. Tokenization
                                                      1. Stop Word Removal
                                                        1. N-Gram Generation
                                                        2. Hashing Features
                                                          1. HashingTF
                                                            1. Feature Hashing Benefits
                                                            2. Word Embeddings
                                                              1. Word2Vec Implementation
                                                                1. Vector Representations
                                                              2. Feature Transformation
                                                                1. Scaling Operations
                                                                  1. StandardScaler
                                                                    1. MinMaxScaler
                                                                      1. MaxAbsScaler
                                                                      2. Encoding Operations
                                                                        1. OneHotEncoder
                                                                          1. StringIndexer
                                                                            1. IndexToString
                                                                            2. Mathematical Transformations
                                                                              1. Polynomial Features
                                                                                1. Interaction Features
                                                                              2. Feature Selection
                                                                                1. Statistical Selection
                                                                                  1. ChiSqSelector
                                                                                    1. Correlation Analysis
                                                                                    2. Dimensionality Reduction
                                                                                      1. VectorSlicer
                                                                                        1. Feature Importance
                                                                                    3. Supervised Learning
                                                                                      1. Classification Algorithms
                                                                                        1. Linear Models
                                                                                          1. Logistic Regression
                                                                                            1. Linear SVM
                                                                                            2. Tree-Based Models
                                                                                              1. Decision Trees
                                                                                                1. Random Forest
                                                                                                  1. Gradient-Boosted Trees
                                                                                                  2. Neural Networks
                                                                                                    1. Multilayer Perceptron
                                                                                                      1. Deep Learning Integration
                                                                                                      2. Ensemble Methods
                                                                                                        1. Voting Classifiers
                                                                                                          1. Stacking
                                                                                                        2. Regression Algorithms
                                                                                                          1. Linear Regression
                                                                                                            1. Ordinary Least Squares
                                                                                                              1. Ridge Regression
                                                                                                                1. Lasso Regression
                                                                                                                2. Tree-Based Regression
                                                                                                                  1. Decision Tree Regression
                                                                                                                    1. Random Forest Regression
                                                                                                                      1. Gradient-Boosted Regression
                                                                                                                      2. Generalized Linear Models
                                                                                                                        1. Poisson Regression
                                                                                                                          1. Gamma Regression
                                                                                                                      3. Unsupervised Learning
                                                                                                                        1. Clustering Algorithms
                                                                                                                          1. Partitioning Methods
                                                                                                                            1. K-Means Clustering
                                                                                                                              1. K-Means++
                                                                                                                                1. Bisecting K-Means
                                                                                                                                2. Probabilistic Models
                                                                                                                                  1. Gaussian Mixture Models
                                                                                                                                    1. Expectation-Maximization
                                                                                                                                    2. Topic Modeling
                                                                                                                                      1. Latent Dirichlet Allocation
                                                                                                                                        1. Topic Discovery
                                                                                                                                      2. Dimensionality Reduction
                                                                                                                                        1. Principal Component Analysis
                                                                                                                                          1. Variance Explanation
                                                                                                                                            1. Component Selection
                                                                                                                                            2. Singular Value Decomposition
                                                                                                                                              1. Matrix Factorization
                                                                                                                                                1. Low-Rank Approximation
                                                                                                                                            3. Model Evaluation and Metrics
                                                                                                                                              1. Classification Metrics
                                                                                                                                                1. Accuracy Measures
                                                                                                                                                  1. Precision and Recall
                                                                                                                                                    1. F1 Score Calculation
                                                                                                                                                      1. ROC and AUC Analysis
                                                                                                                                                        1. Confusion Matrix
                                                                                                                                                        2. Regression Metrics
                                                                                                                                                          1. Error Measures
                                                                                                                                                            1. Mean Squared Error
                                                                                                                                                              1. Root Mean Squared Error
                                                                                                                                                                1. Mean Absolute Error
                                                                                                                                                                2. Goodness of Fit
                                                                                                                                                                  1. R-squared
                                                                                                                                                                    1. Adjusted R-squared
                                                                                                                                                                  2. Clustering Evaluation
                                                                                                                                                                    1. Internal Metrics
                                                                                                                                                                      1. Silhouette Analysis
                                                                                                                                                                        1. Within-Cluster Sum of Squares
                                                                                                                                                                        2. External Metrics
                                                                                                                                                                          1. Adjusted Rand Index
                                                                                                                                                                            1. Normalized Mutual Information

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                                                                                                                                                                      7. Graph Processing with GraphX