Machine Learning with Apache Spark

  1. Supervised Learning with Spark ML
    1. Classification Algorithms
      1. Logistic Regression
        1. Binary Classification
          1. Multiclass Classification
            1. Multinomial Logistic Regression
              1. Regularization Techniques
                1. L1 Regularization
                  1. L2 Regularization
                    1. Elastic Net
                    2. Feature Scaling Requirements
                      1. Convergence Criteria
                      2. Decision Trees
                        1. Tree Construction Algorithm
                          1. Splitting Criteria
                            1. Gini Impurity
                              1. Entropy
                                1. Classification Error
                                2. Tree Depth and Pruning
                                  1. Handling Categorical Features
                                    1. Feature Importance Calculation
                                    2. Random Forests
                                      1. Ensemble Learning Principles
                                        1. Bootstrap Sampling
                                          1. Feature Subsampling
                                            1. Voting Mechanisms
                                              1. Out-of-Bag Error
                                                1. Feature Importance
                                                  1. Hyperparameter Tuning
                                                  2. Gradient-Boosted Trees
                                                    1. Boosting Concept
                                                      1. Sequential Learning
                                                        1. Loss Functions
                                                          1. Learning Rate
                                                            1. Tree Depth Control
                                                              1. Early Stopping
                                                              2. Naive Bayes
                                                                1. Bayes Theorem Application
                                                                  1. Independence Assumptions
                                                                    1. Multinomial Naive Bayes
                                                                      1. Bernoulli Naive Bayes
                                                                        1. Gaussian Naive Bayes
                                                                          1. Laplace Smoothing
                                                                          2. Support Vector Machines
                                                                            1. Linear SVMs
                                                                              1. Kernel Methods
                                                                                1. Margin Maximization
                                                                                  1. Support Vector Identification
                                                                                    1. Limitations in Spark ML
                                                                                    2. Multilayer Perceptron Classifier
                                                                                      1. Neural Network Architecture
                                                                                        1. Hidden Layer Configuration
                                                                                          1. Activation Functions
                                                                                            1. Sigmoid
                                                                                              1. ReLU
                                                                                                1. Tanh
                                                                                                2. Backpropagation Training
                                                                                                  1. Weight Initialization
                                                                                                3. Regression Algorithms
                                                                                                  1. Linear Regression
                                                                                                    1. Ordinary Least Squares
                                                                                                      1. Normal Equation
                                                                                                        1. Gradient Descent
                                                                                                          1. Regularization
                                                                                                            1. Ridge Regression
                                                                                                              1. Lasso Regression
                                                                                                                1. Elastic Net Regression
                                                                                                                2. Feature Scaling Impact
                                                                                                                3. Generalized Linear Regression
                                                                                                                  1. Exponential Family Distributions
                                                                                                                    1. Supported Families
                                                                                                                      1. Gaussian
                                                                                                                        1. Binomial
                                                                                                                          1. Poisson
                                                                                                                            1. Gamma
                                                                                                                              1. Tweedie
                                                                                                                            2. Decision Tree Regression
                                                                                                                              1. Splitting Criteria for Regression
                                                                                                                                1. Variance Reduction
                                                                                                                                  1. Pruning Strategies
                                                                                                                                    1. Handling Continuous Targets
                                                                                                                                    2. Random Forest Regression
                                                                                                                                      1. Ensemble Averaging
                                                                                                                                        1. Variance Reduction
                                                                                                                                          1. Feature Importance
                                                                                                                                            1. Out-of-Bag Predictions
                                                                                                                                            2. Gradient-Boosted Tree Regression
                                                                                                                                              1. Sequential Error Correction
                                                                                                                                                1. Loss Functions for Regression
                                                                                                                                                  1. Shrinkage Parameter
                                                                                                                                                    1. Tree Complexity Control
                                                                                                                                                    2. Isotonic Regression
                                                                                                                                                      1. Monotonicity Constraints
                                                                                                                                                        1. Pool Adjacent Violators Algorithm
                                                                                                                                                          1. Use Cases and Applications

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                                                                                                                                                      4. Building Machine Learning Pipelines

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                                                                                                                                                      6. Unsupervised Learning with Spark ML