Machine Learning Fundamentals

  1. Introduction to Advanced Concepts
    1. Ensemble Methods
      1. Concept: Combining Multiple Models
        1. Wisdom of Crowds
          1. Error Reduction
            1. Improved Generalization
            2. Bagging
              1. Bootstrap Aggregating
                1. Variance Reduction
                  1. Parallel Training
                    1. Random Forests
                      1. Structure and Training
                        1. Decision Tree Ensemble
                          1. Bootstrap Sampling
                            1. Majority Voting
                            2. Feature Bagging
                              1. Random Feature Selection
                                1. Decorrelation Strategy
                                2. Feature Importance
                                  1. Out-of-Bag Error
                                3. Boosting
                                  1. Sequential Learning
                                    1. Bias Reduction
                                      1. Adaptive Weighting
                                        1. Gradient Boosting
                                          1. Sequential Model Training
                                            1. Residual Learning
                                              1. Gradient Descent
                                                1. Additive Models
                                                2. Learning Rate
                                                  1. Tree Depth Control
                                                  2. AdaBoost
                                                    1. Weight Adjustment
                                                      1. Sample Reweighting
                                                        1. Error-Based Updates
                                                        2. Exponential Loss
                                                          1. Weak Learner Combination
                                                          2. XGBoost
                                                            1. Extreme Gradient Boosting
                                                              1. Regularization
                                                                1. Parallel Processing
                                                              2. Stacking
                                                                1. Meta-Learning Approach
                                                                  1. Base Learner Diversity
                                                                    1. Meta-Learner Training
                                                                      1. Cross-Validation Predictions
                                                                      2. Voting Methods
                                                                        1. Hard Voting
                                                                          1. Soft Voting
                                                                            1. Weighted Voting
                                                                          2. Introduction to Neural Networks
                                                                            1. The Perceptron
                                                                              1. Structure and Activation Function
                                                                                1. Linear Combination
                                                                                  1. Threshold Function
                                                                                    1. Binary Classification
                                                                                    2. Learning Algorithm
                                                                                      1. Weight Updates
                                                                                        1. Perceptron Rule
                                                                                        2. Limitations
                                                                                          1. Linear Separability
                                                                                            1. XOR Problem
                                                                                          2. Multi-Layer Perceptrons (MLPs)
                                                                                            1. Hidden Layers
                                                                                              1. Non-Linear Transformations
                                                                                                1. Universal Approximation
                                                                                                  1. Depth vs Width
                                                                                                  2. Activation Functions
                                                                                                    1. Sigmoid Function
                                                                                                      1. ReLU Function
                                                                                                        1. Tanh Function
                                                                                                          1. Softmax Function
                                                                                                          2. Backpropagation
                                                                                                            1. Gradient Computation
                                                                                                              1. Chain Rule Application
                                                                                                                1. Weight Update Rules
                                                                                                                2. Training Process
                                                                                                                  1. Forward Pass
                                                                                                                    1. Loss Computation
                                                                                                                      1. Backward Pass
                                                                                                                        1. Parameter Updates
                                                                                                                      2. Limitations and Use Cases
                                                                                                                        1. Computational Requirements
                                                                                                                          1. Data Requirements
                                                                                                                            1. Interpretability Challenges
                                                                                                                              1. Appropriate Applications

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                                                                                                                          8. Fundamentals of Model Performance and Improvement

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                                                                                                                          10. Practical Considerations