Machine Learning for Developers

  1. Supervised Learning Fundamentals
    1. Regression Tasks
      1. Linear Regression
        1. Simple Linear Regression
          1. Multiple Linear Regression
            1. Polynomial Regression
              1. Assumptions and Limitations
              2. Regularized Regression
                1. Ridge Regression
                  1. Lasso Regression
                    1. Elastic Net
                      1. Regularization Parameter Selection
                      2. Tree-Based Regression
                        1. Decision Trees
                          1. Random Forest
                            1. Gradient Boosting
                              1. XGBoost
                                1. LightGBM
                                2. Advanced Regression Techniques
                                  1. Support Vector Regression
                                    1. Neural Network Regression
                                      1. Ensemble Methods
                                      2. Regression Evaluation
                                        1. Mean Absolute Error
                                          1. Mean Squared Error
                                            1. Root Mean Squared Error
                                              1. R-squared
                                                1. Adjusted R-squared
                                                  1. Cross-Validation Metrics
                                                2. Classification Tasks
                                                  1. Binary Classification
                                                    1. Logistic Regression
                                                      1. Decision Boundaries
                                                        1. Probability Interpretation
                                                          1. Threshold Selection
                                                          2. Multi-Class Classification
                                                            1. One-vs-Rest Strategy
                                                              1. One-vs-One Strategy
                                                                1. Multinomial Approaches
                                                                  1. Class Imbalance Handling
                                                                  2. Classification Algorithms
                                                                    1. k-Nearest Neighbors
                                                                      1. Support Vector Machines
                                                                        1. Naive Bayes
                                                                          1. Decision Trees
                                                                            1. Random Forest
                                                                              1. Gradient Boosting
                                                                              2. Classification Evaluation
                                                                                1. Accuracy and Error Rate
                                                                                  1. Precision and Recall
                                                                                    1. F1-Score and F-Beta
                                                                                      1. ROC Curves and AUC
                                                                                        1. Precision-Recall Curves
                                                                                          1. Confusion Matrix Analysis
                                                                                            1. Multi-Class Metrics

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                                                                                        2. Machine Learning Project Lifecycle

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                                                                                        4. Unsupervised Learning Fundamentals