Machine Learning with Scikit-Learn

  1. Unsupervised Learning
    1. Clustering
      1. Clustering Concepts
        1. Similarity and Distance Measures
          1. Cluster Validation
            1. Choosing Number of Clusters
            2. K-Means Clustering
              1. Algorithm Steps
                1. Centroid Initialization
                  1. Convergence Criteria
                    1. Limitations and Assumptions
                      1. Mini-Batch K-Means
                      2. Hierarchical Clustering
                        1. Agglomerative Clustering
                          1. Linkage Criteria
                            1. Single Linkage
                              1. Complete Linkage
                                1. Average Linkage
                                  1. Ward Linkage
                                  2. Dendrogram Interpretation
                                  3. Density-Based Clustering
                                    1. DBSCAN
                                      1. Core Points
                                        1. Border Points
                                          1. Noise Points
                                            1. Parameter Selection
                                            2. OPTICS
                                              1. Ordering Points
                                            3. Gaussian Mixture Models
                                              1. Probabilistic Clustering
                                                1. Expectation-Maximization
                                                  1. Model Selection
                                                  2. Spectral Clustering
                                                    1. Graph-based Clustering
                                                      1. Affinity Matrices
                                                      2. Mean Shift Clustering
                                                        1. Mode Seeking
                                                          1. Bandwidth Selection
                                                          2. Clustering Evaluation
                                                            1. Silhouette Score
                                                              1. Calinski-Harabasz Index
                                                                1. Davies-Bouldin Index
                                                                  1. Adjusted Rand Index
                                                                2. Dimensionality Reduction
                                                                  1. Curse of Dimensionality
                                                                    1. Linear Methods
                                                                      1. Principal Component Analysis
                                                                        1. Eigenvalue Decomposition
                                                                          1. Variance Explained
                                                                            1. Component Interpretation
                                                                              1. Choosing Number of Components
                                                                              2. Truncated SVD
                                                                                1. Sparse Data Handling
                                                                                  1. Latent Semantic Analysis
                                                                                  2. Independent Component Analysis
                                                                                    1. Signal Separation
                                                                                    2. Factor Analysis
                                                                                      1. Latent Variables
                                                                                    3. Non-linear Methods
                                                                                      1. Manifold Learning
                                                                                        1. Locally Linear Embedding
                                                                                          1. Isomap
                                                                                            1. Geodesic Distances
                                                                                            2. Multi-dimensional Scaling
                                                                                              1. t-SNE
                                                                                                1. Perplexity Parameter
                                                                                                  1. Learning Rate
                                                                                                    1. Visualization Applications
                                                                                                    2. UMAP
                                                                                                  2. Feature Selection vs Feature Extraction
                                                                                                    1. Dimensionality Reduction Evaluation
                                                                                                    2. Anomaly Detection
                                                                                                      1. Outlier vs Anomaly
                                                                                                        1. Statistical Methods
                                                                                                          1. Z-score Method
                                                                                                            1. Modified Z-score
                                                                                                            2. Isolation Forest
                                                                                                              1. Random Partitioning
                                                                                                                1. Anomaly Score
                                                                                                                2. Local Outlier Factor
                                                                                                                  1. Density-based Detection
                                                                                                                    1. Local Density Comparison
                                                                                                                    2. One-Class SVM
                                                                                                                      1. Support Vector Description
                                                                                                                        1. Kernel Methods
                                                                                                                        2. Elliptic Envelope
                                                                                                                          1. Gaussian Distribution Assumption
                                                                                                                          2. Evaluation of Anomaly Detection
                                                                                                                            1. Precision and Recall
                                                                                                                              1. ROC Analysis

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                                                                                                                          8. Improving Model Performance

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                                                                                                                          10. Building Machine Learning Pipelines