Machine Learning with Python

  1. Unsupervised Learning Algorithms
    1. Clustering Algorithms
      1. Partitioning Methods
        1. K-Means Clustering
          1. Algorithm Steps
            1. Initialization Methods
              1. Choosing Number of Clusters
                1. Convergence Criteria
                2. K-Medoids
                  1. PAM Algorithm
                    1. Robustness to Outliers
                    2. Mini-Batch K-Means
                      1. Scalability Improvements
                        1. Memory Efficiency
                      2. Hierarchical Clustering
                        1. Agglomerative Clustering
                          1. Bottom-up Approach
                            1. Linkage Criteria
                              1. Dendrograms
                              2. Divisive Clustering
                                1. Top-down Approach
                                2. Linkage Methods
                                  1. Single Linkage
                                    1. Complete Linkage
                                      1. Average Linkage
                                        1. Ward Linkage
                                      2. Density-Based Clustering
                                        1. DBSCAN
                                          1. Core Points
                                            1. Border Points
                                              1. Noise Points
                                                1. Epsilon and MinPts Parameters
                                                2. OPTICS
                                                  1. Ordering Points
                                                    1. Reachability Distance
                                                  2. Clustering Evaluation
                                                    1. Internal Metrics
                                                      1. Silhouette Score
                                                        1. Davies-Bouldin Index
                                                          1. Calinski-Harabasz Index
                                                          2. External Metrics
                                                            1. Adjusted Rand Index
                                                              1. Normalized Mutual Information
                                                                1. Homogeneity and Completeness
                                                            2. Dimensionality Reduction
                                                              1. Linear Dimensionality Reduction
                                                                1. Principal Component Analysis
                                                                  1. Covariance Matrix
                                                                    1. Eigenvalue Decomposition
                                                                      1. Explained Variance
                                                                        1. Component Interpretation
                                                                        2. Linear Discriminant Analysis
                                                                          1. Between-class Variance
                                                                            1. Within-class Variance
                                                                              1. Supervised Dimensionality Reduction
                                                                              2. Factor Analysis
                                                                                1. Latent Factors
                                                                                  1. Factor Loadings
                                                                                    1. Rotation Methods
                                                                                  2. Non-linear Dimensionality Reduction
                                                                                    1. t-SNE
                                                                                      1. Stochastic Neighbor Embedding
                                                                                        1. Perplexity Parameter
                                                                                          1. Learning Rate
                                                                                            1. Visualization Applications
                                                                                            2. UMAP
                                                                                              1. Uniform Manifold Approximation
                                                                                                1. Topological Structure
                                                                                                  1. Parameter Selection
                                                                                                  2. Manifold Learning
                                                                                                    1. Locally Linear Embedding
                                                                                                      1. Isomap
                                                                                                        1. Spectral Embedding
                                                                                                    2. Association Rule Mining
                                                                                                      1. Market Basket Analysis
                                                                                                        1. Itemsets
                                                                                                          1. Transactions
                                                                                                            1. Support and Confidence
                                                                                                            2. Apriori Algorithm
                                                                                                              1. Frequent Itemset Generation
                                                                                                                1. Rule Generation
                                                                                                                  1. Pruning Strategies
                                                                                                                  2. FP-Growth Algorithm
                                                                                                                    1. FP-Tree Construction
                                                                                                                      1. Pattern Mining
                                                                                                                        1. Memory Efficiency
                                                                                                                        2. Rule Evaluation Metrics
                                                                                                                          1. Lift
                                                                                                                            1. Conviction
                                                                                                                              1. Leverage
                                                                                                                                1. Jaccard Coefficient

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                                                                                                                            4. Supervised Learning Algorithms

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                                                                                                                            6. Introduction to Deep Learning