Machine Learning with Scikit-Learn

  1. Data Preprocessing and Feature Engineering
    1. Data Cleaning
      1. Identifying Data Quality Issues
        1. Handling Duplicate Records
          1. Dealing with Inconsistent Data
            1. Data Type Conversions
            2. Handling Missing Data
              1. Identifying Missing Values
                1. Missing Data Patterns
                  1. Strategies for Handling Missing Data
                    1. Complete Case Analysis
                      1. Pairwise Deletion
                        1. Imputation Techniques
                        2. Using SimpleImputer
                          1. Mean Imputation
                            1. Median Imputation
                              1. Most Frequent Imputation
                                1. Constant Imputation
                                2. Advanced Imputation Methods
                                  1. KNNImputer
                                    1. IterativeImputer
                                  2. Feature Scaling and Normalization
                                    1. Importance of Feature Scaling
                                      1. When to Apply Scaling
                                        1. StandardScaler
                                          1. Z-score Standardization
                                            1. Properties and Use Cases
                                            2. MinMaxScaler
                                              1. Min-Max Normalization
                                                1. Range Specification
                                                2. RobustScaler
                                                  1. Median and IQR Scaling
                                                    1. Outlier Robustness
                                                    2. MaxAbsScaler
                                                      1. Maximum Absolute Scaling
                                                      2. Normalizer
                                                        1. Unit Vector Scaling
                                                        2. QuantileTransformer
                                                          1. Uniform and Normal Distributions
                                                          2. PowerTransformer
                                                            1. Box-Cox Transformation
                                                              1. Yeo-Johnson Transformation
                                                              2. Choosing the Right Scaler
                                                              3. Encoding Categorical Features
                                                                1. Identifying Categorical Variables
                                                                  1. Nominal vs Ordinal
                                                                    1. Binary vs Multi-class
                                                                    2. OrdinalEncoder
                                                                      1. Encoding Ordinal Features
                                                                        1. Custom Ordering
                                                                        2. OneHotEncoder
                                                                          1. Encoding Nominal Features
                                                                            1. Handling Unknown Categories
                                                                              1. Sparse vs Dense Output
                                                                                1. Drop Parameter
                                                                                2. LabelEncoder
                                                                                  1. Target Variable Encoding
                                                                                    1. Limitations and Use Cases
                                                                                    2. Binary Encoding Techniques
                                                                                      1. Target Encoding
                                                                                      2. Discretization and Binning
                                                                                        1. Purpose of Binning
                                                                                          1. Equal-Width Binning
                                                                                            1. Equal-Frequency Binning
                                                                                              1. KBinsDiscretizer
                                                                                                1. Uniform Strategy
                                                                                                  1. Quantile Strategy
                                                                                                    1. KMeans Strategy
                                                                                                      1. Encoding Options
                                                                                                    2. Feature Transformation
                                                                                                      1. Mathematical Transformations
                                                                                                        1. Log Transformation
                                                                                                          1. Square Root Transformation
                                                                                                            1. Reciprocal Transformation
                                                                                                            2. PolynomialFeatures
                                                                                                              1. Creating Polynomial Features
                                                                                                                1. Interaction Features
                                                                                                                  1. Degree Selection
                                                                                                                  2. FunctionTransformer
                                                                                                                    1. Custom Transformations
                                                                                                                      1. Lambda Functions
                                                                                                                        1. Inverse Transformations
                                                                                                                      2. Feature Selection
                                                                                                                        1. Importance of Feature Selection
                                                                                                                          1. Filter Methods
                                                                                                                            1. VarianceThreshold
                                                                                                                              1. Statistical Tests
                                                                                                                              2. Wrapper Methods
                                                                                                                                1. Recursive Feature Elimination
                                                                                                                                  1. Sequential Feature Selection
                                                                                                                                  2. Embedded Methods
                                                                                                                                    1. L1 Regularization
                                                                                                                                      1. Tree-based Feature Importance
                                                                                                                                      2. Univariate Feature Selection
                                                                                                                                        1. SelectKBest
                                                                                                                                          1. SelectPercentile
                                                                                                                                            1. SelectFpr
                                                                                                                                              1. SelectFdr
                                                                                                                                                1. SelectFwe
                                                                                                                                                2. Scoring Functions
                                                                                                                                                  1. chi2
                                                                                                                                                    1. f_classif
                                                                                                                                                      1. f_regression
                                                                                                                                                        1. mutual_info_classif
                                                                                                                                                          1. mutual_info_regression
                                                                                                                                                        2. Feature Creation
                                                                                                                                                          1. Domain-Specific Features
                                                                                                                                                            1. Time-Based Features
                                                                                                                                                              1. Text Features
                                                                                                                                                                1. Image Features

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                                                                                                                                                              3. Machine Learning Fundamentals

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                                                                                                                                                              5. Supervised Learning: Regression