Machine Learning with Scikit-Learn

  1. Machine Learning Fundamentals
    1. Types of Machine Learning
      1. Supervised Learning
        1. Classification
          1. Regression
          2. Unsupervised Learning
            1. Clustering
              1. Dimensionality Reduction
                1. Density Estimation
                2. Semi-Supervised Learning
                  1. Reinforcement Learning Overview
                  2. Key Concepts
                    1. Training and Test Data
                      1. Features and Labels
                        1. Model Complexity
                          1. Bias-Variance Tradeoff
                            1. Overfitting and Underfitting
                              1. Generalization
                              2. A Typical Machine Learning Workflow
                                1. Problem Definition
                                  1. Defining the Objective
                                    1. Identifying the Type of Problem
                                      1. Understanding Business Context
                                        1. Success Metrics Definition
                                        2. Data Acquisition and Exploration
                                          1. Data Collection Strategies
                                            1. Importing Data into Python
                                              1. Initial Data Inspection
                                                1. Data Quality Assessment
                                                2. Exploratory Data Analysis
                                                  1. Descriptive Statistics
                                                    1. Data Visualization
                                                      1. Correlation Analysis
                                                        1. Identifying Patterns and Anomalies
                                                        2. Data Preprocessing
                                                          1. Data Cleaning
                                                            1. Handling Missing Values
                                                              1. Handling Outliers
                                                                1. Feature Engineering
                                                                  1. Data Splitting
                                                                  2. Model Selection and Training
                                                                    1. Algorithm Selection
                                                                      1. Baseline Model Creation
                                                                        1. Model Training
                                                                          1. Cross-Validation
                                                                          2. Model Evaluation
                                                                            1. Performance Metrics
                                                                              1. Model Comparison
                                                                                1. Error Analysis
                                                                                2. Hyperparameter Tuning
                                                                                  1. Parameter Optimization
                                                                                    1. Grid Search
                                                                                      1. Random Search
                                                                                      2. Model Deployment Considerations
                                                                                        1. Model Serialization
                                                                                          1. Production Integration
                                                                                            1. Monitoring and Maintenance

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                                                                                        2. Core Scikit-Learn Concepts and API

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                                                                                        4. Data Preprocessing and Feature Engineering