Machine Learning with Scikit-Learn

Machine Learning with Scikit-Learn focuses on the practical application of machine learning principles using one of Python's most fundamental and user-friendly libraries. It provides a versatile and efficient toolkit for performing predictive data analysis, offering a wide array of algorithms for classification, regression, clustering, and dimensionality reduction through a clean, consistent API. Built upon the scientific Python stack (NumPy, SciPy, and Matplotlib), Scikit-learn is an essential starting point for practitioners, enabling them to preprocess data, train models, and evaluate their performance within a unified framework, making it a cornerstone of modern data science workflows.

  1. Introduction to Scikit-Learn
    1. Overview of Scikit-Learn
      1. Definition and Purpose
        1. Historical Background and Development
          1. Key Features and Capabilities
            1. Target Audience and Use Cases
            2. Role in the Python Data Science Ecosystem
              1. Integration with NumPy
                1. Integration with SciPy
                  1. Integration with Pandas
                    1. Integration with Matplotlib
                      1. Integration with Jupyter Notebooks
                        1. Comparison with Other Machine Learning Libraries
                          1. TensorFlow and Keras
                            1. PyTorch
                              1. XGBoost
                                1. LightGBM
                              2. Key Design Principles
                                1. Consistency of API
                                  1. Inspection and Introspection
                                    1. Non-proliferation of Classes
                                      1. Composition of Pipelines and Transformers
                                        1. Sensible Defaults and Parameter Choices
                                          1. Reproducibility and Random State
                                          2. Installation and Setup
                                            1. System Requirements
                                              1. Python Version Compatibility
                                                1. Operating System Support
                                                  1. Hardware Requirements
                                                  2. Environment Setup
                                                    1. Using Conda Environments
                                                      1. Using Virtualenv
                                                        1. Using venv
                                                        2. Installing Scikit-Learn
                                                          1. Using pip
                                                            1. Using conda
                                                              1. Installing from Source
                                                              2. Installing Dependencies
                                                                1. NumPy
                                                                  1. SciPy
                                                                    1. Pandas
                                                                      1. Matplotlib
                                                                        1. Joblib
                                                                        2. Verifying Installation
                                                                          1. Troubleshooting Installation Issues
                                                                            1. Setting Up Development Environment