Machine Learning with Python

Machine Learning with Python is the practical application of building, training, and deploying machine learning models using the Python programming language. It has become the industry standard due to Python's simple syntax and its extensive ecosystem of powerful, open-source libraries such as Scikit-learn for classical algorithms, and TensorFlow and PyTorch for deep learning. This combination provides a robust and efficient framework that enables developers and data scientists to perform complex tasks—from data preprocessing and feature engineering to model evaluation—allowing them to rapidly develop and integrate intelligent systems into real-world applications.

1. Foundations of Machine Learning and Python
   1. Core Concepts of Machine Learning
      1. Defining Machine Learning
         1. Historical Context and Evolution
         2. Machine Learning vs Traditional Programming
         3. Machine Learning vs Statistical Analysis
         4. Applications and Use Cases
      2. Types of Machine Learning
         1. Supervised Learning
            1. Regression Problems
            2. Classification Problems
            3. Semi-Supervised Learning
         2. Unsupervised Learning
            1. Clustering
            2. Dimensionality Reduction
            3. Association Rule Learning
            4. Anomaly Detection
         3. Reinforcement Learning
            1. Exploration vs Exploitation
            2. Reward Systems
            3. Policy Learning
      3. Key Terminology and Concepts
         1. Features and Labels
            1. Feature Vectors
            2. Target Variables
            3. Feature Types
         2. Models and Algorithms
            1. Model Parameters
            2. Model Hyperparameters
            3. Model Architecture
            4. Algorithm Selection
         3. Training Process
            1. Training Set
            2. Validation Set
            3. Test Set
            4. Cross-Validation
         4. Model Performance Issues
            1. Overfitting
            2. Underfitting
            3. Generalization
         5. Bias-Variance Tradeoff
            1. Sources of Bias
            2. Sources of Variance
            3. Balancing Strategies
   2. Setting Up the Python Environment
      1. Python Installation and Management
         1. Downloading and Installing Python
         2. Python Version Management
         3. System vs User Installation
      2. Package Management
         1. Pip Package Manager
            1. Installing Packages
            2. Upgrading Packages
            3. Uninstalling Packages
            4. Requirements Files
         2. Conda Package Manager
            1. Installing Conda
            2. Managing Packages with Conda
            3. Conda Channels
      3. Virtual Environments
         1. Python venv
            1. Creating Virtual Environments
            2. Activating and Deactivating
            3. Managing Dependencies
         2. Conda Environments
            1. Creating Conda Environments
            2. Environment Configuration
            3. Sharing Environments
      4. Development Environments
         1. Jupyter Notebook
            1. Installation and Setup
            2. Basic Usage
            3. Keyboard Shortcuts
            4. Magic Commands
            5. Extensions
         2. JupyterLab
            1. Advanced Features
            2. Interface Overview
         3. Visual Studio Code
            1. Python Extensions
            2. Integrated Terminal
            3. Debugging Features
         4. PyCharm
            1. Project Setup
            2. Code Navigation
            3. Debugging Tools