1. Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• Applications of Machine Learning
• Overview of Python for Machine Learning

2. Setting Up the Environment

• Installing Python
• Setting up Jupyter Notebook
• Installing necessary libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, PyTorch
• Introduction to Integrated Development Environments (IDEs) like PyCharm or VS Code

3. Python Basics for Machine Learning

• Basic Python syntax and data structures
• Introduction to libraries: NumPy for numerical operations, Pandas for data manipulation
• Data visualization with Matplotlib and Seaborn

4. Data Preprocessing

• Importing and cleaning data
• Handling missing data
• Feature scaling (Normalization and Standardization)
• Encoding categorical data
• Splitting datasets into training and testing sets

5. Exploratory Data Analysis (EDA)

• Descriptive statistics
• Data visualization techniques (histograms, scatter plots, box plots, correlation heatmaps)
• Identifying patterns and correlations in the data
• Feature selection and dimensionality reduction

6. Supervised Learning Algorithms

• Regression:
• Linear Regression
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression
• Classification:
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Support Vector Machines (SVM)
• Decision Trees
• Random Forest Classification
• Naive Bayes
• Gradient Boosting (e.g., XGBoost, LightGBM)

7. Unsupervised Learning Algorithms

• Clustering:
• K-Means Clustering
• Hierarchical Clustering
• DBSCAN
• Dimensionality Reduction:
• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Autoencoders

8. Model Evaluation

• Cross-validation
• Evaluation metrics for regression (MAE, MSE, RMSE, R²)
• Evaluation metrics for classification (Accuracy, Precision, Recall, F1-Score, ROC-AUC)
• Confusion matrix
• Overfitting and underfitting

9. Model Optimization

• Hyperparameter tuning (Grid Search, Random Search)
• Regularization techniques (Lasso, Ridge, ElasticNet)
• Feature selection techniques
• Ensemble methods (Bagging, Boosting, Stacking)

10. Introduction to Deep Learning

• Overview of Neural Networks
• Understanding TensorFlow and Keras
• Building and training neural networks
• Convolutional Neural Networks (CNNs) for image recognition
• Recurrent Neural Networks (RNNs) for sequential data

11. Advanced Topics

• Natural Language Processing (NLP)
• Time Series Forecasting
• Reinforcement Learning basics
• Anomaly detection
• Transfer Learning
• Generative Adversarial Networks (GANs)

12. Deploying Machine Learning Models

• Model serialization using pickle or joblib
• Creating REST APIs with Flask or Django for model deployment
• Model deployment on cloud platforms (AWS, Azure, Google Cloud)
• Introduction to MLOps and Continuous Integration/Continuous Deployment (CI/CD)

13. Capstone Project

• Choose a real-world dataset
• Define the problem statement
• Data preprocessing and EDA
• Model selection and training
• Model evaluation and optimization
• Deployment and presentation