Module 1: Introduction to Machine Learning
- What is Machine Learning?
- Types of Machine Learning: Supervised, Unsupervised, Semi-Supervised, Reinforcement
- Real-World Applications of Machine Learning
- Steps in the ML Workflow
- Introduction to Python Libraries for ML
Module 2: Data Preprocessing
- Understanding Data and Features
- Handling Missing Data
- Data Transformation: Normalization, Standardization, One-Hot Encoding
- Feature Engineering and Selection
- Data Splitting: Train-Test Split, Cross-Validation
Module 3: Supervised Learning – Regression
- Linear Regression: Simple and Multiple Linear Regression
- Polynomial Regression
- Regularization Techniques: Ridge Regression, Lasso Regression
- Evaluation Metrics: MAE, MSE, R² Score
Module 4: Supervised Learning – Classification
- Logistic Regression
- Decision Trees for Classification
- Random Forest Classifier
- Support Vector Machines (SVM)
- Naive Bayes Classifier
- Evaluation Metrics: Confusion Matrix, Precision, Recall, F1-Score, ROC-AUC
Module 5: Unsupervised Learning – Clustering
- Introduction to Clustering
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN (Density-Based Spatial Clustering)
- Evaluation Metrics: Silhouette Score, Davies-Bouldin Index
Module 6: Dimensionality Reduction
- Introduction to Dimensionality Reduction
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
Module 7: Ensemble Learning
- Introduction to Ensemble Methods
- Bagging Techniques: Random Forest
- Boosting Techniques: AdaBoost, Gradient Boosting, XGBoost
- Stacking Models
Module 8: Advanced Topics in Machine Learning
- Handling Imbalanced Data: SMOTE, Weighted Loss Functions
- Time Series Analysis: ARIMA, SARIMA
- Recommendation Systems: Collaborative Filtering, Content-Based Filtering
- Model Deployment: Saving Models with Pickle and Joblib
Module 9: Explainability and Ethics in Machine Learning
- Explainable AI: SHAP, LIME
- Ethical Considerations in Machine Learning: Bias, Privacy, Fairness
- Guidelines and Regulations: GDPR, Ethical AI Standards
Module 10: Capstone Project and Revision
- End-to-End ML Project
- Revision of Key Concepts and QnA
- Final Assessment