Module 1: Introduction to Machine Learning
What is Machine Learning?
Types of ML: Supervised, Unsupervised, Reinforcement
ML vs AI vs Deep Learning
Real-world applications and use cases

Module 2: Python for Machine Learning
Python essentials
NumPy, Pandas for ML
Data preprocessing techniques
Feature scaling and encoding

Module 3: Mathematics for Machine Learning
Linear algebra basics
Probability concepts
Statistics for ML
Cost functions and optimization intuition

Module 4: Data Preprocessing & Feature Engineering
Data cleaning
Handling missing values
Feature selection
Feature extraction

Module 5: Supervised Learning – Regression
Linear regression
Multiple regression
Regularization (Ridge, Lasso)
Model evaluation metrics

Module 6: Supervised Learning – Classification
Logistic regression
KNN
Decision trees
Random forest
Naive Bayes
Evaluation metrics (Accuracy, Precision, Recall, F1-score)

Module 7: Unsupervised Learning
Clustering (K-Means, Hierarchical)
Dimensionality reduction (PCA)
Anomaly detection

Module 8: Model Evaluation & Optimization
Train-test split
Cross-validation
Bias–variance tradeoff
Hyperparameter tuning

Module 9: Introduction to Deep Learning
Neural network basics
Activation functions
Overview of TensorFlow / PyTorch
Use cases

Module 10: ML in Practice
End-to-end ML pipeline
Model deployment basics
ML ethics and fairness
Industry case studies

Module 11: Tools & Frameworks
Scikit-learn
Jupyter Notebook
Git & version control
ML lifecycle management basics

Module 12: Capstone Project
Real-world ML project
Dataset analysis
Model building, evaluation, and presentation