Course Units
5 Comprehensive Modules Covering ML Fundamentals to Advanced Topics
Unit 1: Introduction
Fundamentals of Machine Learning
• What is Machine Learning?
• Types of Learning
• Python Basics
• Data Tools: NumPy, Pandas, Matplotlib
Practicals: 1-2 | Duration: 1 Week
Unit 2: Supervised Learning
Regression & Classification
• Linear & Polynomial Regression
• Classification Algorithms
• Model Evaluation
• Performance Metrics
Practicals: 3-5 | Duration: 2 Weeks
Unit 3: Unsupervised Learning
Clustering & Dimensionality
• K-Means Clustering
• Hierarchical Clustering
• PCA & Dimensionality Reduction
• Anomaly Detection
Practicals: 6-8 | Duration: 2 Weeks
Unit 4: Advanced Topics
Neural Networks & Deep Learning
• Artificial Neural Networks
• Deep Learning (CNN, RNN)
• Ensemble Methods
• XGBoost & Advanced Models
Practicals: 9-10, 12-13 | Duration: 2 Weeks
Unit 5: Ethics & Production
Ethics, Deployment & Real-World Applications
• ML Ethics & Bias
• Privacy & GDPR
• Model Deployment
• Case Studies
Practicals: 11, 14-15 | Duration: 1 Week
Unit Overview
| Unit | Title | Topics | Practicals | Duration |
|---|---|---|---|---|
| Unit 1 | Introduction to ML | Fundamentals, Types, Python | 1-2 | 1 Week |
| Unit 2 | Supervised Learning | Regression, Classification, Evaluation | 3-5 | 2 Weeks |
| Unit 3 | Unsupervised Learning | Clustering, Dimensionality, Anomaly | 6-8 | 2 Weeks |
| Unit 4 | Advanced Topics | Neural Networks, Deep Learning, Ensembles | 9-10, 12-13 | 2 Weeks |
| Unit 5 | Ethics & Production | Ethics, Deployment, Applications | 11, 14-15 | 1 Week |