MACHINE LEARNING COURSE CONTENT
1. Introduction to Machine Learning
- What is Machine Learning?
- Applications of ML
- AI vs ML
- Types of ML (Supervised / Unsupervised / RL)
- Python Libraries for ML
- ML Terminology & Collaboration
2. Supervised Learning
- Key Features & ML Metrics
- Classification Methods
- K Nearest Neighbors
- Naive Bayes Algorithm
- Logistic Regression
- Support Vector Machines
- Linear Regression
3. Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis
- Apriori Algorithm
- Anomaly Detection
- Dimensionality Reduction
4. Reinforcement Learning
- Agents and Environments
- Reward-Based Learning
- Policy Optimization Introduction
5. ML Workflow & Process
- Data Collection & Cleaning
- Feature Engineering
- Model Selection
- Training / Testing Split
- Model Evaluation Techniques
6. Final Project Work
- Real-world project: e.g., Heart Disease Prediction
- Data Preprocessing & Visualization
- End-to-End Implementation
