Module 1: Introduction to AI & ML

• What is AI, ML, and Deep Learning

• Applications of AI in industries

• Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning

Module 2: Data Preprocessing & Exploration

• Data collection and cleaning

• Handling missing data

• Feature engineering and scaling

Module 3: Supervised Learning

• Linear & Logistic Regression

• Decision Trees & Random Forests

• Model evaluation metrics

Module 4: Unsupervised Learning

• Clustering: K-Means, Hierarchical

• Dimensionality Reduction: PCA

Module 5: Model Deployment & Basics of AI Ethics

• Introduction to AI deployment

• Bias & fairness in AI models