Module 1: Mathematics for AI & ML

• Linear Algebra
• Probability and Statistics
• Calculus

Module 2: Programming for AI & ML

• Python for AI & ML
• Libraries: NumPy, Pandas, Matplotlib, and Seaborn

Module 3: Data Preprocessing and Visualization

• Data Cleaning and Transformation
• Feature Engineering
• Exploratory Data Analysis (EDA)

Module 4: Supervised Machine Learning

• Regression (Linear, Polynomial, Logistic)
• Classification (KNN, Decision Trees, SVM)
• Model Evaluation Metrics

Module 5: Unsupervised Machine Learning

• Clustering (K-Means, Hierarchical Clustering)
• Dimensionality Reduction (PCA, t-SNE)

Module 6: Deep Learning

• Neural Networks (ANN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN) and LSTMs
• Transfer Learning

Module 7: Natural Language Processing (NLP)

• Text Preprocessing
• Sentiment Analysis
• Topic Modeling
• Transformers (e.g., BERT, GPT)

Module 8: Reinforcement Learning

• Basics of Reinforcement Learning
• Markov Decision Processes (MDP)
• Q-Learning and Deep Q-Learning

Module 9: AI & ML Tools and Frameworks

• TensorFlow
• PyTorch
• Scikit-learn

Module 10: Model Deployment

• Saving and Loading Models
• Flask/Django for Model Deployment
• Deployment on Cloud Platforms (AWS, Azure, GCP)

Module 11: Ethics in AI

• Bias in AI
• Privacy Concerns
• Explainability and Fairness in AI

Module 12: AI & ML Applications

• Computer Vision
• Speech Recognition
• Recommender Systems
• Predictive Analytics

Module 13: Capstone Projects

• Real-world projects in AI & ML domains
• End-to-End Model Development and Deployment

Module 14: Advanced Topics

• Generative Adversarial Networks (GANs)
• AI for IoT
• AutoML and Hyperparameter Tuning