Module 1: Python for AI & Data Science (6 Hours)

Topics:

• Python basics, functions, OOPs concepts
• NumPy and Pandas for data manipulation
• Matplotlib and Seaborn for data visualization
• Working with CSV, JSON, and databases
• Introduction to Jupyter Notebook and Google Colab

Module 2: Mathematics for AI & ML (6 Hours)

Topics:

• Linear Algebra: Vectors, Matrices, Eigenvalues
• Probability & Statistics: Bayes’ Theorem, Normal Distribution, Hypothesis Testing
• Optimization: Gradient Descent, Cost Functions

Module 3: Machine Learning (20 Hours)

Supervised Learning

• Introduction to Machine Learning
• Data Preprocessing & Feature Engineering
• Regression Models: Linear, Polynomial, Lasso, Ridge
• Classification Models: Logistic Regression, Decision Trees, SVM, Naive Bayes
• Ensemble Learning: Random Forest, XGBoost
• Model Evaluation: Accuracy, Precision, Recall, F1-score, ROC-AUC

Unsupervised Learning

• Clustering: K-Means, DBSCAN, Hierarchical Clustering
• Dimensionality Reduction: PCA, t-SNE
• Anomaly Detection

Module 4: Deep Learning (20 Hours)

Neural Networks & Deep Learning Basics

• Introduction to Neural Networks
• Activation Functions: ReLU, Sigmoid, Softmax
• Forward and Backpropagation
• Loss Functions and Optimization Techniques (Adam, SGD)

Convolutional Neural Networks

• CNN Architecture: Filters, Pooling, Padding
• Transfer Learning with Pretrained Models (VGG16, ResNet)
• Image Classification and Object Detection

Recurrent Neural Networks & Transformers

• RNNs, LSTMs, GRUs
• Introduction to Transformers and BERT
• Natural Language Processing (NLP) Basics

Module 5: Data Science & AI Applications (10 Hours)

Topics:

• Real-world AI use cases: Healthcare, Finance, E-commerce
• End-to-End ML Pipelines
• Deployment of AI models using Flask & FastAPI
• MLOps: Model Monitoring & Optimization
• Ethical AI and Bias in AI

Module 6: Capstone Projects (20 Hours)

• Project selection: Based on industry problems
• Steps: Data Collection → Model Building → Evaluation → Deployment

Module 7: Generative AI & Large Language Models (10 Hours)

Introduction to Generative AI

• What is Generative AI?
• Overview of GANs (Generative Adversarial Networks)
• Variational Autoencoders (VAEs)
• Hands-on: Generating images using GANs

Large Language Models (LLMs) & Transformers

• Understanding Transformers & Attention Mechanisms
• Pretrained Models: GPT, BERT, LLaMA, Mistral
• Fine-tuning LLMs for Custom Applications
• Hands-on: Fine-tuning GPT for text generation

AI Applications & Deployment

• Building AI chatbots with OpenAI API / Hugging Face
• Text-to-Image Generation (Stable Diffusion, DALL·E)
• Responsible AI: Ethical Considerations in Generative AI
• Deploying a Generative AI model using FastAPI

Module 8: Prompt Engineering & Efficient AI Interaction (8 Hours)

Fundamentals of Prompt Engineering

• What is Prompt Engineering? Why is it important?
• Understanding AI-generated responses (LLM mechanics)
• Types of prompts: Instructional, Few-shot, Zero-shot, Chain-of-Thought (CoT)
• Hands-on: Experimenting with different prompting techniques in ChatGPT

Advanced Prompting & Applications

• Role-specific prompting (e.g., AI for coding, writing, customer support)
• Fine-tuning AI responses using structured prompts
• Multi-turn conversation design for AI chatbots