Full Curriculum
A carefully structured journey from Python basics to shipping production-ready Generative AI applications — all in 12 months, every weekend.
Foundations & Python Programming
Build a rock-solid programming foundation before touching any AI. We start from scratch — no experience needed.
Python Basics
- Setting up your development environment
- Variables, data types, conditionals & loops
- Functions, scope, and error handling
- Lists, dictionaries, tuples, and sets
- File I/O and working with external libraries
Advanced Python
- Object-Oriented Programming (OOP) concepts
- Decorators, generators, and iterators
- Async programming and concurrency
- NumPy for numerical computing
- Pandas for data manipulation and analysis
Math for AI
- Linear algebra: vectors, matrices, operations
- Probability & statistics essentials
- Calculus concepts (derivatives, gradients)
- Data visualization with Matplotlib & Seaborn
- Mini-project: Exploratory Data Analysis
Machine Learning & Deep Learning
Understand how machines learn from data. Train your first neural networks and grasp the theory behind AI.
Machine Learning Fundamentals
- Supervised vs Unsupervised learning
- Linear & Logistic Regression
- Decision Trees, Random Forests, XGBoost
- Model evaluation: accuracy, precision, recall, F1
- Project: Build a student performance predictor
Neural Networks
- Biological vs artificial neurons
- Feedforward networks and backpropagation
- Activation functions (ReLU, Sigmoid, Softmax)
- PyTorch basics: tensors, autograd, training loop
- Project: Handwritten digit classifier (MNIST)
Convolutional Neural Networks
- Convolution, pooling, and feature maps
- CNN architectures: VGG, ResNet, EfficientNet
- Transfer learning and fine-tuning
- Image classification and object detection basics
- Project: AI-powered plant disease detector
Generative AI — GANs, Diffusion & LLMs
The heart of the course. Learn to generate images, write prompts like an expert, and work with the world's most powerful language models.
GANs & Diffusion Models
- Generative Adversarial Networks (GANs) architecture
- Training GANs: mode collapse, tips & tricks
- Variational Autoencoders (VAEs)
- Intro to Diffusion Models (DDPM, DDIM)
- Project: Generate custom AI artwork with Stable Diffusion
Transformers & LLMs
- Attention mechanism explained intuitively
- Transformer architecture (encoder-decoder)
- GPT, BERT, T5 — how they differ
- Hugging Face Transformers library
- Project: Build a custom text summarizer
Prompt Engineering & RAG
- Zero-shot, few-shot, and chain-of-thought prompting
- Retrieval-Augmented Generation (RAG) architecture
- Vector databases: Pinecone, ChromaDB
- Building a Q&A bot over your own documents
- Project: AI customer support chatbot with RAG
Capstone, Ethics & Career Launch
Put it all together. Build a complete GenAI product, create your portfolio, and prepare for internships and college.
Building AI Agents
- What are AI Agents? ReAct framework
- Tool use: web search, code execution, APIs
- LangChain and LangGraph basics
- Memory, planning, and multi-agent systems
- Project: Build an autonomous research agent
Ethics, Safety & Deployment
- Bias, fairness, and responsible AI
- Hallucination mitigation strategies
- Deploying AI apps: FastAPI + Docker
- Cloud deployment: Vercel, Railway, HuggingFace Spaces
- Monitoring and logging AI applications
Capstone Project & Career Prep
- Full-stack GenAI SaaS application build
- Portfolio website & GitHub profile optimization
- Resume building and LinkedIn for students
- Mock interviews with industry mentors
- Demo Day: Present to founders & tech leads
Course Outcomes
10+ hands-on AI projects in your portfolio
Industry-recognized certificate with QR verification
Internship referrals from partner companies
Strong college application with AI project showcase
Experience with PyTorch, HuggingFace, LangChain, Docker
Deep understanding of how modern AI systems work
Ready to Start Your AI Journey?
New batch starting soon — weekend only, seats are limited to 30 per batch.