Hands-On Generative AI Course
7-week immersive live & hands-on program to build, deploy and apply Generative AI models and agent-based systems, from fundamentals to advanced workflows.
Meet Your Instructor: Keerti-Purswani
Keerti Purswani is a dynamic computer science educator and founder of Educosys, specializing in data structures, algorithms, system design, and generative AI education. With a passion for making complex technical concepts accessible, Keerti has helped thousands of students excel in competitive programming, technical interviews, and real-world software engineering challenges. Her comprehensive approach combines hands-on practice with deep conceptual understanding, preparing students for careers at top tech companies.
Experience: 10+ years
Students Helped: 50,000+
Specialization: DSA, System Design & AI Education
Course Overview
This comprehensive course is designed to take you from foundational concepts to advanced implementation in dsa, system design & ai education. You'll learn through hands-on project-based learning with live coding sessions, real-world case studies, and comprehensive doubt-clearing support, building real-world projects that demonstrate your skills and enhance your portfolio.
Whether you're looking to start a new career in technology or advance your current skills, this course provides the structured learning path and practical experience you need to succeed in today's competitive tech industry.
Course Curriculum
Course Syllabus
Week 1: Foundations of Generative AI – Introduction to AI; Mathematical Foundations; Probability, Statistics & Linear Algebra; Basics of Neural Networks; Gradient Descent & Optimization; Architectures: Feedforward, RNN, CNN; Mini Project: Build a Simple Neural Network; Mini Project: Train an Autoencoder on the MNIST Dataset
Week 2: Deep Generative Models – Discriminative & Generative Models; GANs; VAEs; Probabilistic Data Generation using VAEs; Four Mini Projects using TensorFlow; Metrics Visualization with TensorBoard; Mini Project: Implement a GAN to Generate Handwritten Digits; Mini Project: Train a VAE to Generate Faces Using the CelebA Dataset
Week 3: Transformers & Large Language Models – RNN, LSTM; Transformer Architecture; Attention Mechanism: Self-Attention & Positional Encoding; Major Project: Code a Transformer from Scratch; Encoder-Decoder Framework; Pretraining Objectives: MLM, CLM; GPT, BERT
Week 4: Fine-Tuning, LangChain, LangGraph – Pretraining & Fine-Tuning; LoRA, QLoRA; Hugging Face; Fine-Tuning for Tasks like Summarization & QA; LangChain Installation & Basic Setup; Overview of LangChain: Prompts, Memory, Chains, Agents; LangGraph: Nodes, State, StateGraph, Workflows; AI Agents; Mini Project: Simple Q&A App Using LangChain
Week 5: Vector Databases & RAG – Vector Databases; ChromaDB; Applications of RAG; Building RAG Pipelines with LangChain; Building Front-end using Streamlit; Major Project: Build End-to-End Chatbot like ChatGPT using Streamlit, LangGraph, ChromaDB, WebSearch Tools, Memory with LLMs; Project: Build App using Streamlit for Image Generation, Image Caption Generation, Video Caption Generation
Week 6: Trending Topics – MCP (Model Context Protocol); Ollama; Projects – Fine-Tuning using Unsloth; Mixture of Experts; Chain of Thoughts; Deepseek Architecture
Week 7: Projects & Advanced Topics – Distillation; Diffusion Models; Vision Transformers; Multimodal Models; CLIP; Prompt Engineering
Requirements
- Basics of Python (NumPy & Pandas recommended)
- Willingness to code and build projects
- Internet access for live classes and hands-on sessions
- Laptop or desktop for coding (phone alone possible but not ideal)
Course Features
Course Details
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