3-Month Intensive Practical Crash Course
Industry-Ready Applied Generative AI
72–80 hours of code-first training for undergraduate engineers. Build a transformer from scratch, ship a production RAG engine, fine-tune vision SLMs, orchestrate agents and deploy a capstone product.
B.Tech / B.E. CS, IT, AI & Data Science
12 Weeks · 36 Lectures · 2 hrs each
75% hands-on coding in Colab / VS Code
TurboVec · MTP · dFlash · ColBERT · VLM OCR
12 Weeks
3-Month Intensive
36 Lectures
2 hrs per lecture
72–80 Hours
Total Duration
75% Hands-on
Colab / VS Code
Course Philosophy
Engineered to bridge academia and the GenAI job market
Modern production AI relies on practical software harness integration, efficient vector search engines, multi-token prediction architectures, lightweight open-weight models and continuous fine-tuning.
Pragmatic Depth
No research-level proofs. Intuitive maths behind self-attention, loss functions and tokenization, then straight into code implementation.
Code-First After Week 1
From Lecture 4 onwards, 75% of class time is spent inside Google Colab or a local VS Code environment.
Cutting-Edge Modernity
TurboVec, Multi-Token Prediction, dFlash attention, ColBERT late interaction, Vision-Language OCR and CLI coding harnesses.
Course Philosophy
9 Modules. 36 Lectures. 9 shipped outcomes.
Generative AI Foundations & Architecture
L01 – L04 (8h)
Custom BPE Tokenizer & PyTorch Attention Block
Building & Training an LLM from Scratch
L05 – L08 (8h)
Working NanoGPT-style Transformer running locally / Colab
Advanced RAG, ColBERT & TurboVec
L09 – L12 (8h)
Production RAG Pipeline with TurboVec & Multi-Vector ColBERT
Fine-Tuning, SLMs & Vision-Language OCR
L13 – L16 (8h)
Fine-Tuned Qwen / SLM for Structured JSON Extraction
Inference Optimization: MTP, dFlash & Quant
L17 – L20 (8h)
Deploying GGML / vLLM engine with Speculative MTP & Quantization
Autonomous AI Agents & Tool Calling
L21 – L24 (8h)
Multi-Agent System with ReAct, Function Calling & Memory
Developer Workflows & CLI Coding Harnesses
L25 – L28 (8h)
Automated Feature Build using Claude Code / OpenCode / Gemini CLI
Reinforcement Learning (RLHF, DPO & GRPO)
L29 – L32 (8h)
Fine-Tuning an LLM with DPO / GRPO for Reasoning
Capstone Project & Industry Deployment
L33 – L36 (8h)
Production-Grade AI Application Deployed with Docker & API
Detailed Syllabus
Lecture-by-lecture 72-hour breakdown
Every lecture pairs core topics with a hands-on Colab lab and curated learning resources.
75% of class time is spent writing
real production code.
From Lecture 4 onwards you live inside Google Colab and VS Code — not slides.
Assessment Architecture
Graded on a job-ready portfolio., not exams
Weekly Lab Coding Notebooks
Submission of completed Google Colab notebooks from Lectures 4 through 32.
Mid-Term Milestone: Custom RAG Engine
Build and evaluate an advanced RAG engine using ColBERT, TurboVec and Ragas metrics.
Fine-Tuned VLM Document Extractor
Fine-tune a 3B Vision SLM to convert unstructured PDF / image documents into validated JSON.
Final Capstone Project
Full-stack production GenAI product deployed with Docker, FastAPI, tool agents and CLI harness integration.
Detailed Syllabus
The exact stack production teams ship with
500+ Hiring Partners
Top global firms across startups, IT services & product companies.
Hardware & Environment
You only need a laptop and free Colabt
Student Laptop
Any modern PC / Mac / Linux machine with at least 16GB RAM and VS Code installed.
Compute Infrastructure
Free-tier Google Colab (T4 GPU) covers all core sessions. Optional Colab Pro or RunPod credit ($10–$15) for faster fine-tuning in Modules 4 and 8.
Python Stack
Python 3.11+, PyTorch 2.x, Transformers, PEFT, TRL, Unsloth, LangChain / LangGraph, llama.cpp, vLLM, TurboVec, RAGatouille, E2B SDK.
Enroll Now
Start your 12-week journey to a GenAI
engineering role
Limited seats per batch to keep lab support personal. Share your details and our team will walk you through the schedule, fees and the demo lecture.
Generative AI Courses in Pune with Placement Assistance | Teknowell
If you are planning to build a career in Generative AI, choosing the right skills and technology stack is extremely important. Some students want to work in AI startups, some are targeting stable IT jobs, while others want practical AI and coding skills that can help them perform better in interviews and real-world projects. At Teknowell, the focus is not just on teaching AI theory. Students learn how Generative AI applications are actually built using Python, Machine Learning, Deep Learning, Large Language Models (LLMs), Prompt Engineering, APIs, databases, and AI-powered tools. Whether you are a complete beginner, a college student, or someone planning a career switch into AI and IT, these Generative AI courses in Pune are designed to help you learn through practical implementation, hands-on coding, and real-time projects rather than just slides and notes.
What You Get During Training
- ✓ Live instructor-led Classes
- ✓ Real-time project training
- ✓ Practical coding sessions
- ✓ Placement assistance support
- ✓ Interview preparation guidance
- ✓ Flexible weekday and weekend batches
- ✓ Online and classroom training in Pune
FAQ
Generative AI crash course questions
Q1: Who is this crash course designed for?
B.Tech / B.E. students in Computer Science, IT, AI & Data Science who want an industry-ready Generative AI portfolio before graduating.
Q2: How much of the course is hands-on coding?
75% hands-on coding in Google Colab or local VS Code, and 25% high-level intuition. From Lecture 4 onward almost every session is code-first.
Q3: Do I need a GPU laptop?
No. Any machine with 16GB RAM and VS Code works. Free-tier Google Colab with a T4 GPU is sufficient for all core coding sessions.
Q4: What makes this curriculum different from other GenAI courses?
It covers the newest production stack: TurboVec vector indexing, Multi-Token Prediction, dFlash attention, ColBERT late interaction, Vision-Language OCR and CLI coding harnesses.
Q5: What will I actually build?
A custom BPE tokenizer, a NanoGPT transformer trained from scratch, a production RAG engine, a fine-tuned Vision SLM JSON extractor, multi-agent systems and a Dockerized capstone product.
Q6: How is the course assessed?
30% weekly lab notebooks, 20% mid-term RAG engine, 20% fine-tuned VLM extractor and 30% final capstone project — no traditional written exams.