Free GenAI Course: Build a Real AI Agent from Scratch
LogicWiz's GenAI course is a free, hands-on generative AI course that teaches you to build a working AI agent in Python, not to watch someone else build one. All 38 lessons are free with no signup and no payment. Every lesson pairs written material with an in-browser lab and an AI tutor.
The Rise of Your AI Agent Empire — Build Nova — your own intelligent AI assistant — from first line of code to full agentic system.
What You'll Build in This Free GenAI Course
One project runs the length of the course. You build Nova, an AI assistant, in 38 lessons across 9 modules, and each module leaves you with something that runs:
- A first Python script, written from scratch, with no prior Python assumed.
- A prompted assistant that calls a real language model, once you know what a token and a context window cost you.
- An agent loop: Nova receives a goal, picks an action, runs a tool, reads the result and decides again.
- Retrieval over a private archive, built the long way, through embeddings, chunking, indexing and retrieval (RAG).
- A team of agents orchestrated with LangGraph, rather than one model doing everything.
- A voice interface, speech in and speech out, deployed behind an API.
Who This GenAI Course Is For
Complete beginners are the default reader: the course opens with Python from scratch (variables, data structures, conditionals, loops and functions) before it touches a language model, so no prior Python is assumed. It also suits self-taught engineers, students and working developers who can already code but have only used generative AI through a chat box and want to understand the agent loop, retrieval and orchestration underneath it.
Free GenAI Tutorials
- Build an AI Agent From Scratch in Python, A working AI agent in ~60 lines of Python, no framework. The agent loop, tool calling, memory, and the failure modes nobody warns you about.
What "hands-on" actually means in a free Generative AI course — a checklist for telling a genuinely hands-on AI course from one wearing the label.
Free GenAI Course Syllabus (38 Lessons, 9 Modules)
Every lesson below is linked and readable right now. Nothing here is behind a payment or an account.
Awakening the Machine
Learn Python — the language of AI — and take your first steps toward building Nova
Building the Brain's Toolkit
Give Nova organized memory and the ability to make decisions
Leveling Up Your Powers
Build reusable abilities, unlock Python's ecosystem, and master data analysis
The Agent Awakens
Meet the machines behind Nova — from generative AI and LLMs to prompts, tools, memory, and the reflex-to-utility family of agents
- The Machine That Learned to Improvise
- Nova Finds Her Voice
- The Art of the Prompt
- The Two Walls
- The Agent Loop
- The Agent Family
Nova Reads the Archives
Retrieval-Augmented Generation — give Nova a searchable memory of LogicWizNews's private archive so she answers from real facts instead of guessing. Embeddings, semantic search, chunking, indexing, and retrieval.
- Why Nova Needs RAG
- Embeddings & Semantic Search
- Chunking: Breaking Down Long Documents
- Indexing & Retrieval
Nova Goes to Production
Turn Nova's RAG prototype into a real product — orchestrate the whole pipeline with LangChain, hold a multi-turn conversation, measure answer quality, and let Nova reason with tools (agentic RAG).
- One Chain to Rule Them All
- The Goldfish Problem
- Is Nova Actually Good?
- Nova Learns to Think in Steps
Nova Builds a Team
Multi-agent systems: when one agent is not enough, the patterns that coordinate many (routing, orchestrator-worker, parallelization, sub-agents), and building it for real with LangGraph.
Nova Finds Her Voice
Conversational & multimodal AI: how models generate one token at a time, why tokens cost what they do, seeing and hearing (vision, music, audio), voice agents, natural conversation, and a voice-enabled multi-agent shopping assistant.
- One Word at a Time
- The Price of Every Word
- Nova Learns to See and Hear
- Nova Gets a Voice
- Talking to Nova, Naturally
- Nova Runs the Store
Nova Joins the Network
Give Nova reach beyond her frozen memory. Wire her to live tools, then tame the N×M wiring explosion with MCP — the Model Context Protocol: client/server, the list_tools/call_tool handshake, transports, gateways, and semantic tool-routing. Then go one level up to A2A, where agents discover and delegate tasks to each other, and learn to choose the right protocol for any job.
- Why Nova Reaches Beyond Herself
- The N×M Explosion
- Enter MCP: One Plug for Every Tool
- The MCP Handshake
- When (and When Not) to Use MCP
- MCP at Scale: Gateways
- A2A: When Agents Talk to Agents
- Choosing Your Protocol
Frequently Asked Questions About This Free GenAI Course
Is the LogicWiz GenAI course free?
Yes. All 38 lessons and their hands-on labs are free, with no signup and no payment. You can open lesson one and start building immediately.
How much does the full GenAI course cost?
Nothing. Every one of the 38 lessons is free, and there is no upgrade needed to read or run any of them. LogicWiz separately offers a Placement Pro membership at ₹999 per month for career support alongside the course, but it buys no additional lessons.
Do I need to know Python before starting?
No. The course opens with Python from scratch (variables, data structures, conditionals, loops and functions) before it touches language models, so a complete beginner can follow it end to end.
What do you actually build in this GenAI course?
You build Nova, an AI assistant, across the whole course: starting as a first Python script, gaining prompting and an agent loop, then retrieval over a private archive, then multi-agent orchestration with LangGraph, and finally a voice interface deployed as an API.
What topics does the GenAI course cover?
38 lessons across 9 modules: Python foundations, LLMs and prompt engineering, the agent loop and agent families, tool use, retrieval-augmented generation (embeddings, chunking, indexing, retrieval), LangGraph orchestration and multi-agent systems, transformer internals including tokenization and attention, and speech-to-text and text-to-speech pipelines.
What is the difference between an AI agent and a chatbot?
A chatbot completes one turn. You send text, it returns text, and control returns to your program. An agent runs a loop: it receives a goal, decides on an action, executes a tool, observes the result and decides again until the goal is met. The model, not your code, chooses the next step.
Are there hands-on exercises, or just reading?
Every lesson pairs written material with an in-browser lab containing practical exercises, plus an AI tutor you can ask questions mid-lesson.