Tutorials
100 Days of ML
100 Days of DL
100 Days of NLP
MCP Trilogy
FastAPI for ML
PyTorch Deep Learning
LangChain & GenAI
LangGraph Agents
Code Lab (LeetCode AI)
Syllabus Path
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Free AI Course for Beginners — 8 tracks covering Machine Learning, Deep Learning, LangChain, LangGraph, RAG, MCP & Multi-Agent AI in Hindi & English
Free Generative AI tutorial — 8 structured tracks covering LangChain, LangGraph, ML, Deep Learning, NLP, PyTorch, FastAPI, and MCP. Pick any track, start today.
From Python basics to production ML — EDA, feature engineering, supervised & unsupervised algorithms, model evaluation, and deployment.
Perceptrons to Transformers — MLPs, backpropagation, CNNs, RNNs, LSTMs, attention, and the full Transformer architecture from scratch.
NLP pipeline to production — tokenization, TF-IDF, Word2Vec, text classification, HMMs, POS tagging, and a real duplicate-detection case study.
Build real LLM-powered applications — chains, RAG pipelines, memory, tools, and end-to-end GenAI apps with the LangChain ecosystem.
Stateful, multi-step AI agents — build graph-based agent workflows, human-in-the-loop systems, and production-grade agentic pipelines.
Model Context Protocol — connect AI agents to tools, APIs, and data sources. Build MCP servers and clients for production agent systems.
PyTorch from tensors to custom training loops — autograd, datasets, dataloaders, custom models, and GPU-accelerated training.
Serve ML models as production REST APIs — request validation, async endpoints, background tasks, Docker, and deployment patterns.
Test your AI and Machine Learning coding skills. Code activation functions, linear algebra, losses, and scalers in Python WebAssembly.
Samjho toh sab simple hai. AI sirf computers ko sikhana hai — jaise ek bachche ko sikhate hain.
Artificial Intelligence (AI) is the broad field of making computers think and act like humans. Think of it as a tree with three main branches:
Machine Learning (ML) — Teaching computers to learn from data without explicit programming. Example: predicting house prices based on past sales data.
Deep Learning (DL) — A more advanced branch of ML that uses neural networks (inspired by the human brain) to learn complex patterns. Example: recognizing faces in photos.
Generative AI (GenAI) — The newest and most exciting branch. AI that creates new content — text, images, code, music — that never existed before. Example: ChatGPT writing essays, Midjourney creating art.
Kya difference hai? Kya pehle seekhna chahiye? Sab samajh lo ek baar mein.
| Aspect | Machine Learning | Deep Learning | Generative AI |
|---|---|---|---|
| What it does | Learns patterns from data to make predictions | Learns complex patterns using neural networks | Creates entirely new content (text, images, code) |
| Data needed | Structured data (tables, spreadsheets) | Large amounts of unstructured data (images, text) | Massive datasets from the internet |
| Computing power | Moderate (laptop is enough) | High (GPU recommended) | Very High (powerful GPUs / cloud) |
| Examples | Spam detection, price prediction, recommendation systems | Image recognition, speech-to-text, self-driving cars | ChatGPT, Midjourney, GitHub Copilot |
| Best for beginners? | ✅ Yes — start here | ⚠️ After ML basics | ⚠️ After ML + DL |
4 simple steps. Har ML project follow karta hai ye same pipeline.
Ye hai wo rasta jo top companies mein placement dilata hai. Step-by-step, no shortcuts.
Stay updated with cutting-edge developments. Our tutorials evolve as AI evolves.
The "Attention Is All You Need" paper (2017) introduced the Transformer, the backbone of modern LLMs like GPT-4, Claude, and Gemini. Our Deep Learning tutorial covers Transformers from scratch with full mathematical derivation.
RAG combines the power of LLMs with external knowledge bases, solving hallucination problems. It is now the industry standard for building production AI chatbots. Our LangChain tutorial covers RAG end-to-end.
Instead of one AI doing everything, multiple specialized AI agents collaborate — like a team. LangGraph is the leading framework. Our LangGraph tutorial teaches stateful multi-agent design patterns.
Diffusion models learn by gradually adding noise to images and then reversing the process. Powers DALL-E 3, Midjourney, and Stable Diffusion. Our DL tutorial covers the math behind diffusion.
Anthropic's open standard for connecting AI models to external data sources and tools. The "USB-C for AI applications." Our MCP tutorial is the most comprehensive free resource in India.
MoE architectures use sparse expert networks to scale to trillions of parameters efficiently. Powers GPT-4, Mixtral, and Gemini. Our DL tutorial explains MoE with intuitive analogies.
Search karo, milega. India's most comprehensive free AI curriculum.
Generative AI refers to AI models that can create text, images, code, and more. Learning it gives you the skills to build AI-powered products, automate workflows, and access the fastest-growing job category in tech. Our free Generative AI tutorial covers LangChain, LangGraph, RAG, and agentic AI from scratch.
Yes — 100% free, forever. All 8 tutorial tracks on GenAIWallah are openly accessible. No paywall, no signup required. We believe in free-first AI education for India.
Start with 100 Days of Machine Learning — it begins from Python basics and builds up to deployment. Once done, move to 100 Days of Deep Learning, then the LangChain Generative AI tutorial to dive into GenAI.
Yes. GenAIWallah tutorials are in Hindi + English (Hinglish format). We're building India's best free Generative AI tutorial resource so learners from every college and city can access quality AI education in their language.
LangChain is a framework for building LLM-powered chains and RAG applications. LangGraph extends LangChain with stateful, graph-based agentic workflows — useful when you need complex multi-step AI agents with memory and human-in-the-loop control. Both are covered in our free Generative AI tutorial.
RAG (Retrieval-Augmented Generation) is a technique where an LLM retrieves relevant documents from a vector database before generating a response, making answers more accurate. Our free LangChain tutorial and LangGraph tutorial both cover RAG with full code examples.
Multi-agent AI uses multiple AI agents with specific roles collaborating on complex tasks. LangGraph is the top framework for it. Our free LangGraph multi-agent tutorial covers design patterns, stateful graphs, and production deployment in Hindi and English.
MCP is Anthropic's open standard letting AI models connect to external tools and data. Our free MCP tutorial covers Claude MCP setup, tool bindings, and remote SSE proxies — the most complete MCP course available free in India.
Yes. Our Machine Learning course starts from Python basics — zero prior coding needed. All content is in Hindi + English (Hinglish) so anyone from any background can learn AI. GenAIWallah is built specifically for Tier 2/3 college students and career switchers across India.
AI is the broad field of making machines intelligent. ML is a subset of AI where machines learn from data. Deep Learning is a subset of ML that uses neural networks with many layers. Think of it like: AI = All vehicles, ML = Cars, DL = Sports cars. Our tutorials cover all three levels — ML, DL, and GenAI.
Basic math (class 12 algebra and statistics) is helpful but not required to start. We introduce concepts like gradient descent, linear algebra, and probability intuitively with real-world analogies. You will build mathematical intuition as you progress through the 100 Days of ML course. No advanced calculus needed to begin.
Yes! 2,000+ students from GenAIWallah have been placed in AI/ML roles. The key is not just watching tutorials but building projects. Complete the 100 Days of ML → build 3 end-to-end projects → create a portfolio → apply. Our Foundation Program includes resume reviews, mock interviews, and job referrals.
A neural network is a computer system inspired by the human brain. It has layers of connected "neurons" that process information. When you show it many examples (like photos of cats), it adjusts its connections to recognize patterns. The more layers it has, the "deeper" it is — hence Deep Learning. Our Deep Learning tutorial builds a neural network from scratch using Python.
GenAIWallah's 100 Days of Machine Learning is India's best free AI course for beginners. It starts from Python basics, covers the full ML pipeline, and includes real-world projects — all in Hindi + English. No IIT degree required. No payment required. Join 10,000+ students learning AI for free.
Prompt engineering is the art of writing effective instructions to get better outputs from AI models like ChatGPT. A well-crafted prompt can be the difference between useless and brilliant results. It's one of the highest-demand skills in AI right now. Our LangChain tutorial covers advanced prompt engineering techniques including few-shot prompting and chain-of-thought.
With consistent effort (2-3 hours daily), you can complete our Machine Learning track in 3 months, Deep Learning in 2 months, and Generative AI in 1.5 months. That's about 6-7 months to go from zero to being able to build real AI applications. Students who follow our 90-day mentorship program often get placed within 4-6 months.