🚀 Complete AI Learning Roadmap

Your comprehensive guide to mastering GenAI and Agentic AI — from fundamentals to advanced deployment

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📋 Table of Contents

Click to expand/collapse

• 🔢 Math Foundations
• 🐍 Python Basics
• 🎨 Streamlit
• ⚡ FastAPI
• 🤖 Machine Learning — Core Basics
• 📊 Machine Learning — Deep Dive
• 📝 ML for NLP
• 🧠 Deep Learning Basics
• 🎯 Core Deep Learning
• 🛠️ DL Frameworks
• 🔧 MLOps
• 🔄 Transformers
• ✨ Introduction to Gen AI
• 🦾 Large Language Models (LLMs) - Advanced
• 🔗 Introduction to LangChain
• 🔍 RAG (Retrieval Augmented Generation)
• 💾 Vector Databases
• 🤝 Agentic AI
• 🧪 Harness Engineering
• 📈 LangGraph & Advanced Agents
• 🌐 Model Context Protocol (MCP)
• 🚀 Fine-tuning
• 🚀 LLMOps
• 📚 Additional Agentic Framework
• 📚 Additional Resources

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0. Math Foundations

S.No	Topic	Description	Resources
0	Math for ML/DL	Linear Algebra, Probability, Statistics, Calculus	🇮🇳 Hindi: CampusX Mathematics 🌍 English: 3Blue1Brown - Linear Algebra Khan Academy - Statistics

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1. Python Basics

S.No	Topic	Description	Resources
1	Python Fundamentals	Basics, data structures, file handling, exception handling, OOP	🇮🇳 Hindi: CampusX Python Chaiaurcode Python 🌍 English: FreeCodeCamp Python Corey Schafer Python

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2. Streamlit

S.No	Topic	Description	Resources
2	Streamlit Basics	UI building, web apps for ML	🇮🇳 Hindi: Chai aur Code Streamlit 🌍 English: Official Streamlit Tutorial Data Professor Streamlit

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3. FastAPI

S.No	Topic	Description	Resources
3	FastAPI Fundamentals	REST APIs, async programming, model deployment	🇮🇳 Hindi: CampusX FastAPI 🌍 English: Official FastAPI Docs FreeCodeCamp FastAPI

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4. Machine Learning — Core Basics

S.No	Topic	Description	Resources
4	ML Fundamentals	Classification, Regression, Pipelines, Feature Engineering	🇮🇳 Hindi: CampusX ML Course 🌍 English: Stanford CS229 Andrew Ng ML Course
5	ML Evaluation	Accuracy, Precision, Recall, Confusion Matrix, ROC-AUC	🇮🇳 Hindi: CampusX Model Evaluation 🌍 English: StatQuest ML Metrics Google ML Crash Course
6	Feature Scaling	Normalization, Standardization, MinMax, Robust Scaling	🇮🇳 Hindi: CampusX Feature Engineering 🌍 English: Scikit-learn Preprocessing
7	Data Labeling	Manual annotation, Label Studio, Roboflow	🇮🇳 Hindi: CampusX Data Labeling 🌍 English: Label Studio Docs Roboflow Tutorial

🛠 P1: Core ML Projects

Project	Description	Datasets	Tech Stack
ML Classification App	Build a classification app using sklearn + Streamlit	Iris, Titanic, MNIST	sklearn, Streamlit, pandas
Regression Price Predictor	Housing price prediction with feature engineering	Boston Housing, California Housing	scikit-learn, seaborn, matplotlib

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📊 5. Machine Learning — Deep Dive

S.No	Topic	Description	Resources
8	Unsupervised ML	Clustering (K-Means, DBSCAN, Hierarchical), Dimensionality Reduction (PCA, t-SNE, UMAP)	🇮🇳 Hindi: CampusX Clustering 🌍 English: StatQuest Clustering Google Developers Clustering
9	Ensemble Methods	Bagging, Boosting (XGBoost, LightGBM), Stacking	🇮🇳 Hindi: Krish Naik Ensemble CampusX Ensemble 🌍 English: StatQuest Ensemble Methods
10	Hyperparameter Tuning	GridSearchCV, RandomSearch, Optuna, Bayesian Optimization	🇮🇳 Hindi: CampusX Hyperparameter 🌍 English: Optuna Official Docs Hyperparameter Tuning Guide
11	Core ML Concepts	Bias-variance tradeoff, Underfitting/Overfitting, Regularization (L1/L2)	🇮🇳 Hindi: CampusX ML Concepts 🌍 English: Andrew Ng ML Concepts

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6. ML for NLP

S.No	Topic	Description	Resources
12	Traditional NLP	Text preprocessing, One-Hot Encoding, Bag of Words, TF-IDF, Word2Vec	🇮🇳 Hindi: CampusX NLP 🌍 English: Krish Naik NLP Hugging Face NLP Course

🛠 P2: NLP Projects

Project	Description	Datasets	Tech Stack
Text Classifier	Spam detection or sentiment analysis using BoW/TF-IDF	SMS Spam, IMDb Reviews	sklearn, NLTK, pandas
Word2Vec Explorer	Visualize similarity between words using Word2Vec	Google News Word2Vec	Gensim, matplotlib, seaborn

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7. Deep Learning Basics

S.No	Topic	Description	Resources
13	Deep Learning Fundamentals	Neural Networks, Loss Functions, Optimizers, Activation Functions	🇮🇳 Hindi: CampusX Deep Learning 🌍 English: Krish Naik DL Playlist 3Blue1Brown Neural Networks MIT 6.S191

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8. Core Deep Learning

S.No	Topic	Description	Resources
14	Neural Networks & ANN	Feedforward networks, backpropagation, gradient descent	🇮🇳 Hindi: CampusX ANN 🌍 English: Krish Naik DL Playlist MIT 6.S191 Deep Learning 3Blue1Brown Neural Networks
15	CNN	Convolutional Neural Networks for computer vision	🇮🇳 Hindi: CampusX CNN 🌍 English: Stanford CS231n DeepLizard CNN
16	RNN & LSTM	Sequential data modeling, time series	🇮🇳 Hindi: CampusX RNN/LSTM 🌍 English: MIT RNN Lecture Colah's LSTM Blog

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9. DL Frameworks

S.No	Topic	Description	Resources
17	PyTorch	Tensors, model building, training loops	🇮🇳 Hindi: CampusX PyTorch 🌍 English: PyTorch Official Tutorial FreeCodeCamp PyTorch

🛠 P3: Deep Learning Projects

Project	Description	Datasets	Tech Stack
Image Classifier	Build CNN to classify cats vs dogs	Dogs vs Cats (Kaggle)	TensorFlow/Keras, PyTorch
Sentiment with LSTM	Sentiment prediction using LSTM networks	IMDb, Twitter Sentiment	Keras, PyTorch, torchtext

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10. MLOps

S.No	Topic	Description	Resources
18	MLOps Fundamentals	Model versioning, experiment tracking, CI/CD for ML, monitoring	🇮🇳 Hindi: Krish Naik MLOps CampusX MLOps Krish Naik LLMOps Course 🌍 English: MLOps Community ML-Ops.org Best Practices
19	Model Deployment	Docker, cloud deployment, model serving, A/B testing	🇮🇳 Hindi: Krish Naik Docker 🌍 English: Docker for ML Full Stack Deep Learning
20	Experiment Tracking	MLflow, Weights & Biases, model registry	🇮🇳 Hindi: Krish Naik MLflow 🌍 English: MLflow Official Docs Weights & Biases Tutorial

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11. Transformers

S.No	Topic	Description	Resources
21	Transformer Architecture	Self-attention, Multi-head attention, Positional Encoding, Encoder-Decoder	🇮🇳 Hindi: CampusX Transformers Krish Naik Transformers 🌍 English: 3Blue1Brown Attention Illustrated Transformer Original Paper
22	Tokenization	BPE, SentencePiece, GPT-2 tokenizer, Hugging Face tokenizers	🇮🇳 Hindi: CampusX Tokenization 🌍 English: Andrej Karpathy Tokenizers Hugging Face Tokenizers

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12. Introduction to Gen AI

S.No	Topic	Description	Resources
23	GenAI Fundamentals	AI vs ML vs DL vs GenAI, How GPT/LLMs are trained, LLM evolution	🇮🇳 Hindi: Krish Naik GenAI CampusX GenAI Course 🌍 English: Fireship GenAI Two Minute Papers
24	LLM Evaluation	BLEU, ROUGE, Perplexity, Human Evaluation, Benchmarks	🇮🇳 Hindi: Krish Naik LLM Metrics 🌍 English: Hugging Face Evaluation
25	Ethics & AI Safety	Hallucination, bias, responsible deployment, alignment	🇮🇳 Hindi: Krish Naik AI Ethics 🌍 English: AI Safety Fundamentals Anthropic Safety Research

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🦾 13. Large Language Models (LLMs) - Advanced

S.No	Topic	Description	Resources
26	PEFT (Parameter Efficient Fine-Tuning)	LoRA, QLoRA, AdaLoRA, Prefix Tuning, P-Tuning	🇮🇳 Hindi: Krish Naik PEFT CampusX Fine-tuning Krish Naik Fine-tuning Course 🌍 English: Hugging Face PEFT LoRA Paper
27	LoRA & QLoRA	Low-Rank Adaptation, Quantized LoRA for efficient fine-tuning	🇮🇳 Hindi: Krish Naik LoRA 🌍 English: QLoRA Paper Practical LoRA Tutorial
28	Quantization Techniques	INT8, INT4, GPTQ, AWQ, GGML/GGUF formats	🇮🇳 Hindi: Krish Naik Quantization 🌍 English: BitsAndBytes GitHub GPTQ Paper
29	Model Compression	Pruning, Distillation, Quantization-Aware Training	🇮🇳 Hindi: Krish Naik Model Compression 🌍 English: Neural Compression Distillation Paper
30	Advanced Fine-tuning	Full fine-tuning vs PEFT, Instruction tuning, RLHF basics	🇮🇳 Hindi: Krish Naik RLHF CampusX Fine-tuning 🌍 English: Hugging Face Fine-tuning RLHF Paper

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14. Introduction to LangChain

S.No	Topic	Description	Resources
31	LangChain Fundamentals	Components, Chains, Agents, Memory	🇮🇳 Hindi: Krish Naik LangChain CampusX LangChain 🌍 English: LangChain Official Docs Sam Witteveen LangChain
32	LLM Integration	OpenAI, Ollama, Hugging Face, Groq integration	🇮🇳 Hindi: Krish Naik Ollama 🌍 English: Ollama Setup Guide Groq API Docs
33	Prompt Engineering	Zero-shot, few-shot, chain-of-thought, prompt optimization	🇮🇳 Hindi: CampusX Prompt Engineering Krish Naik Prompting 🌍 English: OpenAI Cookbook Prompt Engineering Guide Learn Prompting

🛠 P4: LangChain Projects

Project	Description	Tech Stack
Chatbot with LangChain	Build intelligent chatbot using LangChain + LLM + Streamlit	LangChain, Streamlit, Ollama/OpenAI
Document Summarizer	Summarize PDF/Text documents with LLMs	LangChain, PyPDF, Hugging Face Transformers
SQL Query Generator	Natural language to SQL using LangChain	LangChain, SQLAlchemy, OpenAI/Groq

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15. RAG (Retrieval Augmented Generation)

S.No	Topic	Description	Resources
34	RAG Fundamentals	Retrieval pipeline, embedding models, vector similarity	🇮🇳 Hindi: Krish Naik RAG CampusX RAG Tutorial 🌍 English: LangChain RAG Tutorial Pinecone RAG Guide
35	Advanced RAG	Multi-query retrieval, re-ranking, hybrid search	🇮🇳 Hindi: Krish Naik Advanced RAG 🌍 English: Advanced RAG Techniques RAG from Scratch

🛠 P5: RAG Projects

Project	Description	Tech Stack
PDF Q&A with RAG	Upload PDF → extract → chunk → embed → query via LLM	LangChain, FAISS, OpenAI/Groq, Streamlit
Multi-Document RAG	Query across multiple documents with source attribution	ChromaDB, LangChain, sentence-transformers
Web Scraper + RAG	Scrape websites and build RAG system	BeautifulSoup, LangChain, Pinecone

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16. Vector Databases

S.No	Topic	Description	Resources
36	Vector DB Fundamentals	FAISS, ChromaDB, Pinecone, Weaviate, similarity search	🇮🇳 Hindi: Krish Naik Vector DB CampusX Vector Databases 🌍 English: Pinecone Documentation ChromaDB Official Docs FAISS Tutorial
37	Embedding Models	sentence-transformers, OpenAI embeddings, custom embeddings	🇮🇳 Hindi: Krish Naik Embeddings 🌍 English: Sentence Transformers Docs OpenAI Embeddings Guide

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17. Agentic AI

S.No	Topic	Description	Resources
38	AI Agent Fundamentals	Agent architecture, planning, tool use, memory systems	🇮🇳 Hindi: Krish Naik AI Agents CampusX Agentic AI 🌍 English: Lilian Weng's Agent Blog DeepLearning.AI Agents
39	Tool-Using Agents	Function calling, external APIs, code execution	🇮🇳 Hindi: Krish Naik Function Calling 🌍 English: OpenAI Function Calling LangChain Tools
40	Multi-Agent Systems	Agent collaboration, communication protocols	🇮🇳 Hindi: Krish Naik CrewAI 🌍 English: Microsoft AutoGen CrewAI Documentation
41	ReAct & Planning	Reasoning + Acting, chain-of-thought for agents	🇮🇳 Hindi: Krish Naik ReAct 🌍 English: ReAct Paper ReAct Tutorial

🛠 P6: Agentic AI Projects

Project	Description	Tech Stack
Research Assistant Agent	AI agent that can search web, summarize, and synthesize information	LangChain, Tavily/SerpAPI, OpenAI
Code Review Agent	Agent that reviews code, suggests improvements, runs tests	GitHub API, LangChain, code execution tools
Multi-Agent Workflow	Multiple agents collaborating on complex tasks	CrewAI, AutoGen, LangChain

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🧪 Harness Engineering

Harness Engineering is the practice of building reliable test harnesses, eval loops, observability, and guardrails around AI agents so they can be shipped safely. Use this section after learning agents, LangGraph, MCP, and LLMOps.

S.No	Topic	Description	Resources
HE-1	Harness Engineering Fundamentals	AI system harnesses, eval suites, test environments, feedback loops, reliability workflows	🌍 English: Awesome Harness Engineering Learn Harness Engineering
HE-2	Practical Learning Path	Build small harnesses for prompts, tools, RAG, agents, regression tests, and production monitoring	Suggested practice: Create eval datasets for agent tasks Run deterministic regression tests before changing prompts or tools Track failures, latency, cost, and tool-call accuracy Add CI checks for critical AI workflows

🛠 Harness Engineering Projects

Project	Description	Tech Stack
Prompt Regression Harness	Test prompt changes against fixed examples before shipping	Python, pytest, LLM API
RAG Evaluation Harness	Measure retrieval quality, groundedness, and answer consistency	LangChain/LlamaIndex, vector DB, eval dataset
Agent Tool-Use Harness	Validate tool calls, retries, failures, and final answers for agents	LangGraph, pytest, mock APIs

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18. LangGraph & Advanced Agents

S.No	Topic	Description	Resources
42	LangGraph Fundamentals	State machines, graph-based workflows for agents	🇮🇳 Hindi: CampusX LangGraph Krish Naik LangGraph 🌍 English: LangGraph Official Docs LangGraph Tutorial
43	Complex Agent Workflows	Multi-step reasoning, conditional flows, human-in-the-loop	🇮🇳 Hindi: CampusX Advanced LangGraph 🌍 English: LangGraph Advanced Patterns Building Complex Agents
44	Agent Orchestration	Managing multiple agents, workflow optimization	🇮🇳 Hindi: CampusX Agent Orchestration 🌍 English: LangGraph Multi-Agent

🛠 P7: LangGraph Projects

Project	Description	Tech Stack
Multi-Step Research Agent	Agent that plans research, gathers info, and creates reports	LangGraph, multiple LLMs, web search APIs
Customer Service Agent	Complex customer service with escalation and human handoff	LangGraph, FastAPI, database integration
Autonomous Data Analyst	Agent that analyzes data, creates visualizations, generates insights	LangGraph, pandas, plotly, LLMs

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19. Model Context Protocol (MCP)

S.No	Topic	Description	Resources
45	MCP Fundamentals	Protocol for connecting AI assistants to external data sources and tools	🇮🇳 Hindi: CampusX MCP Series 🌍 English: MCP GitHub Repository MCP Official Documentation
46	MCP Implementation	Building MCP servers, client integration, tool development	🇮🇳 Hindi: CampusX MCP Implementation 🌍 English: Building MCP Servers MCP Examples

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21. Fine-tuning

S.No	Topic	Description	Resources
48	Fine-tuning Fundamentals	Full fine-tuning, transfer learning, domain adaptation	🇮🇳 Hindi: Krish Naik Fine-tuning Course SunnySavita Fine-tuning 🌍 English: Hugging Face Fine-tuning Guide Fine-tuning Best Practices
49	Parameter Efficient Fine-tuning	LoRA, QLoRA, AdaLoRA, Prefix Tuning, P-Tuning	🇮🇳 Hindi: Krish Naik PEFT CampusX PEFT Tutorial 🌍 English: Hugging Face PEFT LoRA Paper
50	Instruction Tuning	Supervised fine-tuning, instruction following, dataset creation	🇮🇳 Hindi: Krish Naik Instruction Tuning 🌍 English: Instruction Tuning Paper Self-Instruct

🛠 P8: Fine-tuning Projects

Project	Description	Tech Stack
Custom Domain Fine-tuning	Fine-tune LLM for specific domain (medical, legal, finance)	Transformers, LoRA, Custom datasets
Instruction Following Model	Create model that follows specific instructions	Alpaca, FLAN-T5, Instruction datasets
Code Generation Fine-tuning	Fine-tune model for code generation tasks	CodeT5, StarCoder, HumanEval dataset

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🚀 22. LLMOps

S.No	Topic	Description	Resources
51	LLMOps Fundamentals	LLM lifecycle management, deployment strategies, monitoring	🇮🇳 Hindi: Krish Naik LLMOps Course CampusX LLMOps 🌍 English: LLMOps Best Practices MLflow for LLMs
52	LLM Serving & Deployment	Model serving, API endpoints, scaling, load balancing	🇮🇳 Hindi: Krish Naik LLM Deployment 🌍 English: vLLM Documentation TensorRT-LLM
53	LLM Monitoring & Evaluation	Performance metrics, A/B testing, quality assurance	🇮🇳 Hindi: Krish Naik LLM Metrics 🌍 English: LangSmith Monitoring Weights & Biases LLMOps

🛠 P9: LLMOps Projects

Project	Description	Tech Stack
LLM API Service	Deploy LLM as scalable API with monitoring	FastAPI, Docker, Kubernetes, Prometheus
LLM A/B Testing Platform	Compare different LLM versions in production	MLflow, Gradio, custom evaluation metrics
Cost-Optimized LLM Pipeline	Implement cost-effective LLM serving with caching	Redis, vLLM, token optimization

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19. Model Context Protocol (MCP)

S.No	Topic	Description	Resources
54	Crew Ai	Minimal agentic framework for build agents	🇮🇳 Hindi: crew ai project 🌍 English: fundamental with project
55	Vercel AI Sdk	Your Go To Ai Sdk Tool fo Built Agents	🇮🇳 Hindi: SQL Agent Project 🌍 English: fundamentals Docs

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�📚 Additional Resources

🎥 Top YouTube Channels

🇮🇳 Indian Creators

Channel	Focus Area	Link
CampusX	Complete ML/AI/GenAI courses	Visit Channel
Krish Naik	Comprehensive ML/AI tutorials	Visit Channel
Codebasics	Data Science & ML	Visit Channel

🌍 Foreign Creators

Channel	Focus Area	Link
Andrej Karpathy	Deep Learning from scratch	Visit Channel
3Blue1Brown	Math intuition for ML/DL	Visit Channel
StatQuest with Josh Starmer	ML concepts simplified	Visit Channel
Jeremy Howard	Practical Deep Learning	Visit Channel
Serrano Academy	AI explanations	Visit Channel
Lex Fridman	AI interviews & discussions	Visit Channel
Machine Learning Street Talk	Deep AI discussions	Visit Channel
FreeCodeCamp	Programming & ML tutorials	Visit Channel
IBM Technology	Quick tech recaps	Visit Channel

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📖 Essential Books

Click to view book collection

📥 Complete AIML Books Collection (Google Drive)

Recommended Books:

• Hands-On Machine Learning by Aurélien Géron
• Deep Learning by Ian Goodfellow
• Pattern Recognition and Machine Learning by Christopher Bishop
• The Hundred-Page Machine Learning Book by Andriy Burkov
• Natural Language Processing with Transformers by Lewis Tunstall
• Designing Data-Intensive Applications by Martin Kleppmann

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📄 Must-Read Research Papers

Click to view paper list

Foundation Papers

• Attention Is All You Need - Original Transformer (2017)
• BERT: Pre-training of Deep Bidirectional Transformers (2018)
• GPT-3: Language Models are Few-Shot Learners (2020)

Fine-tuning & Efficiency

• LoRA: Low-Rank Adaptation of Large Language Models (2021)
• QLoRA: Efficient Finetuning of Quantized LLMs (2023)
• GPTQ: Accurate Post-Training Quantization (2022)

Agents & Reasoning

• ReAct: Synergizing Reasoning and Acting in Language Models (2022)
• Chain-of-Thought Prompting (2022)

RAG & Retrieval

• Retrieval-Augmented Generation for Knowledge-Intensive NLP (2020)

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🛠️ Interactive Learning Platforms

Platform	Description	Link
Kaggle	Datasets, competitions, notebooks	kaggle.com
Hugging Face	Models, datasets, documentation	huggingface.co
Google Colab	Free GPU notebooks	colab.research.google.com
Papers With Code	Research papers + implementation	paperswithcode.com

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🌟 Learning Paths by Goal

🎯 I want to become an ML Engineer

Follow: Sections 0-11, 17-20 Focus: Strong foundation in ML, DL, MLOps, and deployment Timeline: 6-9 months

🤖 I want to work with LLMs and GenAI

Follow: Sections 0-1, 11-19 Focus: Transformers, LLMs, RAG, Agents Timeline: 4-6 months (with ML basics)

🔧 I want to build AI products

Follow: Sections 1-3, 11-20 Focus: FastAPI, LangChain, RAG, deployment Timeline: 3-5 months (with programming basics)

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💡 Pro Tips for Learning

✅ DO:

- Build projects alongside learning
- Join AI communities (Discord, Reddit, Twitter)
- Read research papers gradually
- Contribute to open source
- Document your learning journey

❌ DON'T:

- Jump to advanced topics without fundamentals
- Only watch tutorials without coding
- Try to learn everything at once
- Skip the math (at least understand basics)
- Give up when things get difficult

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🤝 Contributing

We welcome contributions! Here's how you can help:

• ⭐ Star this repository if you find it helpful
• 🐛 Report broken links or outdated content
• 💡 Suggest new resources via pull requests
• 📝 Share your learning experience
• 🔄 Keep resources updated

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📬 Stay Connected

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⭐ Star History

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Last Updated: October 2025

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