Your comprehensive guide to mastering GenAI and Agentic AI โ from fundamentals to advanced deployment
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- ๐ข Math Foundations
- ๐ Python Basics
- ๐จ Streamlit
- โก FastAPI
- ๐ค Machine Learning โ Core Basics
- ๐ Machine Learning โ Deep Dive
- ๐ ML for NLP
- ๐ง Deep Learning Basics
- ๐ฏ Core Deep Learning
- ๐ ๏ธ DL Frameworks
- ๐ง MLOps
- ๐ Transformers
- โจ Introduction to Gen AI
- ๐ฆพ Large Language Models (LLMs) - Advanced
- ๐ Introduction to LangChain
- ๐ RAG (Retrieval Augmented Generation)
- ๐พ Vector Databases
- ๐ค Agentic AI
- ๐ LangGraph & Advanced Agents
- ๐ Model Context Protocol (MCP)
- ๐ Fine-tuning
- ๐ LLMOps
- ๐ Additional Agentic Framework
- ๐ Additional Resources
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 0 | Math for ML/DL | Linear Algebra, Probability, Statistics, Calculus |
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| S.No | Topic | Description | Resources |
|---|---|---|---|
| 1 | Python Fundamentals | Basics, data structures, file handling, exception handling, OOP |
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| S.No | Topic | Description | Resources |
|---|---|---|---|
| 2 | Streamlit Basics | UI building, web apps for ML |
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| S.No | Topic | Description | Resources |
|---|---|---|---|
| 3 | FastAPI Fundamentals | REST APIs, async programming, model deployment |
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| S.No | Topic | Description | Resources |
|---|---|---|---|
| 4 | ML Fundamentals | Classification, Regression, Pipelines, Feature Engineering |
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| 5 | ML Evaluation | Accuracy, Precision, Recall, Confusion Matrix, ROC-AUC |
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| 6 | Feature Scaling | Normalization, Standardization, MinMax, Robust Scaling |
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| 7 | Data Labeling | Manual annotation, Label Studio, Roboflow |
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| 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 |
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 8 | Unsupervised ML | Clustering (K-Means, DBSCAN, Hierarchical), Dimensionality Reduction (PCA, t-SNE, UMAP) |
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| 9 | Ensemble Methods | Bagging, Boosting (XGBoost, LightGBM), Stacking |
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| 10 | Hyperparameter Tuning | GridSearchCV, RandomSearch, Optuna, Bayesian Optimization |
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| 11 | Core ML Concepts | Bias-variance tradeoff, Underfitting/Overfitting, Regularization (L1/L2) |
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| S.No | Topic | Description | Resources |
|---|---|---|---|
| 12 | Traditional NLP | Text preprocessing, One-Hot Encoding, Bag of Words, TF-IDF, Word2Vec |
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| 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 |
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 13 | Deep Learning Fundamentals | Neural Networks, Loss Functions, Optimizers, Activation Functions |
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| S.No | Topic | Description | Resources |
|---|---|---|---|
| 14 | Neural Networks & ANN | Feedforward networks, backpropagation, gradient descent |
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| 15 | CNN | Convolutional Neural Networks for computer vision |
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| 16 | RNN & LSTM | Sequential data modeling, time series |
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| S.No | Topic | Description | Resources |
|---|---|---|---|
| 17 | PyTorch | Tensors, model building, training loops |
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| 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 |
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 18 | MLOps Fundamentals | Model versioning, experiment tracking, CI/CD for ML, monitoring |
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| 19 | Model Deployment | Docker, cloud deployment, model serving, A/B testing |
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| 20 | Experiment Tracking | MLflow, Weights & Biases, model registry |
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| S.No | Topic | Description | Resources |
|---|---|---|---|
| 21 | Transformer Architecture | Self-attention, Multi-head attention, Positional Encoding, Encoder-Decoder |
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| 22 | Tokenization | BPE, SentencePiece, GPT-2 tokenizer, Hugging Face tokenizers |
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| S.No | Topic | Description | Resources |
|---|---|---|---|
| 23 | GenAI Fundamentals | AI vs ML vs DL vs GenAI, How GPT/LLMs are trained, LLM evolution |
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| 24 | LLM Evaluation | BLEU, ROUGE, Perplexity, Human Evaluation, Benchmarks |
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| 25 | Ethics & AI Safety | Hallucination, bias, responsible deployment, alignment |
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| S.No | Topic | Description | Resources |
|---|---|---|---|
| 26 | PEFT (Parameter Efficient Fine-Tuning) | LoRA, QLoRA, AdaLoRA, Prefix Tuning, P-Tuning |
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| 27 | LoRA & QLoRA | Low-Rank Adaptation, Quantized LoRA for efficient fine-tuning |
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| 28 | Quantization Techniques | INT8, INT4, GPTQ, AWQ, GGML/GGUF formats |
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| 29 | Model Compression | Pruning, Distillation, Quantization-Aware Training |
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| 30 | Advanced Fine-tuning | Full fine-tuning vs PEFT, Instruction tuning, RLHF basics |
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| S.No | Topic | Description | Resources |
|---|---|---|---|
| 31 | LangChain Fundamentals | Components, Chains, Agents, Memory |
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| 32 | LLM Integration | OpenAI, Ollama, Hugging Face, Groq integration |
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| 33 | Prompt Engineering | Zero-shot, few-shot, chain-of-thought, prompt optimization |
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| 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 |
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 34 | RAG Fundamentals | Retrieval pipeline, embedding models, vector similarity |
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| 35 | Advanced RAG | Multi-query retrieval, re-ranking, hybrid search |
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| Project | Description | Tech Stack |
|---|---|---|
| PDF Q&A with RAG | Upload PDF โ extract โ chunk โ embed โ 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 |
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 36 | Vector DB Fundamentals | FAISS, ChromaDB, Pinecone, Weaviate, similarity search |
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| 37 | Embedding Models | sentence-transformers, OpenAI embeddings, custom embeddings |
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| S.No | Topic | Description | Resources |
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| 38 | AI Agent Fundamentals | Agent architecture, planning, tool use, memory systems |
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| 39 | Tool-Using Agents | Function calling, external APIs, code execution |
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| 40 | Multi-Agent Systems | Agent collaboration, communication protocols |
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| 41 | ReAct & Planning | Reasoning + Acting, chain-of-thought for agents |
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| 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 |
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 42 | LangGraph Fundamentals | State machines, graph-based workflows for agents |
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| 43 | Complex Agent Workflows | Multi-step reasoning, conditional flows, human-in-the-loop |
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| 44 | Agent Orchestration | Managing multiple agents, workflow optimization |
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| 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 |
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 45 | MCP Fundamentals | Protocol for connecting AI assistants to external data sources and tools |
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| 46 | MCP Implementation | Building MCP servers, client integration, tool development |
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| S.No | Topic | Description | Resources |
|---|---|---|---|
| 48 | Fine-tuning Fundamentals | Full fine-tuning, transfer learning, domain adaptation |
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| 49 | Parameter Efficient Fine-tuning | LoRA, QLoRA, AdaLoRA, Prefix Tuning, P-Tuning |
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| 50 | Instruction Tuning | Supervised fine-tuning, instruction following, dataset creation |
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| 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 |
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 51 | LLMOps Fundamentals | LLM lifecycle management, deployment strategies, monitoring |
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| 52 | LLM Serving & Deployment | Model serving, API endpoints, scaling, load balancing |
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| 53 | LLM Monitoring & Evaluation | Performance metrics, A/B testing, quality assurance |
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| 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 |
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 54 | Crew Ai | Minimal agentic framework for build agents |
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| 55 | Vercel AI Sdk | Your Go To Ai Sdk Tool fo Built Agents |
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| 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 |
| 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 |
Click to view book collection
Recommended Books:
- Hands-On Machine Learning by Aurรฉlien Gรฉron
- 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
Click to view paper list
- 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)
| 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 |
๐ฏ 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
๐ค 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)
๐ง I want to build AI products
Follow: Sections 1-3, 11-20 Focus: FastAPI, LangChain, RAG, deployment Timeline: 3-5 months (with programming basics)
โ
DO:
- Build projects alongside learning
- Join AI communities (Discord, Reddit, Twitter)
- Read research papers gradually
- Contribute to open source
- Document your learning journey
โ 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 difficultWe welcome contributions! Here's how you can help:
- โญ Star this repository if you find it helpful
- ๐ Report broken links or outdated content
- ๐ก Suggest new resources via pull requests
- ๐ Share your learning experience
- ๐ Keep resources updated
If this roadmap helped you, please consider giving it a โญ
Last Updated: October 2025