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🚀This repo is all you need for AI
This cheatsheet serves as a practical roadmap and resource guide for anyone looking to get into GenAI or Agentic AI.
I'm actively exploring more resources and refining this roadmap to make it more detailed and genuinely helpful — so ⭐ it if you find it valuable!
checkout - https://aiml-sheet.vercel.app/
📋 Table of Contents
- 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
- LangGraph & Advanced Agents
- Model Context Protocol (MCP)
- FastAPI (Backend for AI)
- Resources
0. Math Foundations
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 0 | Math for ML/DL | Linear Algebra, Probability, Statistics, Calculus | 3Blue1Brown · CampusX |
1. Python Basics
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 1 | Python Fundamentals | Basics, data structures, file handling, exception handling, OOP | FreeCodeCamp |
2. Streamlit
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 2 | Streamlit Basics | UI building, web apps for ML | Chai aur Code |
3. FastAPI
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 3 | FastAPI Fundamentals | REST APIs, async programming, model deployment | FastAPI Docs · FastAPI Course FastAPI Course |
4. Machine Learning — Core Basics
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 4 | ML Fundamentals | Classification, Regression, Pipelines, Feature Engineering | CampusX · Stanford CS229 |
| 5 | ML Evaluation | Accuracy, Precision, Recall, Confusion Matrix, ROC-AUC | StatQuest |
| 6 | Feature Scaling | Normalization, Standardization, MinMax, Robust Scaling | Scikit-learn Docs |
| 7 | Data Labeling | Manual annotation, Label Studio, Roboflow | Label Studio · Roboflow |
🛠 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 |
5. Machine Learning — Deep Dive
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 8 | Unsupervised ML | Clustering (K-Means, DBSCAN, Hierarchical), Dimensionality Reduction (PCA, t-SNE, UMAP) | StatQuest |
| 9 | Ensemble Methods | Bagging, Boosting (XGBoost, LightGBM), Stacking | Krish Naik |
| 10 | Hyperparameter Tuning | GridSearchCV, RandomSearch, Optuna, Bayesian Optimization | Optuna Docs |
| 11 | Core ML Concepts | Bias-variance tradeoff, Underfitting/Overfitting, Regularization (L1/L2) | Andrew Ng ML |
6. ML for NLP
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 12 | Traditional NLP | Text preprocessing, One-Hot Encoding, Bag of Words, TF-IDF, Word2Vec | Krish Naik |
🛠 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 |
7. DL Basics
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 13 | Deep Learning Fundamentals | Neural Networks, Loss Functions, Optimizers, Activation Functions | 3Blue1Brown · MIT 6.S191 Campus X |
8. Core DL
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 14 | Neural Networks & ANN | Feedforward networks, backpropagation, gradient descent | MIT 6.S191 · 3Blue1Brown Campus X |
| 15 | CNN | Convolutional Neural Networks for computer vision | Campus X MIT 6.S191 · CS231n |
| 16 | RNN & LSTM | Sequential data modeling, time series | Campus X MIT 6.S191 · Colah's Blog |
9. DL Frameworks
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 17 | PyTorch/TensorFlow | Tensors, model building, training loops | PyTorch Docs · TensorFlow Docs · PyTorch Tutorial |
🛠 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 |
10. MLOps
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 18 | MLOps Fundamentals | Model versioning, experiment tracking, CI/CD for ML, monitoring | MLOps Playlist · MLOps Best Practices |
| 19 | Model Deployment | Docker, cloud deployment, model serving, A/B testing | MLOps Playlist |
| 20 | Experiment Tracking | MLflow, Weights & Biases, model registry | MLflow Docs · Weights & Biases |
11. Transformers
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 21 | Transformer Architecture | Self-attention, Multi-head attention, Positional Encoding, Encoder-Decoder | 3Blue1Brown · Campus X |
| 22 | Tokenization | BPE, SentencePiece, GPT-2 tokenizer, Hugging Face tokenizers | Campus X Andrej Karpathy · Original Paper |
🛠 P4: Chatgpt from scratch
| Project | Description | Tech Stack |
|---|---|---|
| Build an LLM from scratch | Build an LLM using transformers arch | Goat |
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 | Fireship · Two Minute Papers |
| 24 | LLM Evaluation | BLEU, ROUGE, Perplexity, Human Evaluation, Benchmarks | Hugging Face Evaluation |
| 25 | Ethics & AI Safety | Hallucination, bias, responsible deployment, alignment | AI Safety Course |
13. Introduction to LangChain
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 31 | LangChain Fundamentals | Components, Chains, Agents, Memory | LangChain Docs · LangChain Tutorial |
| 32 | LLM Integration | OpenAI, Ollama, Hugging Face, Groq integration | Ollama Setup · Groq API |
| 33 | Prompt Engineering | Zero-shot, few-shot, chain-of-thought, prompt optimization | OpenAI Cookbook · Prompt Engineering Guide |
🛠 P5: 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 |
14. RAG (Retrieval Augmented Generation)
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 34 | RAG Fundamentals | Retrieval pipeline, embedding models, vector similarity | RAG Tutorial · LangChain RAG |
| 35 | Advanced RAG | Multi-query retrieval, re-ranking, hybrid search | Pinecone RAG Guide |
🛠 P6: 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 |
15. Vector Databases
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 36 | Vector DB Fundamentals | FAISS, ChromaDB, Pinecone, Weaviate, similarity search | Pinecone Docs · ChromaDB |
| 37 | Embedding Models | sentence-transformers, OpenAI embeddings, custom embeddings | Sentence Transformers |
16. Agentic AI
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 38 | AI Agent Fundamentals | Agent architecture, planning, tool use, memory systems | Lilian Weng's Blog |
| 39 | Tool-Using Agents | Function calling, external APIs, code execution | OpenAI Function Calling |
| 40 | Multi-Agent Systems | Agent collaboration, communication protocols | AutoGen · CrewAI |
| 41 | ReAct & Planning | Reasoning + Acting, chain-of-thought for agents | ReAct Paper |
🛠 P7: 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 |
17. Large Language Models (LLMs) - Advanced
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 26 | PEFT (Parameter Efficient Fine-Tuning) | LoRA, QLoRA, AdaLoRA, Prefix Tuning, P-Tuning | Hugging Face PEFT · LoRA Paper |
| 27 | LoRA & QLoRA | Low-Rank Adaptation, Quantized LoRA for efficient fine-tuning | QLoRA Paper · Practical LoRA |
| 28 | Quantization Techniques | INT8, INT4, GPTQ, AWQ, GGML/GGUF formats | BitsAndBytes · GPTQ |
| 29 | Model Compression | Pruning, Distillation, Quantization-Aware Training | Neural Compression |
| 30 | Advanced Fine-tuning | Full fine-tuning vs PEFT, Instruction tuning, RLHF basics | Hugging Face Fine-tuning |
18. LangGraph & Advanced Agents
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 42 | LangGraph Fundamentals | State machines, graph-based workflows for agents | Campus X · LangGraph Tutorial |
| 43 | Complex Agent Workflows | Multi-step reasoning, conditional flows, human-in-the-loop | Campus X |
| 44 | Agent Orchestration | Managing multiple agents, workflow optimization | Campus X |
🛠 P8: 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 |
19. Model Context Protocol (MCP)
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 45 | MCP Fundamentals | Protocol for connecting AI assistants to external data sources and tools | Campus X · MCP GitHub |
| 46 | MCP Implementation | Building MCP servers, client integration, tool development | Krishnaik |
20. FastAPI (Backend for AI)
| S.No | Topic | Description | Resources |
|---|---|---|---|
| 47 | AI Model Deployment | Serving ML/DL models, batch processing, monitoring | MLOps Best Practices · MLOps Playlist |
📚 Resources
🎥 Popular YouTube Channels
- 3Blue1Brown - AIML indepth intuition
- CampusX - Indian ML education
- Krish Naik - Comprehensive ML/AI tutorials
- IBM Technology - Fast recap while interviews
- Codebasics - extras
- FreeCodeCamp - extras
- Andrej Karpathy
- Jeremy Howard
- 3Blue1Brown
- Serrano Academy
- Lex Fridman
- Hamel Husain
- Machine Learning Street Talk
- Jason Liu
- StatQuest with Josh Starmer
- Dave Ebbelaar
📖 Essential Books
- All Top AIML Books - collection in one place
📄 Key Research Papers
- Attention Is All You Need - Original Transformer paper
- BERT: Pre-training of Deep Bidirectional Transformers
- GPT-3: Language Models are Few-Shot Learners
- LoRA: Low-Rank Adaptation of Large Language Models
- QLoRA: Efficient Finetuning of Quantized LLMs
- ReAct: Synergizing Reasoning and Acting in Language Models
🤝 Contributing
Feel free to contribute to this roadmap by:
- Adding new resources and tutorials
- Suggesting improvements to the learning path
- Sharing your project experiences
- Reporting broken links or outdated content
⭐ Star this repository if you find it helpful!
This roadmap is continuously updated with the latest developments in Generative AI and Machine Learning.
📌 Additional
- Follow me on X: https://x.com/lochan_twt
- Buy me a coffee: https://buymeacoffee.com/srilochan7p
