Skillget

ai-learning

mcp
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Purpose
This repository is a comprehensive educational roadmap and collection of resources for learning Artificial Intelligence, Machine Learning, and Generative AI. It primarily links to external tutorials, videos, and guides rather than functioning as an executable software package or a traditional MCP server.

Security Assessment
Overall Risk: Low. The repository does not request dangerous permissions, contain hardcoded secrets, or appear to execute shell commands. There is a flagged environment variable access (`process.env`) inside a sample SQL agent configuration file (`agentic_frameworks/sql_agent/drizzle.config.ts`). This is a standard, safe practice for managing local database credentials in tutorials and poses no security threat to the user.

Quality Assessment
The project is actively maintained, with its most recent push occurring just today. It has garnered 22 GitHub stars, indicating a basic level of community trust and usefulness for learners. However, the repository completely lacks a formal open-source license. This means that technically, default copyright laws apply, and the materials are not legally cleared for free copying, distribution, or modification by other developers.

Verdict
Safe to use for personal educational reference, but use caution if you intend to fork, copy, or redistribute the content due to the absence of a formal software license.
SUMMARY

AI Learning: A comprehensive repository for Artificial Intelligence and Machine Learning resources, primarily using Jupyter Notebooks and Python. Explore tutorials, projects, and guides covering foundational to advanced concepts in AI, ML, DL and Gen/Agentic Ai.

README.md

🚀 Complete AI Learning Roadmap

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

⭐ Star this repo
🔄 Actively Updated
📚 Free Resources

📋 Table of Contents

Click to expand/collapse

0. Math Foundations

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0 Math for ML/DL Linear Algebra, Probability, Statistics, Calculus

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1. Python Basics

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1 Python Fundamentals Basics, data structures, file handling, exception handling, OOP

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2. Streamlit

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2 Streamlit Basics UI building, web apps for ML

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3. FastAPI

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3 FastAPI Fundamentals REST APIs, async programming, model deployment

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4. Machine Learning — Core Basics

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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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🛠 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

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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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6. ML for NLP

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12 Traditional NLP Text preprocessing, One-Hot Encoding, Bag of Words, TF-IDF, Word2Vec

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🛠 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. Deep Learning Basics

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13 Deep Learning Fundamentals Neural Networks, Loss Functions, Optimizers, Activation Functions

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8. Core Deep Learning

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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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9. DL Frameworks

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17 PyTorch Tensors, model building, training loops

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🛠 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

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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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11. Transformers

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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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12. Introduction to Gen AI

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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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🦾 13. Large Language Models (LLMs) - Advanced

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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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14. Introduction to LangChain

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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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🛠 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

15. RAG (Retrieval Augmented Generation)

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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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🛠 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

16. Vector Databases

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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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17. Agentic AI

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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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🛠 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

🧪 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.

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HE-1 Harness Engineering Fundamentals AI system harnesses, eval suites, test environments, feedback loops, reliability workflows

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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

18. LangGraph & Advanced Agents

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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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🛠 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

19. Model Context Protocol (MCP)

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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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21. Fine-tuning

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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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🛠 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

🚀 22. LLMOps

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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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🛠 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

19. Model Context Protocol (MCP)

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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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�📚 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

📖 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

📄 Must-Read Research Papers

Click to view paper list

Foundation Papers

Fine-tuning & Efficiency

Agents & Reasoning

RAG & Retrieval

🛠️ 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

🌟 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)

💡 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

🤝 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

📬 Stay Connected

GitHub
Twitter
LinkedIn

⭐ Star History

If this roadmap helped you, please consider giving it a ⭐

Last Updated: October 2025

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