AI

From Foundations to Production — A Structured Path to Becoming an AI Expert

---

📖 Overview

This repository is my structured, comprehensive journey to mastering Artificial Intelligence. It covers everything from the mathematical foundations to cutting-edge topics like Large Language Models (LLMs), Diffusion Models, and AI Agents.

The goal is not just to learn theory, but to build, deploy, and understand AI systems at a production level. Every topic includes hands-on implementation, and the curriculum is designed to be followed sequentially or explored modularly based on your existing knowledge.[reference:0][reference:1]

🎯 Why This Repository?

• Complete Coverage: 20+ core domains spanning Mathematics, Classical ML, Deep Learning, NLP, Computer Vision, Reinforcement Learning, Generative AI, MLOps, and more.
• Structured Learning: Each directory contains a detailed TODO.md with every sub-topic that must be covered.
• Project-Based: Theory is paired with practical projects to solidify understanding.
• Production-Focused: Includes MLOps, system design, and deployment strategies — not just Jupyter notebooks.
• Open-Source & Evolving: Contributions and suggestions are always welcome.

---

📂 Repository Structure

Directory	Description
AgentDesign/	Intelligent agent architectures, PEAS framework, multi-agent systems
Applications/	Real-world AI applications across healthcare, finance, autonomous vehicles, etc.
CV/	Computer Vision — CNNs, object detection (YOLO, R-CNN), segmentation, Vision Transformers
DL/	Deep Learning fundamentals — backpropagation, optimizers, regularization, attention
Diffusion/	Diffusion models — DDPM, score-based models, Stable Diffusion
EdgeAI/	Deploying AI on edge devices — quantization, pruning, TinyML
Ethics&XAI/	AI ethics, fairness, explainability (SHAP, LIME), privacy, safety
Intro/	Introduction to AI — history, paradigms, intelligent agents, applications
LLM/	Large Language Models — Transformers, GPT, BERT, fine-tuning, RAG, prompt engineering
ML/	Classical Machine Learning — regression, classification, ensembles, clustering
MLOPs/	MLOps — experiment tracking, CI/CD, model monitoring, Kubernetes, feature stores
Math/	Mathematical foundations — linear algebra, calculus, probability, statistics
NLP/	Natural Language Processing — tokenization, embeddings, RNNs, Transformers
Philosophy/	Philosophical foundations — Turing Test, Chinese Room, consciousness, AI alignment
RL/	Reinforcement Learning — MDPs, Q-Learning, DQN, policy gradients, PPO
Robotics/	AI for robotics — kinematics, SLAM, path planning, ROS
Speech/	Speech processing — ASR, TTS, speaker recognition, speech enhancement
SysDesign/	System design for AI — recommendation systems, search, chatbots, scalability
VLM/	Vision-Language Models — CLIP, LLaVA, multimodal AI
Career_Projects/	Capstone projects and portfolio building

---

🗺️ Learning Path

The curriculum follows a natural progression, but you can jump to any area based on your goals:

graph TD
    Start["🚀 Start Here"] --> Math["Mathematics"]
    Math --> Python["Python Programming"]
    Python --> ML["Classical ML"]
    ML --> DL["Deep Learning"]
    DL --> NLP["NLP & LLMs"]
    DL --> CV["Computer Vision"]
    DL --> GenAI["Generative AI"]
    NLP --> Agents["AI Agents"]
    GenAI --> Agents
    NLP --> MLOps["MLOps & Production"]
    CV --> MLOps
    Agents --> MLOps
    MLOps --> E2E["End-to-End Systems"]
    DL --> Safety["AI Ethics & Safety"]
    E2E --> Frontiers["Emerging Frontiers"]
    Safety --> Frontiers

Loading