🧠 AI / ML Roadmap by COLAB

This roadmap has been created with the help of surveys from students, instructors, and industry experts (including professionals from IBM) who have practical, hands-on experience in the AI/ML field.

⚠️ Before diving into AI/ML, it is essential to have a basic understanding of a programming language, preferably Python.

📺 Complete Roadmap Overview (Video)

🎥 A summarized video format of the full roadmap:
👉 AI/ML Roadmap Overview

1️⃣ Linear Algebra

The mathematical foundation for many ML algorithms — matrices, transformations, and optimization.

📚 Course Link:
🔗 Linear Algebra for Machine Learning

2️⃣ Probability and Statistics (with a CS Perspective)

Helps you understand data distributions, uncertainties, and evaluation techniques in ML.

📚 Course Playlist:
🔗 Probability and Stats Playlist

3️⃣ Numerical Computing with Python

Covers core numerical methods for optimization, solving equations, and simulations.

📚 Course Playlist:
🔗 Numerical Computing Playlist

4️⃣ Introduction to Machine Learning

Dive into ML fundamentals like regression, classification, clustering, and model evaluation.

📚 Course Playlist:
🔗 Intro to ML Playlist

5️⃣ Deep Learning by Andrew Ng

Explore neural networks, CNNs, RNNs, backpropagation, and advanced DL concepts.

📚 Course Playlist:
🔗 Deep Learning Specialization

6️⃣ Natural Language Processing (NLP)

Learn how machines understand and generate human language.

🧰 Suggested Resources:

Stanford NLP Courses
Hugging Face Tutorials
YouTube channels and NLP blogs

7️⃣ Large Language Models (LLMs)

Understand how models like GPT are trained and deployed, including transformers and embeddings.

🧠 Suggested Exploration:

Open-source models: LLaMA, Mistral, Falcon
Hugging Face Transformers Library

8️⃣ Reinforcement Learning

Focuses on agent-environment interaction, reward systems, and applications in robotics and games.

📚 Course Playlist:
🔗 Reinforcement Learning Playlist

📝 Final Note

This roadmap is designed to help you build progressively from foundational concepts to advanced topics in AI/ML.
✅ Follow it step-by-step for a clear and smooth learning experience.

ayyzenn/AI&ML-roadmap-v1.md