If you want a complete ML path (basics → advanced), these are honestly some of the best resources 👇

📘 Start with fundamentals

• Hands-On Machine Learning (Aurélien Géron) → best book for concepts + practical intuition
• Andrew Ng’s Machine Learning Specialization → most recommended beginner course on Reddit (clear + structured) ()

🎓 Build strong theory

• Stanford CS229 (Andrew Ng lectures) → deeper math + real understanding
• Covers regression, SVMs, kernels, etc.

⚡ Go practical (important)

• fast.ai → learn by building real models (projects from day 1)
• Kaggle → apply what you learn

🧠 Go advanced

• Deep Learning Specialization (Andrew Ng)
• Transformers / modern DL after basics

💡 Reddit consensus:

Simple roadmap:
Basics → Theory → Practice → Advanced DL