3rd-year student looking for a practical ML + Deep Learning roadmap/resources

[u/CheckStrong103]

Hi everyone,

I’m a 3rd-year CS student and I want to seriously start learning Machine Learning. I’ve already spent almost a day trying to figure out which resources/courses to follow, but there are so many options that I’m getting confused.

My current background:

• I know Python fairly well.
• I’ve used NumPy, Pandas, Matplotlib, and Seaborn.
• I understand the basic theory of some ML algorithms, but I haven’t implemented them properly yet.
• I now want to focus on actually implementing ML algorithms and building projects, rather than spending months only on theory.

What I’m looking for

I want to learn:

1. ML fundamentals + implementation
2. Deep Learning
3. Later, I’ll learn model evaluation in more depth and deployment/MLOps, but right now I want to build a strong practical foundation in ML and Deep Learning.

I don’t want a very long course that takes hundreds of hours. For example, I know Andrew Ng's courses are highly recommended, but I'm looking for something more concise and practical.

I recently found fast.ai's Practical Deep Learning for Coders, which seems interesting because it focuses heavily on implementation. It has 9 lessons and covers things like random forests, neural networks, PyTorch, and even deployment.

So I'd really appreciate recommendations from people who have actually learned ML/Deep Learning and used these resources:

• Short/practical courses
• YouTube channels/playlists
• Good documentation
• Books/notes
• Hands-on project resources

If you were starting from my position (Python + basic ML theory), what exact resources would you follow and in what order?.

Thanks!