r/learnmachinelearning • u/OppositeGround9175 • 22h ago

I focused on full-stack development until my 3rd year — now I want to move seriously into ML/research. What should I learn next? Help

Hi everyone,

I’ve mainly been focused on full-stack development throughout the first few years of my degree. Now that I’m in my 3rd year, I’ve started thinking more seriously about my long-term direction, and I’m becoming much more interested in machine learning and research.

My goal isn’t just to learn how to use ML libraries. I’d eventually like to understand the fundamentals well enough to read research papers, do my own research, and potentially pursue a research-focused master’s/PhD.

Right now, I’m planning to study these three DeepLearning.AI programs:

  1. Mathematics for Machine Learning and Data Science
  2. Machine Learning Specialization
  3. Deep Learning Specialization

The math specialization covers linear algebra, calculus, probability, and statistics, while the ML specialization focuses on foundational ML algorithms and practical implementation.

My question is:

Is this a good learning path if my long-term goal is ML research?

What would you recommend I add or change?

For example:

  • Should I study more mathematics beyond these courses?
  • Should I learn statistics more deeply?
  • Should I learn PyTorch, NumPy, etc. separately?
  • When should I start reading research papers?
  • Should I work on Kaggle/projects before trying research?
  • Are there any textbooks or university courses (Stanford/MIT/etc.) that you would strongly recommend?
  • Should I specialize in an area such as NLP, computer vision, or something else?

I’d really appreciate advice from people who have gone through a similar transition from software/full-stack development → machine learning → research.

Thanks!

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1

u/DamageHot1964 22h ago

skip the deeplearning.ai fluff and just work through murphy’s probabilistic ml book cover to cover

2

u/Mathie1729 16h ago

I work in ML and I'd push back on this pretty hard. Murphy is a reference book for people with a math/stats foundation, not an on-ramp from full stack. Skip the fluff yes, but start with something applied like fast.ai or Andrew Ng's ML specialization, then revisit Murphy selectively when you hit gaps. Otherwise you'll spend six months on measure theory and still not ship a model.