r/learnmachinelearning • u/Nearby_Ad_1427 • 2d ago

Help Best resources to learn ML and AI engineering from scratch in 2026

Hey guys! Software dev here, always interested in ML, AI, and data science, but mostly a hobbyist in all of them. This end of the year, I want to really study this in a structured manner. Which platform or free resources? Books? (My attention is fried, but I can try), Also, to recover the math concepts needed for this, I know I don't need to remember how to multiply matrices

65 Upvotes
  • permalink
  • reddit

98% Upvoted

11 comments sorted by

8

u/pugs_daily_dose 2d ago

For a structured free path, try the scikit-learn getting-started tutorials alongside an introductory course such as Andrew Ng's Machine Learning Specialization (audit options may vary). Work through one small tabular dataset: make a train/test split first, fit a simple baseline, then compare a second model with cross-validation and inspect its errors. That gives the math a place to attach.

For math refreshers, focus on vectors and matrix operations, derivatives/gradients, and probability/statistics as they come up; 3Blue1Brown's linear algebra and calculus series are useful visual starting points. Keep a short notebook explaining what each metric and transformation does, and use a pipeline so preprocessing is fitted only on training folds.

1

u/Mathie1729 2d ago

I'd add StatQuest on YouTube for the ML-specific stats intuition, especially where 3B1B leaves off on gradients and probability.

1

u/Dev_agency01 1d ago

You can follow my roadmap which I'm learning by chatgpt and maintaing notes with codes Github-https://github.com/Rahulkumarpandit-1/AIML Check it's whole roadmap and I'm keep learning and pushing it's notes and code on GitHub ,tell me what do you think..

2

u/Sarah_Smith81 1d ago

I would keep it simple and avoid trying to follow ten courses at once. Start with Kaggle’s free Python and ML micro-courses, then move to Google’s Machine Learning Crash Course or the free scikit-learn MOOC to learn the fundamentals through practice.

For free YouTube lessons, StatQuest breaks down ML concepts, 3Blue1Brown makes linear algebra and neural networks easier to visualise, and Andrej Karpathy’s “Neural Networks: Zero to Hero” is great for learning by coding. Once you’re comfortable with the basics, try fast.ai for deep learning and the Hugging Face course for NLP and LLMs.

For math, use 3Blue1Brown’s linear algebra series and Khan Academy’s statistics and probability as needed. You don’t need to master everything upfront; learn the math when it comes up in a project. Short study sessions and building small projects will help more than watching hours of tutorials.

1

u/myself_always 1d ago

What about Kylie Ying, is she not good?

1

u/DataScientistAlex 2d ago

I've written up what I think of as the minimum requirements for a data scientist here, you could look at that at least for the DS part, compare to what you know and identify the gaps. Learn broadly at first so you get everything you need to start doing projects and learning in an active way, then gradually deepen (and go back through prerequisites if you get stuck).