A sensible default order
Start with Python, the language nearly all ML work happens in, plus just enough math to read what comes next: linear algebra (vectors and matrices), probability and statistics, and a little calculus for how models learn. You don't need to master the math first. You need enough not to be blocked, and you can deepen it as topics demand.
From there, learn classic machine learning (regression, classification, trees, how to evaluate a model) before deep learning, so neural networks land on top of solid intuition rather than replacing it. Then move into neural networks and deep learning, and only then pick a specialisation: computer vision, nlp and transformers, generative ai, or reinforcement learning. Picking a specialisation too early is the most common ordering mistake, because each one assumes the foundations underneath it.
Why the order matters more than the resource
Two people can use the same excellent course and have wildly different experiences depending on what they did before it. The resource is rarely the problem. The missing prerequisite is. Transformers feel like magic without a grasp of embeddings and attention's building blocks, and reinforcement learning feels impossible without basic probability and some deep learning first.
That's what path·ai is built around. Its curated tracks are sequenced so each module assumes only what earlier modules covered, and generated paths are assembled to respect the same dependencies. You still do the learning, but you're never blocked by having skipped something you didn't know you needed.
how it works
- 01
python + essential math
Get comfortable in Python and learn enough linear algebra, probability, and calculus to not be blocked.
- 02
classic machine learning
Regression, classification, trees and model evaluation: the intuition deep learning sits on.
- 03
neural networks & deep learning
How nets learn, then CNNs and sequence models, with code at each step.
- 04
pick one specialisation
Go deep on computer vision, nlp, generative ai or rl. One of them, not all at once.
frequently asked
What order should I learn machine learning in?
Python and essential math first, then classic ML, then neural networks and deep learning, then a single specialisation such as computer vision or nlp. path·ai sequences its tracks this way so each step builds on the last.
Do I need to learn math before machine learning?
You need enough not to be blocked: basic linear algebra, probability and a little calculus, not a full degree first. You can deepen the math as specific topics require it.
What should I learn after Python for machine learning?
The essential math and then classic ML (regression, classification, evaluation) before deep learning, so neural networks land on solid intuition.
Should I learn machine learning or deep learning first?
Classic machine learning first. Deep learning makes far more sense once you understand models, training, and evaluation in the simpler setting.
Last updated June 7, 2026