how to

What order should you learn machine learning in?

the short answer

A sensible order to learn machine learning is: Python and the math you need (linear algebra, probability, a little calculus), then classic ML, then neural networks and deep learning, then one specialisation like computer vision or nlp — and aipath encodes that dependency order for you so each step builds on the last.

Almost every beginner asks the same question once the initial excitement fades: in what order am I supposed to learn this? It is the right question, because machine learning is full of dependencies — concepts that only make sense once an earlier one has clicked. Learn them out of order and everything feels harder than it should, which is a big part of why people conclude they are 'bad at math' and give up.

There is no single perfect sequence, but there is a sensible one that respects those dependencies. This page lays it out, explains why each step comes where it does, and shows how aipath builds that order in so you are not guessing.

in orderthe sequence is the hard part — aipath encodes it
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A sensible default order

Start with Python, because it is the language nearly all ML work happens in, and 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 do not need to master the math first — you need enough to not 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 exact same excellent course and have completely 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; reinforcement learning feels impossible without basic probability and some deep learning first.

This is the specific thing aipath 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 are never blocked by having skipped something you did not know you needed.

how it works

  1. 01

    python + essential math

    Get comfortable in Python and learn enough linear algebra, probability, and calculus to not be blocked.

  2. 02

    classic machine learning

    Regression, classification, trees, and how to evaluate a model — the intuition deep learning sits on.

  3. 03

    neural networks & deep learning

    How nets learn, then CNNs and sequence models, with code at each step.

  4. 04

    pick one specialisation

    Go deep on computer vision, nlp, generative ai, or rl — once, 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. aipath sequences its tracks this way so each step builds on the last.
Do I need to learn math before machine learning?
You need enough to not 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

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