Daniel

This is hands-down one of the best structured ML learning plans I’ve seen. It’s broken into 4 levels, and each one focuses on math, ML concepts, and programming.

- Level 1: Just get the basics - what ML actually is and isn’t. Stuff like AI for Everyone and Andrew Ng’s Machine Learning Yearning are great starters.


- Level 2: Start diving into the math behind it all, linear algebra, calculus, stats, with resources like 3Blue1Brown and Khan Academy.


- Level 3: Now you’re reading research papers, building full ML pipelines, and integrating stuff into real software systems.


- Level 4: That’s PhD territory. yYou’re doing theoretical work or open-source contributions.

If you like structure and want to go from “curious” to “expert,” this roadmap is gold.
              

Léa

Someone gave a super practical take: if you’re just starting out, don’t try to jump straight into an “AI engineer” role. Those are nearly impossible to land without experience. Instead, get really good at databases (specifically database development, not admin stuff).

SQL is everywhere, and every serious ML engineer relies on it. Starting as a data analyst is a smart move. You’ll learn how data actually works in the real world and build a foundation for the ML side later on.
              

Anastasia

This is the advice that hits hardest in practice: model building is just a small part of real-world ML. Most of your time goes into data ingestion and engineering.
If you want to be market-ready, learn to work with cloud ML platforms: AWS SageMaker, Azure ML, Google Cloud ML. Their docs and notebooks are basically free training material.

Also, while everyone’s obsessed with deep learning right now, most real-life projects still run on classics like XGBoost and Random Forests.
              

Pablo

Daniel Bourke’s roadmap keeps getting recommended for good reason. It literally walks you through everything he studied: from algebra and matrices to Python, ML, and deep learning. It’s a super complete self-taught path, and even people with formal degrees swear by it.
              

Wei

A lot of folks recommend fast.ai for getting started. It’s beginner-friendly, practical, and covers ethics too.
But some newer advice points to Lightning.ai as a more modern framework that might be a better long-term bet than Keras. It’s more flexible and future-proof if you plan to go deep into building stuff.
              

Tomasz

One thing beginners often miss is that deep learning is just one type of machine learning. It’s basically a fancy term for neural networks with multiple layers. The “deep” just means there are several layers between input and output, that’s it. Not every ML model is a neural net.
              

John

If you’re already a Cloud Solutions Architect, you don’t really need to become a full-blown ML engineer. The goal is more about understanding how to design and architect ML systems.
For that, check out the deeplearning.ai MLOps specialization and the Google Professional ML Engineer certification: both are really solid for learning how ML works in production, not just theory.
              

Amara

Someone actually put together a GitHub repo called d0r1h/ML-University. It’s a collection of the best free ML courses online. Super handy because, honestly, the amount of resources out there is overwhelming. This one curates the good stuff in one place.
              

Ahmed

People can’t say this enough: get your math basics down. Learn your stats and focus on linear algebra topics like matrix multiplication and eigenvalues. Most ML models are just advanced versions of classical statistical models applied to data, so the better your math, the faster everything else clicks.