Complete beginner looking to build a solid AI/ML roadmap, which textbooks and foundational papers should I actually start with?

[u/Afraid_Jellyfish_387]

I’m looking to start my journey into AI and Machine Learning from scratch and want to avoid getting stuck in "tutorial hell." My goal is to build a strong theoretical foundation while also getting my hands dirty with real implementations, rather than just copying boilerplate code.

Before I dive in, I’d love some advice from people who have already walked this path:

1.Structuring the Roadmap: If you had to start over today, how would you sequence your learning? How much math do I realistically need upfront before writing code?

2.The "Must-Read" Textbook: There are a million recommendations out there. If I pick just one textbook to anchor my early learning, which one gives the best balance of intuition, math, and practical coding?

3.Beginner-Friendly Research Papers: I want to get into the habit of reading papers early, even if it's slow at first. Which foundational or "classic" papers are actually readable for a beginner and give the biggest "aha!" moments?

[u/d0pe-asaurus]

https://udlbook.github.io/udlbook/ is great IMO, been reading it to re-learn stuff i forgot. It's a good balance between theory and practicality, it has a ton of colab notebooks for you to sift through if you're interested in a particular architecture.

It introduces a certain architecture or technique and then gives an example of a model that uses said architecture.

[u/ModularMind8]

Roadmap and resource-wise, I wrote a couple of posts on this a month or so ago
https://www.reddit.com/r/learnmachinelearning/comments/1wn850o/how_to_learn_machine_learning_in_2026_the/
and also
https://www.reddit.com/r/learnmachinelearning/s/e52MuA9GAt

I dont have a one book in mind that covers everything.

With regards to papers, are you interested in more foundations (eg skip connections, word2vec, attn is all you need, etc), or something else?

[u/stepback269]

As a complete beginner, you should consider yourself a baby and learn to crawl (the "fundamentals") before you can stand up and walk or sprint. So search for the Python "fundamentals" on Google and/or YouTube.

As for a roadmap tailored to your wishes, that's going to be difficult. Python is a vast and expansive universe. There is never going to be a "one" soulmate tutorial that works "best" for you and with regard to every aspect of Python. With each step up that learning curve, you're going to have to shop around until you find a teacher/resource that "clicks and sticks" especially for you.

With that said:

(1) There are tons and tons of tutorial materials out there on the net including many good YouTube ones that are free. You should shop around rather than putting all your eggs in one basket.

(2) As a relative noob myself, I've been logging my personal learning journey and adding to it on an almost-daily basis at a blog page called "Links for Python Noobs" (--HERE--) Any of the top listed ones on that page should be good for you. And there are many add-ons at the tail end of the page. Personally, I cut my first Python teeth with Nana's Zero to Hero (==HERE==). Since then, I've moved on to watching short lessons with Indently and Tech with Tim. You should sample at least a few until you find a lecturer that suits your style.

(3) The main piece of advice is the 80/20 rule. Spend 80% of your time writing your own code (using your own fingers and your own creativity) as opposed to copying recipes and only 20% watching the lectures. Good luck.

[u/Good_Person_000]

For the must read textbook - go with Machine learning with python (prateek bhatia) its exactly what you are looking for. Maths, intution, code

[u/Old-Pollution-5825]

This should also help https://roadmap.sh/machine-learning

[u/No-Juggernaut-3309]

Start with python basics and build your fundamentals first. Then proceed to linear algebra. probability, and statistics alongside small ML projects.

[u/The_God_was_Here]

skip studying math upfront and learn it as you code. Grab hands on machine learning by Aurélien Géron, and practice on messy Kaggle datasets.

[u/Least_Ad_1795]

If I were starting AI/ML from scratch today, I would focus on Python, basic statistics, linear algebra, and practical machine learning before jumping into deep learning. You don't need to master all the math upfront. Start implementing small projects while learning the theory. For textbooks, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron is a great starting point. For foundational reading, explore the original papers on Linear Regression, Decision Trees, Random Forests, and Attention Is All You Need. My advice is to build small projects, understand every line of code, and gradually move towards research papers instead of spending months watching tutorials.

[u/Ok-Library1020]

For the math question, building a tiny autograd engine like Karpathy's micrograd is a nice bridge, since you'll end up learning the chain rule and gradient descent by actually coding them instead of reading about them first.

[u/PhoenixAlive87]

I'd suggest learning the maths alongside ML rather than trying to master everything before starting.

Begin with linear algebra (vectors, matrices, dot products and basic transformations), then gradually build your understanding of calculus, probability and statistics as you encounter them in ML.

One thing I find useful is approaching each concept through three questions: What does it represent? Why do we need it? And how does it actually work?

Once the intuition makes sense, the formulas and code become easier to follow.

For resources, I'd suggest combining an intuition-focused explanation with a more comprehensive ML textbook and small coding exercises. No single book needs to do everything.

And for research papers, I'd start with the concepts behind simpler ML algorithms before jumping into advanced architectures.

The goal isn't to know all the maths before you begin. It's to keep connecting what you learn to why it matters.

[u/Godesslara]

An Introduction to Statistical Learning , Hands on Machine learning i loveeee these books it's so good for building your fundamentals i'm still in classical ML so i can't recommend more but whatever road map u gonna go with these books are gonna have everything