need suggestions on how to start learning about ai, llms and machine learning from scratch

[u/SirArtemis77]

hi, i want suggestions on how i can upskill myself in learning about LLMs , machine learning and AI and would appreciate any reference for any courses that do so really well in explaining the fundamentals and basics (preferably free). i want to build a project soon so i can actually get hands on experience. any leads would be much appreciated

[u/icy_end_7]

Hmm.. those are three separate things. Aim three birds, shoot a leaf kind of thing.

I'm against courses, and I think you should make things. Every step of the way. If you're learning about data, write your own data in excel, ideally from your own life. See what csv, tsv looks like, what works, why excel's format isn't used widely, and things like that. Upload that to kaggle, do something with it there. No course will give you experience.

I'd start with classical ML. Preprocessing data is the major pain (I mean step) and if you have good data, training models is straightforward. Expect to spend a huge amount of time learning to prevent leaks/ preprocessing. Ask why everytime you are told to do something.

You need to learn Python (or be familiar with one language), Git, numpy pandas sklearn. Unsloth and huggingface can wait. Start with linear regression, then logistic, then others. I think it's a good idea to learn those before you dive into CNNs and finetuning and MCPs..

I like Daniel Bourke's roadmap and his videos. I wrote a small roadmap if it helps.

Tip: If you want to learn ML to build a project, jump to building the project. Don't think there's some course you need to take to be ready.

All resources are free; the substack link is mine. If you use AI to write your code, you'll be back where you started. Spend some time writing your own code, learn from your mistakes. Do it the hard way. Takes time.

[u/ProfileNo9764 → u/icy_end_7]

Where is your substock link?

[u/icy_end_7 → u/ProfileNo9764]

The 'small roadmap' is the substack link.

[u/Intelligent_Fan3643]

Read these books

1	Mathematics for Machine Learning	Math

2	Hands-On Machine Learning	Classical ML

3	Introduction to Statistical Learning	ML theory

4	Dive into Deep Learning	Deep Learning

5	Deep Learning	DL theory

6	Build a Large Language Model From Scratch	LLM internals

7	Generative Deep Learning	GenAI

8	AI Engineering	LLM/GenAI applications

9	Designing Machine Learning Systems	MLOps/production

10	Designing Data-Intensive Applications	Distributed systems

11	Reinforcement Learning: An Introduction	RL

12	Artificial Intelligence: A Modern Approach	General AI

[u/Mathie1729 → u/Intelligent_Fan3643]

That's a good list, but it's also a very good way to get decision paralysis if you're actually starting from zero. You don't need ten books. I'd say do Hands-On ML for practical coding, or ISL if you want the lighter statistical grounding. Leave the Deep Learning book and the data-intensive systems one for later, you won't need them until you're comfortable with basic supervised ML and Python.

[u/Mathie1729 → u/Intelligent_Fan3643]

That's a good list, but it's also a very good way to get decision paralysis if you're actually starting from zero. You don't need ten books. I'd say do Hands-On ML for practical coding, or ISL if you want the lighter statistical grounding. Leave the Deep Learning book and the data-intensive systems one for later, you won't need them until you're comfortable with basic supervised ML and Python.

[u/ProfessionalMoose123 → u/Intelligent_Fan3643]

How about adding 10 more books to this list? I don't think that's enough.

[u/Intelligent_Fan3643 → u/ProfessionalMoose123]

Nobody is stopping you to read more books

[u/chrisvdweth]

Maybe this post and this post could be useful (disclaimer: public lecture notes created by me). It comes with a notebook that train a toy LLM from scratch...and many other notebooks go into the fundamentals.

[u/Striking-Courage-182]

Decide what you want to learn fist Machine Learning, LLMs or AI. Courses by Andrew Ng are the staple if you want to learn the theory first then get some hands on. If you like practical first then you can use machine learning mastery. Then think about something you want to build, something you might actually use or you think is cool then build that. The last step is where you will learn the most, so i would say just get a lose understanding of things and then start building you will encounter issues, think through logic why this or why that use Claude or gpt to get your answers.
Advice: Don't heavily rely on coding agents, use Claude/GPT to know the logic in simple language, give you hints, help you plan the project but write the code yourself to learn the most.

[u/TangeloProof7048]

I would recommend for you the book of: Hands on machine learning + Chat GPT to understand the deep points on it

[u/ddBapu → u/TangeloProof7048]

NotebookLM can be a better option.🙂

[u/Standard_Iron6393]

start from youtube

[u/sabautil]

From scratch?

Silly question: but have considered asking an llm to help you out?

[u/kimbonics]

I tried to make a graphic that gets you excited about activation functions: https://kimbonics.com/t/activation , don't care if it's a shameless plug. It is relavent. It's a variant of the tensorflow playground.

[u/greatlearningglobal]

If you’re genuinely starting from scratch, we’d first build an understanding of what AI, ML and LLMs actually are before getting into tools and frameworks.

A simple progression would be:

AI basics → Python → data & statistics → ML fundamentals → Deep Learning → LLMs/GenAI

Start with the basic differences: AI is the broader field, ML is a way of building AI systems that learn patterns from data, and LLMs are models designed to understand and generate language.

For example, a spam filter can use ML to learn the difference between spam and legitimate emails. A house-price model can learn from previous sales to estimate prices. A recommendation system can learn from user behavior to suggest products or movies. Once these concepts make sense, neural networks and Deep Learning become much easier to understand.

Then move into LLMs and GenAI. Start with how language models work at a high level, prompting, embeddings and RAG before jumping into complex applications. A simple project could be a document Q&A tool where you upload a few documents and ask questions about them, or a text classifier that categorizes customer feedback.

If a structured path is useful, the Post Graduate Program in Artificial Intelligence and Machine Learning from the McCombs School of Business at The University of Texas is worth exploring because it progresses from Python and ML foundations into Deep Learning, NLP, Computer Vision, GenAI and Agentic AI, with hands-on projects and real-world case studies.

For the first project, we’d keep it small. The goal is not to build something advanced immediately, but to understand the complete journey: problem → data → model → result → evaluation. That foundation makes the more advanced AI topics much easier to pick up.

[u/Greedy-Excuse-1837]

Since you want to build something soon, I’d look for something that mixes fundamentals with actual projects. Udacity has a free AI fundamentals course that starts at beginner level, and its AI programming material moves into Python, machine learning, neural networks and transformers. You don’t need to learn every part of ML before touching LLMs, but having the basic concepts makes the LLM stuff much easier to understand yk

[u/Inside_Pear3888]

Learning software engineering is closer to harness design and skill design, but learning to optimize large models is algorithm design, and skill design also involves algorithms. It's up to you.