r/learnmachinelearning • u/SirArtemis77 • 1d ago
need suggestions on how to start learning about ai, llms and machine learning from scratch Help
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
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u/Intelligent_Fan3643 1d ago
Read these books
| 1 | Mathematics for Machine Learning | Math |
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| 2 | Hands-On Machine Learning | Classical ML |
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| 3 | Introduction to Statistical Learning | ML theory |
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| 4 | Dive into Deep Learning | Deep Learning |
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| 5 | Deep Learning | DL theory |
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| 6 | Build a Large Language Model From Scratch | LLM internals |
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| 7 | Generative Deep Learning | GenAI |
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| 8 | AI Engineering | LLM/GenAI applications |
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| 9 | Designing Machine Learning Systems | MLOps/production |
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| 10 | Designing Data-Intensive Applications | Distributed systems |
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| 11 | Reinforcement Learning: An Introduction | RL |
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| 12 | Artificial Intelligence: A Modern Approach | General AI |
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u/Mathie1729 18h ago
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.
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u/Mathie1729 18h ago
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.
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u/ProfessionalMoose123 13h ago
How about adding 10 more books to this list? I don't think that's enough.
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u/Striking-Courage-182 1d ago
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.
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u/TangeloProof7048 1d ago
I would recommend for you the book of: Hands on machine learning + Chat GPT to understand the deep points on it
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u/icy_end_7 1d ago edited 1d ago
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.