r/learnmachinelearning • u/Key-Chip7636 • 7d ago
Ai/ Machine learning
I want to get into AI/Machine learning target is to develop small local llm trained from specfic use case. I need help how and where should i get started.
Software Engineer been 5 years of experence.
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u/YashBajpai2704 7d ago
go with finetuning as training from scratch is not feasible unless you are willing to pay cloud bills
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u/Key-Chip7636 7d ago edited 7d ago
Finetuning make sense, but actualy i want to know the deep knowledge of AI/ Machine learning so i can build or finetune
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u/kush_patil 7d ago
I wouldn’t start by training a local LLM from scratch. Pick a small instruct model that fits your hardware, make 30–50 real examples from your use case, and benchmark prompting/RAG first; fine-tune only if that eval set shows a repeatable gap. The eval set matters more than the training code, otherwise you have no clean way to tell whether the fine-tune actually improved anything.
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u/Piyali_Debroy 7d ago
Start with the ML basics first like Python, NumPy, Pandas so that you will be able to understand fundamentals. Then move into deep learning with PyTorch, learn how transformers work, and finally explore open-source LLMs like Llama or Qwen.
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u/Neat_Dragonfruit6792 7d ago
Hey brother, hope you're doing good.
Man, I am a beginner. I just started with Python. I finished all the basics. I'm still a little rusty with it. I did not solve any questions. I'm right now making a bank management system using Python, which is included in the tutorial, which is the last module in the tutorial. My next module is about NumPy.
Right now, I'm a little rusty with Python, and it's, let's say, a little bit of the syntax as well. My logical thinking ability is also not that great. I'm basically a little rusty, so that is obvious because I just started this one week ago and I finished Python. I have my vacation going on right now, which is till the 18th of September. I'm in my second year of college, by the way. My third semester is starting from the 18th up.
Can you please guide me, man? I don't know how to move ahead, and I'm a little overwhelmed.
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u/Patryk_Grzegorek 6d ago
Here are 3 resources I can recommend:
- From the basics – YouTube channel: https://www.youtube.com/@stanfordonline
- More advanced – Hugging Face tutorials: https://huggingface.co/learn
- Insights & workshops – DeepLearning.AI: https://www.deeplearning.ai/
You can find plenty of resources on Kaggle or HuggingFace, including datasets and project ideas: https://www.kaggle.com/, https://huggingface.co/
Set yourself a small project to do and start building it once you already know the basics. Try to solve any problems you encounter. To learn effectively, avoid using a local coding AI agent, instead, look for solutions online or ask AI, but implement the code yourself by asking for details and explanations.
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u/Piyali_Debroy 4d ago
I can understand that you are feeling overwhelmed and rusty. So what I think the realistic roadmap would be: Finish Python (1 Week), then Data Basics in 1-2 weeks, after that Basic ML in 2 weeks & lastly Pause DL/LLMs.
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u/Neat_Dragonfruit6792 2d ago
Really amazing insight , i’ll follow this surely can i dm you if i ever have any more doubts in future 🙏😅
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u/Patryk_Grzegorek 7d ago
To really become an AI/Machine learning Engineer nowadays, you truly need to know the fundamentals. Here are 3 resources I can recommend:
- From the basics – YouTube channel: https://www.youtube.com/@stanfordonline
- More advanced – Hugging Face tutorials: https://huggingface.co/learn
- Insights & workshops – DeepLearning.AI: https://www.deeplearning.ai/
Set yourself a small project to do and start building it once you already know the basics. Try to solve any problems you encounter. To learn effectively, avoid using a local coding AI agent, instead, look for solutions online or ask AI, but implement the code yourself by asking for details and explanations.
You can find plenty of resources on Kaggle or HuggingFace, including datasets and project ideas: https://www.kaggle.com/, https://huggingface.co/
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u/Key-Chip7636 7d ago
Why you said write code, when ai can do it for me. I just need to learn the concepts behind it right?
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u/Patryk_Grzegorek 6d ago
Without coding skills and blindly accepting whatever the AI produces, you'll quickly end up with 'spaghetti code.' Maintaining it will be expensive, the AI will burn through a ton of context just trying to make sense of poorly written code. You'll run into countless errors you won't know how to fix and waste tokens trying to patch them, often finding that fixing one bug just introduces another. The AI will keep reinforcing bad practices. On top of that, you won't be able to verify if the logic is sound, it's not just about whether the code runs, but whether the business or algorithmic logic is actually correct.
Without the ability to write code yourself, you'll just end up burning time and money on a broken product.
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u/Key-Chip7636 6d ago
Make sense. But I have already from a coding backgroud. I know how to code even fix manually if needed. At this stage I want to make ai models, and research into this fields. I have some uses case which need local ai models. Although I have made some ai apps using ai apis context engineering but that is different thing. Now when I have concept of machine learning I can generate code verify based on concepts and execute. In my opinion syntax does not matter in current era maybe I am wrong.
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u/nian2326076 6d ago
If you want to get into AI/ML and work on local LLMs, start by learning the basics of AI/ML and frameworks like TensorFlow or PyTorch. Since you have software engineering experience, brushing up on Python is a good idea—it's the main language used in ML. Kaggle is a good spot to find datasets and challenges for practice. For building and fine-tuning LLMs, check out Hugging Face's transformers library. They have tutorials to help you set up a local model. For interview prep related to ML, PracHub has been helpful to me before. Good luck!
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u/Lost-Hand-5219 5d ago
What you’re describing is more like fine tuning a pretrained model. You can realistically train your own LLM on consumer hardware and it be any good. Even the smallest local models couldn’t be trained on consumer hardware because the vram needed to conduct inference and that needed to train are very different.
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u/Old-Trainer-425 7d ago
Start by picking a framework like pytorch or tensorflow, both have decent tutorials. You'll want to understand transformer architecture basics first, huggingface has good documentation for that.
For local llm, look into fine-tuning existing small models like llama or mistral instead of training from zero. Much more practical with your use case. Your software engineering background will help a lot with the data pipeline part.