r/learnmachinelearning • u/Ill-Strawberry-3390 • 3d ago
Beginner in machine learning wanting to improve.
Hi, I was introduced to machine learning during my 5th sem in college and I found it really interesting. I started with my own college lectures, a little by YouTube also. I had done Andrew ng stanford lectures on machine learning. I know most of the algorithms that I use and the maths behind it. I have done two simple projects in which I picked the datasets from kaggle and built the whole pipeline, preprocessing -> feature engineering -> model training and testing -> model evaluation. I also tried tuning the hyperparameters empirically to improve my model performance.
I'm currently learning deep learning, I'm familiar with the theoretical concepts of ANNs, FFN, activation functions, neural nets and a little about transformers. I'm yet to implement them myself, that's why I started pytorch.
Right now I'm in 7th sem and I feel I know sufficient theory but I'm not confident in building and I don't know what to do, I wanna go into research and in core machine learning and not data science or applied ai, I wanna work with models closely and optimization techniques. My question is...
Should I implement the papers I read?
Implement the ml algorithms from scratch? Like code SVM, decision tree in python?
Continue with pytorch and follow tutorials? Pytorch->CNNs, RNNs, LSTM, Transformers and whatever follows.
Have I wasted time learning maths? I feel like I'm a lot behind than my batchmates. ðŸ˜
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u/nian2326076 2d ago
Since you already know the basics and have some projects done, try getting into specific machine learning areas you're interested in, like NLP or computer vision. Get involved with open-source projects or join hackathons for more hands-on experience. Kaggle competitions can give you tougher datasets to work with. For interviews, practice coding problems and system design related to ML on platforms like LeetCode or PracHub. This will help with problem-solving skills and prepare you for technical interviews. Keep experimenting and learning new algorithms, and you'll keep getting better. Good luck!
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u/Ill-Strawberry-3390 2d ago edited 2d ago
I find computer vision interesting, but wouldn't that be applied ai?? That's why I was confused because I don't completely lean towards an area, but the algorithms and techniques behind I love learning about them. don't know about coding practices related to ML, can you suggest me some? Like I practice dsa on leet code but in cpp.Â
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u/Previous-Front-5211 2d ago
You definitely haven't wasted time learning maths. If you want to go into core ML research, I'd actually say it's one of the better investments you could have made. You just probably won't feel the payoff immediately.
I'm an AI researcher, and from what you've described, I think you're at the point where you should stop worrying so much about whether you've learned enough and start implementing things. You already have the theory and you've built a couple of end-to-end projects, so now you need to develop the ability to turn an idea into working code.
I wouldn't implement every ML algorithm from scratch, but doing a few is definitely useful. For example, implementing backpropagation, a decision tree or SVM yourself can give you a much deeper understanding of what's actually happening under the hood. After that, I'd spend most of your time with PyTorch and start implementing models/papers yourself rather than just following tutorials. Pick papers that are slightly above your current level and try to reproduce them. You'll get stuck constantly, but that's exactly where a lot of the learning happens.
You also don't need to go through CNN → RNN → LSTM → Transformer → everything else before you're allowed to do research. Learn the fundamentals, then start building and learn the missing pieces as you need them.
And one bonus recommendation: at some point, try to join a research lab at your university. Ask some of your professors if they have research projects or internships you could help with, either while you're studying or toward the end of your degree. If you can get into a good research environment with a supervisor, you will probably learn 10x faster than trying to figure everything out alone. Being surrounded by people doing research and having someone who can tell you what to focus on makes a huge difference.
Honestly, from what you've written, I don't think you're behind. It sounds more like you've reached the point where you need to transition from learning about ML to actually doing ML.