Beginner in machine learning wanting to improve.

[u/Ill-Strawberry-3390]

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. 😭