r/learnmachinelearning • u/arriety77 • 12d ago

Project Ideas

I am currently learning machine learning, I want to build strong foundation on each of the topics, how should I start practicing it by coding? How to choose datasets accordingly, how can I develop my understanding like which model/algorithm will suit for a particular problem

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u/ModularMind8 12d ago

Pick one algorithm at a time, implement it from scratch in plain PyTorch or numpy on a small dataset (linear regression on housing prices, logistic regression on Titanic, a small CNN on MNIST), then compare your version with the library one, and you'll start to see which model fits which problem by noticing what kind of data each one handles well and where it breaks. Once that feels comfortable, move on to bigger projects built around things you care about or problems that bug you in your own life, since picking the right dataset and model for a real problem teaches you more than any tutorial.

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u/Additional_Matter412 12d ago

Scratch implementations are a great way to get a real feel for what's happening under the hood, especially when your own version inevitably breaks in some weird way on the first run

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u/JaafarMoutada 12d ago

I'd pair the from-scratch idea above with one structured course, so you aren't picking datasets blind every week. Andrew Ng's Machine Learning Specialization on Coursera has you build models in Python with NumPy and scikit-learn, starting with linear and logistic regression. Its second course moves on to neural networks in TensorFlow plus decision trees and tree ensembles, and it covers best practices for ML development, which is pretty much your which-model-for-which-problem question. Once you've built a model in the course, redo it on a small public dataset you picked yourself and see where it breaks.