I've studied and understood well quite a bit of theoretical concepts of Machine Learning (except Deep Learning): e.g. the mathematics behind several classification and clustering algorithms, SGD and related optimization stuff, regularization, cross-validation, class imbalance and how to handle it. I've basic notions in Python and previously wrote simple codes in Python as well. I find the math behind ML easy, having an advanced degree in pure math, and I thoroughly enjoyed reading from books like Bishop's Pattern Recognition and Machine Learning.
However, since I'm applying for several data scientist positions, I'd like to practice building models and get my hand dirty to get a feel for industrial problems. Courses like Udemy doesn't seem to cover this, as they're mostly lectures with basic theory. On the other hand, Kaggle seems bit daunting for a total beginner. Hence, I'm looking for simple, previously solved, exercises like "Build a random forest and test with this and that metric", ideally something that can be solved within hours, not days (I hear Kaggle problems take time). Since I'm completely alone in this effort, ideally I'd like an exercise platform that contains the solutions or hints. I'd appreciate if anyone could suggest me some platforms, keeping in mind that I'm an absoulte beginner in practical ML.