Last semester, we have learned Machine learing from data visualization to model training to optimization to deployment.

In short, starting from Association Rule mining -> market basket analysis -> Then some Algorithms/Models Manually including Decision tree, random forest, KNN, K-means -> understand feature importance -> Dimentionality reduction using PCA -> Some touch of NLP (Bayse theorem, Naive Bayes) .

Completed DataTalksClub ML Zoomcamp where learn from Raw ML model training to deployment to AWS and fly.io using docker images creating ML pipelines. Anyone want to learn practical Machine Learing should check it out. Deployed some models of ML.

That was all about ML for now. Yes I know Still a lot remaining. I will be making some application related to ML soon from raw code to deployment with proper MLOps stuff...

Plear do share your experience in comment and your advise for your younger self like me.....