After reading different articles about ML and algorithms, scientist tends to use different words when describing the different aspects in ML.
So now I'm a bit confused myself and I hope you can correct me if I'm wrong.
1) So to my understanding supervised/unsupervised learning are different categories of machine learning algorithms. Each category contains different algorithms such as Neural Networks and Bayesian?
2) Regression, Classification and Clustering are types of models?
3) A model is the result of a trained algorithm?
I hope that I'm not completely wrong, thanks! :)