Confused about the different aspects in Machine Learning 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! :)
Good question and welcome to Datascience

Imagine you have the tree as follows.

                 Machine Learning Models
                          |
           ----------------------------------------------------
           |                                                  |
       Supervised                                         Unsupervised
           |                                                  |
   - --------------------                                  Clustering
   |                    |
Regression          Classification


A model is indeed a trained version of the algorithm you are chosing,
regardless of whether its Regression, Classification or Clustering. The above
tree is ofcourse a simplified version of all ML algorithms.