The video tutorial explains machine learning models, focusing on supervised learning. It describes how models can be classification or regression types and how they predict labels from input vectors. The tutorial emphasizes that models are mathematical functions with parameters that need to be optimized for accurate predictions. It also discusses the process of training models by selecting the right function and settings to map input vectors to output labels effectively.

How does a machine learning model generate predictions?

What is the goal of training a machine learning model?

What does it mean for a model to generate a label near the true label?