A common term in machine learning and data science is the model

In this guide, we're going to talk about the term algorithm.

In the machine learning space, you're going to hear the term fit or fitting quite a bit. And if you've walked through all of the various algorithm implementations and case studies in this course then we have performed a fitting step at each stage.

In this guide, we're going to walk through the term feature.

When working in the machine learning and data science space you're going to hear the term label come up quite a bit. And so it's going to be helpful to have an understanding on what exactly a label is.

In this lesson, we're going to walk through the term regularization.

In this guide and we're going to walk through two different terms in the machine learning space and they are model parameters and hyperparameters.

In this lesson, we're going to walk through two machine learning terms lazy and eager learning.

In this lesson, we're going to walk through two key terms in the machine learning space and they are overfitting and generalization.

In this lesson, we're going to discuss the term Gaussian distribution. Now, this is a slightly intimidating phrase but it really represents a pretty basic mathematical principle.

In this lesson, we're going to walk through two terms that can be a little bit confusing if you've never heard them before and they are generative and discriminative algorithms and so these are two different types of algorithms that pretty much every algorithm that you're going to work with will fit in in some way or another.