Artificial Intelligence is the broad theme that encompasses all tasks carried out by a computer usually done by a human and thus “intelligent”, this range includes everything under the sun from your phone telling you what the weather is to self-driving cars. Machine Learning is a subset of this and Deep Learning is a subsequent subset of that.

Essentially Machine learning is about using a computer to predict outcomes using “features” that you’ve defined, e.g size and number of bedrooms to produce housing prices. Deep learning, however, identifies its own features.

Video Guide here: AI vs. Machine Learning vs. Deep Learning – Relationship Overview

Deep Learning and machine learning both have use cases where they would be preferable over the other. Generally, Deep Learning is preferred as you can get highly accurate results and insights for the model from “features” you otherwise wouldn’t have thought were significant i.e retina pattern in predicting whether someone is male or female. It can also be more useful if you want to generate predictions in a field where you aren’t an expert on the data, however, knowledge in the field will of course help.

Machine Learning tends to be more useful when you want to gain insights from the model such as why the model is making the predictions it is. Furthermore, Deep Learning requires vast amounts of data (thousands, at least), when you don’t have access to this machine learning may be the best choice.

Essentially each one is useful in it’s own right and as you gather experience you will learn when to use which.
Video guide here: Introduction to Deep Learning: Machine Learning vs. Deep Learning