Machine Learning vs. Deep Learning: What’s the difference? Posted on : Nov 01 - 2021

Machine Learning and Deep Learning are often confused with one another because they both fall under the data science umbrella. While Machine Learning and Deep Learning share similarities, there are also key differences between them.

Here we’ll briefly explain these differences along with three examples for each type of data science.

1. Data Sets, Data Sets, Data Sets

The first key difference between Machine Learning and Deep Learning lies in the type of data being analyzed. Machine Learning data sets are much larger than Deep Learning data sets.

Machine Learning data sets are often structured data, which means they have labels or categories associated with the data points. For example, this data set is related to housing data and includes information on houses in Boston based on 4 attributes:

Machine Learning data sets are large but structured which makes them a good fit for a data scientist.

Deep Learning

Deep Learning data sets are often images, audio files, or video files with no labels associated with them. An example of this type of data set would be photos of dogs and other animals:

Dogs | Cats | Horses | Snakes

Deep Learning data sets are large but unstructured.

Machine Learning data sets are larger than Deep Learning data sets because it can be very expensive to label data. For example, if data scientists were to build an image recognition data set for dogs, they would need to hire individuals to read the labels associated with each photo to correct inaccurate data. View more