Ten Machine Learning Concepts You Should Know for Data Science Interviews

Ten Machine Learning Concepts You Should Know for Data Science Interviews

A summary of the most fundamental machine learning concepts. In this article, I’m going to cover what I think are the ten most fundamental machine learning concepts that you should learn and understand.

As you may know, there’s an endless amount of information and knowledge that data science and machine learning has to offer. That being said, there are only a handful of core ideas that the majority of companies test for. The reason for this is that these ten concepts serve as the base for more intricate ideas and concepts.

In this article, I’m going to cover what I think are the ten most fundamental machine learning concepts that you should learn and understand.

With that said, here we go!

1. Supervised vs Unsupervised Learning

You’re probably wondering why I even bothered to put this in because it’s so fundamental. However, I think that it’s important that you truly understand the difference between the two and are able to communicate the differences:

Supervised learning involves learning on a labeled dataset where the target variable is known.

Unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes — there’s** no target variable**.

Now that you know the distinction between the two, you should know whether a machine learning model is supervised or unsupervised, and you should also know whether a given scenario requires a supervised learning algorithm or an unsupervised learning algorithm.

For example, if I wanted to predict whether a customer buys milk given that they already bought cereal, would that require a supervised or unsupervised learning algorithm?

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