Welcome back to Obviously AI University. 

In our last video, we defined Artificial Intelligence. In this video, we are going to explore machine learning - what it is and how it relates to artificial intelligence.

Imagine you were asked to scan a small parking lot and state the colors and manufacturer of each car? This would be a fairly easy task. You’d look for patterns scanning for the cars for color, then you’d make your way through the household brands we know that manufacture car. 

Now, say we were asked to perform the same task on 10 million cars. Suddenly, our task to find commonality becomes much more difficult, very time-consuming, and potentially error-prone.

This example helps us answer the question: What is Machine Learning?

Machine Learning is a subfield of artificial intelligence that solves problems using algorithms and statistical models to extract knowledge from data. Machine learning models are used to draw inferences and patterns from the data.

Simply put, Machine earning is the art of finding patterns in data.

In our car example, machines can inherently do this work faster, but they need to know what to look for and where to find these patterns and this is where it becomes an art. Then through trial and error, you’ll be able to identify the commonalities of these cars incredibly quickly. Once this trial and error are refined, you can eventually predict future events at scale.

We’re still early into this course and by the end, you’ll know how to build machine learning models to make accurate decisions at scale. This may sound too good to be true or overly ambitious considering using traditional routes to build Machine Learning models takes a huge amount of time. But I’m up for the challenge if you are.

Next up we’ll walk through supervised learning, so join us in our next video.

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