A long time ago, I made a huge bet: After 15 years of experience as a software engineer, I decided to focus on Machine Learning. It was tough, and most people thought I was crazy. They were wrong. That bet paid off and became one of my best decisions. If you write software for a living, I want to show you in 2 minutes why learning Machine Learning is like going to the Hogwarts School of Witchcraft and Wizardry. Let's start with a simple programming exercise: "Write a function that returns zero if the supplied number is even or one if the number is odd." If you are a developer, you know how to write this function. To keep things simple, we can represent the function like this: y = f(X) Here, "f" is the function that does the work, "X" is the input number, and "y" will be zero if the number is even or one if the number is odd. The relationship between X and y is obvious in this example, so writing the function is straightforward. But what happens when that's not the case? How would you solve any of the following problems? • Predict tomorrow's temperature based on previous years. • Predict a missing word in a sentence. • Determine whether an image shows a dog or a cat. That's where Machine Learning comes in! It gives you the tools to solve problems other software developers can't solve. Programmers write rules and apply them to data to produce answers. But if those rules aren't obvious, they are stuck. With Machine Learning, we don't write the rules; we learn them from data. That's a superpower! Look at the attached image from @fchollet's book. It's a good summary of this idea. Think about how we learn: we look at things, discover patterns, and then generalize those ideas whenever we see something similar. That's the same principle behind Machine Learning: we use existing data to infer the answer to similar, future samples. It's 2023. The best time to start learning Machine Learning is now. I'd make the same bet all over again.