If you're not a machine learning expert, your first questions about machine learning might be what is it? Why is it important? How do I use it to create value in my business as part of my profession? I'll cover these questions and more in this module. I'll begin by briefly going over what machine learning is. Next, I'll differentiate between a few key terms. Artificial intelligence, ML, and deep learning. I'll examine a few real world examples of machine learning. And as we start talking about machine learning projects, I'll go over the various phases. I'll dive deep into phase one, which is to assess the feasibility of an ML use case. Finally, I'll close the module with an exercise to help you brainstorm a few of your own ML use cases. Let's jump in. [MUSIC] So what is machine learning? It's a way of teaching a computer to solve problems by feeding it examples of the correct answers. Usually these problems are about predicting something. For example, you can predict how long it takes to travel from one location to another by feeding the computer examples of the completed journeys. Similarly, you can predict the estimated taxes owed by feeding the computer examples of tax filings. You do the same for predicting weather patterns over the next few days. But what kinds of problems should ML solve? That's the key question. And where should you look for the best types of problems? You can start by looking at your organization's mission statement. Every organization has a mission statement. For example, at Google, our mission is to organize the world's information and make it universally accessible and useful. At Home Depot, their goal is to provide the highest level of service, the broadest selection of products, and the most competitive prices to the do it yourself customers, such as homeowners. The do it for me customers who provide installation services and professional customers, such as contractors, interior designers, and renovators in the home improvement business. At Spotify, their mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the opportunity to enjoy and be inspired by it. Your company has a mission too. As a business professional, you may want to further that mission. And remember, a mission can be applied to any size organization, team, group, or even for yourself. Perhaps your team's mission is to provide a particular service and your personal mission is to improve customer satisfaction. Whatever the case may be, you can use the mission as your starting point to think about how you can use machine learning to improve business processes or create new value. Before we go too far, let's make sure you're familiar with a few key terms that will be useful for when you start working with ML experts and data scientists. Watch the next video to learn more.