From the course: Artificial Intelligence Foundations: Machine Learning

Reviewing the course scenarios

From the course: Artificial Intelligence Foundations: Machine Learning

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Reviewing the course scenarios

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- I've picked three fun, real-world scenarios that I'll use to drive the concepts home during this course. What I love about this course is that it's not just all theory. You'll navigate the entire machine learning lifecycle through practical, hands-on examples of real machine learning systems I've developed. During the demonstrations, you can follow along with me step by step, hands on keyboard, or just sit back, and watch me while I explain the details. The first scenario in our course predicts the cost of homes in California. For your information, California is on the West Coast of the United States. We'll use a form of machine learning called supervised learning to solve a linear regression problem. Don't let these new, fancy terms: supervised learning and linear regression, be off-putting. Write them down, and as you learn the meanings, you'll feel your expertise growing. We'll take features of a home like location, age, number of bedrooms, ocean proximity, et cetera, to predict what that home will sell for in the beautiful state of California. The second scenario in our course classifies, or identifies, flowers in an image. We'll use supervised learning again, but we're solving a multi-label image classification problem this time. We'll feed the machine images of flowers, doing a training process to teach it to identify dandelions, sunflowers, daisies, tulips, and my favorite: roses. And we'll see what happens when we give the model an orchid to classify, something we didn't teach it about. The third scenario in our course predicts crime in the UK. For your information, the UK, United Kingdom, comprises four countries: England, Scotland, Wales, and Northern Ireland. We'll use supervised learning again, but we're solving a binary classification problem this time. We'll take features from stop-and-search data like location, gender, age, ethnicity, time of day, and more to predict whether or not a stop will lead to an arrest. I'm sure one of these scenarios will resonate with you and do a great job of helping you understand the concepts.

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