Hello! My name is David Chappell, and I’m the author of Understanding Machine Learning here at Pluralsight.
Have you ever wondered what machine learning is? That’s what this course is designed to teach you. You’ll explore the open source programming language R, learn about training and testing a model as well as using a model. By the time you’re done, you’ll have a clear understanding of exactly what machine learning is all about.
It’s all ready and waiting for you – jump in whenever you’re ready, and thanks for visiting me here at Pluralsight.
David Chappell is Principal of Chappell & Associates in San Francisco, California. David has been the keynote speaker for more than a hundred events, and his seminars have been attended by tens of thousands of people in forty-five countries. His books have been published in a dozen languages, and his consulting clients have included HP, IBM, Microsoft, Stanford University, and Target Corporation.
Course Overview Hi everybody, I'm David Chappell. Welcome to my course, Understanding Machine Learning. I'm the principal of Chappell & Associates in San Francisco, California, and I'm convinced that the rise of machine learning is among the most important trends of our time. Machine learning underlies many of the services you use today, including things like speech recognition, and recommendations from Amazon, and even whether a grocery store lets you use your credit card for your latest purchase. This course is a quick introduction to machine learning. No prior knowledge is required. The major topics we'll cover include what machine learning is and what it can be used for, the machine learning process, and the basic concepts and terminology of the field. By the end of this course, you'll know enough to go deeper, if you choose to, and to start thinking intelligently about whether machine learning can help your organization. I hope you'll join me to learn about this important topic, with the Understanding Machine Learning course at Pluralsight.
Introduction Here's the truth. You need to understand machine learning. I don't care who you are, I don't care what your job is, you need to know at least the basics of this technology. And here's why. It's because machine learning is becoming so important. Machine learning is a bigger and bigger part of our world every single day. I'm David Chappell, and in this course, I'll introduce you to the basics of machine learning. To do that, we'll walk through a few modules. We'll start by answering the big question, what is machine learning? Then, we'll look at the machine learning process, and we'll end with a closer look at machine learning. When you're done, you'll understand enough about machine learning to go on to watch more courses, or to have intelligent conversation. Ready? Let's go.
What Is Machine Learning? Let's begin at the beginning. What is machine learning? There's probably no definition that the whole world would agree on, but there certainly are some core concepts. To think about those, think about what machine learning does. The core thing machine learning does is finds patterns in data. It then uses those patterns to predict the future. Some examples, you could use machine learning to detect credit card fraud. Suppose you have data about previous credit card transactions. You could find patterns in that data potentially. That will let you detect when a new credit card transaction is likely to be fraudulent. Or, maybe you want to determine whether a customer is likely to switch to a competitor. Again, you could possibly find patterns in the existing customer data that will help you do that. Or, maybe you want to decide when it's time to do preventive maintenance on a factory robot. Once again, you could look at existing data, you can find patterns that predict when a robot is going to fail. There are lots more, but the core idea is that machine learning lets you find patterns in data, then use those patterns to predict the future.
The Machine Learning Process Understanding machine learning means understanding the machine learning process, and the machine learning process is iterative. You repeat things over and over, in both big and small ways. The machine learning process also is challenging, typically. It's rarely easy, and the reason is that you're working with what are often large amounts of potentially complex data, and you're trying to find patterns, meaningful patterns, predictive patterns, in this data. This can be hard. It's why we work with specialists, it's why data scientists are often so important to machine learning projects. And finally, the machine learning process is often rewarding. As I've said, the benefits of success here can be substantial. But not always. It's always possible that you will fail; be aware of that. This process is worth doing in many, many cases, but it doesn't always succeed.
A Closer Look at the Machine Learning Process It's time we took a closer look at machine learning. In this last module, I want to talk about machine learning concepts in a somewhat more detailed way. I also want to use the terminology that a machine learning person would use. So, we're going to talk about training data. We're going to talk about supervised and unsupervised learning. We're going to look at how we classify machine learning problems and algorithms. I'll discuss training a model, which actually means something very simple, as you'll see. We'll look at testing a model, and finally, we'll talk just a little more about using a model.