Gain an Introduction to Machine Learning Concepts

What's behind the machine learning hype? In this non-technical course, you’ll learn everything you’ve been too afraid to ask about machine learning. There’s no coding required.

You will explore basic yet essential concepts to start your machine learning journey, using hands-on exercises to cement your knowledge. This includes developing an understanding beyond the jargon and learning how this exciting technology powers everything from self-driving cars to your personal Amazon shopping suggestions.

Explore the Machine Learning Basics

How does machine learning work, when can you use it, and what is the difference between AI and machine learning? This course covers all of these topics.

You’ll start by unpacking what machine learning is, exploring its basic definition and its relation to data science and artificial intelligence. Then, you will familiarize yourself with its vocabulary and end with the machine learning workflow for building models.

We wrap up the course by digging deeper into deep learning. You will explore two common use cases for deep learning: computer vision and natural language processing (NLP), and acknowledge the limits and dangers of machine learning.

Syllabus

What is Machine Learning?

In this chapter, we'll define machine learning and its relation to data science and artificial intelligence. Then, we'll unpack important machine learning jargon and end with the machine learning workflow for building models.

Machine Learning Models

Now that you know the basics of machine learning, let's dive a little bit deeper. At the end of this chapter, you will know the different types of machine learning, as well as how to evaluate and improve your models.

Deep Learning

In this chapter, we'll unpack deep learning beginning with neural networks. Next, we'll take a closer look at two common use-cases for deep learning: computer vision and natural language processing. We'll wrap up the course discussing the limits and dangers of machine learning.