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.

What is machine learning?

Generating movie recommendations

AI, data science, and machine learning walk into a bar...

Options

True

False

What's true about machine learning?

Machine learning concepts

Feature

Label

Target Variable

Machine learning lingo

Supervised vs unsupervised

Machine learning workflow

Machine Learning Workflow 

Steps for building a model

A true step

What is Machine Learning?

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.

Supervised learning

Warm up

Regressing with class

Unsupervised learning

We don't need no supervision

Gotta cluster 'em all!

Evaluating performance

True or false?

Land of confusion

Improving performance

It's a long way to the top

Explore hyperparameter tuning

Machine Learning Models

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.

Deep learning

What is deep learning?

Should I use deep learning?

Computer vision

Image data

The process

Natural Language Processing

Sentiment analysis

Classifying machine learning tasks

Bag of words

Limits of machine learning

To black box or not to black box?

Spotting bias in machine learning

Congratulations!

Deep Learning

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. Hands-on exercises will help you get past the jargon and learn how this exciting technology powers everything from self-driving cars to your personal Amazon shopping suggestions.

How does machine learning work, when can you use it, and what is the difference between AI and machine learning? They’re all covered. Gain skills in this hugely in-demand and influential field, and discover why machine learning is for everyone!


<h2>Gain an Introduction to Machine Learning Concepts</h2> 
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.
<br><br>
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. 
<br><br>
<h2>Explore the Machine Learning Basics</h2> 
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.
<br><br>
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.
<br><br>
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.

This two-hour introduction to machine learning course teaches the essential ML concepts. Topics include machine learning vocabulary, models, and deep learning. 

Understanding Machine Learning

An introduction to machine learning with no coding involved.

AI Fundamentals

Understanding Data Topics

Extract the features for each product in the shop, including brand, number of times viewed, cost, color, and clothing type.

Split the dataset into 2/3 and 1/3 for the train and test dataset, respectively

Train the model using the train dataset and a logistic regression model

Evaluate the percentage of products in the test dataset that were accurately predicted as bought

Steps for building a model

Understanding Machine Learning

Hands-on interactive exercise