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...

What's true about machine learning?

Machine learning concepts

Machine learning lingo

Supervised vs unsupervised

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

Tasks

Classification

Regression

Regressing with class

Unsupervised learning

Supervised

Unsupervised

We don't need no supervision

Gotta cluster 'em all!

Evaluating performance

True

False

True or false?

Land of confusion

Improving performance

Options

Hyperparameter Tuning

Dimensionality Reduction

Ensemble Methods

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.
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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. 
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<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.
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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.
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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 Temelleri

Veri Konularını Anlama

While training a Decision Tree, change the minimum count of observations for what constitutes a branch.

Write a script to try 3 different values of three different hyperparameters of your SVM, and keep the configuration that yields the best results.

On a dataset of running sessions to assess a treadmill's longevity, with duration, speed, inclination, remove the column "calories_burned".

Run an analysis to identify the four main features that give the best predictions, and get rid of the others.

Use the most common prediction from an SVM, a K-Means and a Decision Tree model to assign a category to an observation.

Use the average prediction from a Linear Regression, a K-Means and a Decision Tree model to assign a category to an observation.

It's a long way to the top

Understanding Machine Learning

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