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Machine Learning for Everyone

An introduction to machine learning with no coding involved.

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2 Hours12 Videos36 Exercises116,659 Learners2350 XPData Literacy Fundamentals Track

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Course Description

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!

  1. 1

    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.

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    What is machine learning?
    50 xp
    Generating movie recommendations
    50 xp
    AI, data science, and machine learning walk into a bar...
    50 xp
    What's true about machine learning?
    100 xp
    Machine learning concepts
    50 xp
    Machine learning lingo
    100 xp
    Supervised vs unsupervised
    50 xp
    Machine learning workflow
    50 xp
    Steps for building a model
    100 xp
    A true step
    50 xp
  2. 2

    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.

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  3. 3

    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.

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In the following tracks

Data Literacy Fundamentals
Hadrien Lacroix Headshot

Hadrien Lacroix

Curriculum Manager at DataCamp

Hadrien has collaborated on 30+ courses ranging from machine learning to database administration through data engineering. He's currently enrolled in a Masters of Analytics at Georgia Tech.

Hadrien started using DataCamp when the platform only had 27 courses. He then joined the Support team and helped students before becoming a Content Developer himself.

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Sara Billen Headshot

Sara Billen

Curriculum Manager at DataCamp

Sara is a graduate of a master's degree in Business Engineering and Marketing Analysis. Prior to working at DataCamp she worked as a Data Science consultant for a Belgian IT company. Sara is passionate about education, data science, and business and loves that she is able to combine all of these disciplines in her job as curriculum manager at DataCamp.
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Lis Sulmont Headshot

Lis Sulmont

Workspace Architect at DataCamp

Lis holds a Master's degree in Computer Science from McGill University with a focus on computer science education research and applied machine learning. She's passionate about teaching all things related to data and improving the accessibility of these topics.
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What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA