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Ben Bolstad has completed

Understanding Machine Learning

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2 hours
2,350 XP
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Course Description

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.
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  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

AI FundamentalsUnderstanding Data Topics
Lis Sulmont HeadshotLis Sulmont

Content Program Manager at Duolingo

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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Hadrien Lacroix HeadshotHadrien 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.

Follow Hadrien on LinkedIn
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Sara Billen HeadshotSara Billen

Data Scientist 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 at DataCamp.
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