Machine Learning for Everyone
An introduction to machine learning with no coding involved.
- 4 hours
- 12 Videos
- 37 Exercises
- 25,141 Participants
- 2,400 XP
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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!
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1
What is Machine Learning?
FreeIn 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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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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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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1
What is Machine Learning?
FreeIn 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.
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Lis Sulmont
Curriculum Manager at DataCamp
Lis is a Curriculum Manager at DataCamp. She holds a Master's degree in Computer Science from McGill University with a focus on computer science education research and applied machine learning. Lis is passionate about teaching all things related to data and improving the accessibility of these topics.
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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
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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