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Understanding Machine Learning
An introduction to machine learning with no coding involved.
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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
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.
What is machine learning?50 xpGenerating movie recommendations50 xpAI, data science, and machine learning walk into a bar...50 xpWhat's true about machine learning?100 xpMachine learning concepts50 xpMachine learning lingo100 xpSupervised vs unsupervised50 xpMachine learning workflow50 xpSteps for building a model100 xpA true step50 xp - 2
Machine Learning Models
FreeNow 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 learning50 xpWarm up50 xpRegressing with class100 xpUnsupervised learning50 xpWe don't need no supervision100 xpGotta cluster 'em all!50 xpEvaluating performance50 xpTrue or false?100 xpLand of confusion50 xpImproving performance50 xpIt's a long way to the top100 xpExplore hyperparameter tuning50 xp - 3
Deep Learning
FreeIn 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 learning50 xpWhat is deep learning?100 xpShould I use deep learning?50 xpComputer vision50 xpImage data50 xpThe process100 xpNatural Language Processing50 xpSentiment analysis50 xpClassifying machine learning tasks100 xpBag of words50 xpLimits of machine learning50 xpTo black box or not to black box?100 xpSpotting bias in machine learning50 xpCongratulations!50 xp
In the following tracks
Understanding Data Topics
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.
Follow Hadrien on LinkedIn
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
Sara 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 as curriculum manager at DataCamp.
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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