Machine Learning Fundamentals Introduction to Machine Learning Part 1

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Explore the fundamentals behind machine learning. Learn about two common machine learning approaches: • Unsupervised learning, which finds hidden patterns in input data • Supervised learning, which trains a model on known input and output data so that it can predict future outputs. • You’ll also learn about three common techniques within these approaches: • Clustering techniques put data into different groups based on shared characteristics in the data. • Classification techniques predict discrete responses—like whether an email is genuine or spam. • Regression techniques predict continuous responses, such as what temperature a thermostat should be set at or fluctuations in electricity demand. • Get started with MATLAB for Machine Learning with these interactive examples. You can run the examples right in your browser to see MATLAB in action: https://bit.ly/4dIW8w0 • Learn more about MATLAB for machine learning: https://bit.ly/2tUPS0O • Machine Learning with MATLAB eBook: http://bit.ly/2QBFiZO • Get a Free Machine Learning Trial: http://bit.ly/2QKxhBX • ----------------------------------------------------------------------------------------------------------------------------- • Get a free product Trial: https://goo.gl/ZHFb5u • Learn more about MATLAB: https://goo.gl/8QV7ZZ • Learn more about Simulink: https://goo.gl/nqnbLe • See What's new in MATLAB and Simulink: https://goo.gl/pgGtod • © 2018 The MathWorks, Inc. MATLAB and Simulink are registered • trademarks of The MathWorks, Inc. • See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders.

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