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Machine learning is quickly becoming a powerful tool for solving complex modeling problems across a broad range of industries. It is enabling engineers and scientists to develop models which learn from data and can be deployed as a part of packaged applications that can run efficiently on embedded systems as well as cloud infrastructure. The benefits of machine learning are being realized in applications everywhere, including predictive maintenance, health monitoring, financial portfolio forecasting, and advanced driver assistance. However, successfully applying machine learning in practice presents several challenges. It is not always clear which data is going to be the most useful for prediction, and tuning machine learning hyperparameters can consume a large amount of time.

In this webinar, you will learn how machine learning tools in MATLAB address these challenges. We will demonstrate:

Working with large out-of-memory data using the MATLAB “tall” framework
Reducing dimensionality and identifying import features using advanced feature selection techniques
Best practices for tuning hyperparameters to optimize the performance of your model
How to deploy models for use in production IT systems or embedded devices

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