Explore the fundamentals behind machine learning, focusing on unsupervised and supervised learning. You’ll learn what each approach is, and you’ll see the differences between them. In addition, you’ll explore common machine learning techniques including clustering, classification, and regression.
Advanced topics include:
– Feature engineering for transforming raw data into features that are suitable for a machine learning algorithm.
– ROC curves, for comparing and assessing machine learning results.
– Hyperparameter optimization, so you can find the best set of parameters for a machine learning algorithm.
– Embedded systems, including best practices for preparing your machine learning models to run on embedded devices.
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