Applied Machine Learning
Explore machine learning topics, learning what they are and how to use them. Topics include:
Feature engineering, which is a technique for transforming raw data into features that are suitable for a machine learning algorithm.
ROC curves, which are used to compare and assess 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.
Part 1: Feature Engineering Explore how to perform feature engineering, a technique for transforming raw data into features that are suitable for a machine learning algorithm.
Part 2: ROC Curves Use ROC curves to assess classification models. Walk through several examples that illustrate what ROC curves are and why you’d use them.
Part 3: Hyperparameter Optimization Learn about hyperparameters, including what they are and why you’d use them. Explore how changing the hyperparameters in your machine learning algorithm enables you to more accurately fit your models to data.
Part 4: Embedded Systems Walk through several key techniques and best practices for running your machine learning model on embedded devices.