Learn essential techniques to create, transform, and optimize features for machine learning models.

Understand the importance of feature engineering in machine learning, focusing on techniques for improving model performance.

Create new features to improve the performance of your Machine Learning models.

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.

Learn the art of feature engineering with Generative AI! This course will teach you how to optimize datasets and automate feature engineering using generative AI strategies.

Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.

In this session, you'll learn about the entire feature flag life cycle, including creation, naming conventions, workflows, testing, and removal.

Features are added and updated in every release. The Feature management experience provides a workspace where you can view a list of features that have been delivered in each release. Then, you can use the workspace to view feature documentation and to enable or disable features.

One of the most important aspects of Machine Learning is using the right data in the right format for your models. In this course you will learn how to extract, normalize, and select the best features for your models using Azure Machine Learning Studio.

This course will teach you how you can store, access, manage, and share your preprocessed machine learning features using the Databricks Feature Store.

Learn how to use feature toggles to separate a deployment from a release and enable multiple releases a day! Apply practices to split up your solutions into NuGet packages and apply a versioning strategy that enables fast release cycles.