Feature Selection for Machine Learning

Learn how to select features and build simpler, faster and more reliable machine learning models.

This is the most comprehensive, yet easy to follow, course for feature selection available online. Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor’s experience as a Data Scientist.

You will have at your fingertips, altogether in one place, multiple methods that you can apply to select features from your data set.

The course starts describing simple and fast methods to quickly screen the data set and remove redundant and irrelevant features. Then it describes more complex techniques that select variables taking into account variable interaction, the feature importance and its interaction with the machine learning algorithm. Finally, it describes specific techniques used in data competitions and the industry. 

The lectures include an explanation of the feature selection technique, the rationale to use it, and the advantages and limitations of the procedure. It also includes full code that you can take home and apply to your own data sets.

This course is therefore suitable for complete beginners in data science looking to learn how to go about to select features from a data set, as well as for intermediate and even advanced data scientists seeking to level up their skills.

With more than 50 lectures and 8 hours of video this comprehensive course covers every aspect of variable selection. Throughout the course you will use python as your main language.

So what are you waiting for? Enrol today, learn how to select variables for machine learning, and build simpler, faster and more reliable learning models.