The most comprehensive online course on feature selection for machine learning. You will learn multiple feature selection methods to select the best features in your data set and build simpler, faster, and more reliable machine learning models.
Create simpler, faster and more reliable machine learning models.
If you're disappointed for whatever reason, you'll get a full refund.
Sole is a lead data scientist, instructor and developer of open source software. She created and maintains the Python library Feature-engine, which allows us to impute data, encode categorical variables, transform, create and select features. Sole is also the author of the"Python Feature engineering Cookbook" by Packt editorial.
Welcome to Feature Selection for Machine Learning, the most comprehensive course on feature selection available online.
In this course, you will learn multiple feature selection methods to select the best features in your data set and build simpler, faster, and more reliable machine learning models.
Feature selection is the process of identifying and selecting a subset of features from the original data set to use as inputs in a machine learning algorithm.
Data sets usually contain a large number of features. We can use multiple algorithms to quickly disregard irrelevant features and identify those important features in our data.
Feature selection algorithms can be divided into 1 of 3 categories: filter methods, wrapper methods, and embedded methods.
Filter methods comprise basic data preprocessing steps to remove constant and duplicated features and statistical tests to assert feature importance. Wrapper methods wrap the search around the estimator. They use backward and forward selection to examine and identify the best set of features. Embedded methods combine feature selection with the fitting of the classifier or regression model.
Feature selection is key to creating easier to interpret and faster models, as well as to avoiding overfitting. When creating machine learning models to use in the real-world, feature selection is an integral part of the machine learning pipeline.
In this course, you will learn multiple feature selection techniques, gathered from scientific articles, data science competitions and my experience as a data scientist, to identify relevant features in your data sets.
You will learn the following filter methods:
You will learn the following wrapper methods:
You will learn the following embedded methods:
You will learn the following hybrid methods:
