Course on feature engineering for machine learning. The MOST comprehensive course on feature engineering available online.
Transform your data and build better performing 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," published by Packt.
Welcome to the most comprehensive course on feature engineering for machine learning available online.
In this course, you will learn everything you need to preprocess your datasets to train machine learning models like linear regression, logistic regression, decision trees, random forests and gradient boosting machines.
Feature engineering consists in using domain knowledge and statistical methods to create features that make machine learning algorithms work effectively.
Raw data is almost never suitable to train machine learning models. In fact, data scientists devote a lot of effort to data analysis, data engineering and preprocessing, and feature extraction, to create the best features to train predictive models.
Feature engineering includes imputation of missing data, encoding of categorical variables, transformation or discretization of continuous variables, combination of variables, extraction of dates and times, and much more.
In this course, you will learn about missing data imputation, encoding of categorical features, numerical variable transformation, discretization, and how to create new features from your dataset.
You probably saw a lot of courses on other learning platforms like Coursera or Udemy. In fact, this is the full version of the Udemy course. Why is this course special?
While most online courses will teach you the very basics of feature engineering, like imputing variables with the mean or transforming categorical features using one hot encoding, this course will teach you all of that, and much more.
Here, you will first learn the most popular techniques for variable engineering, like mean and median imputation, one-hot encoding, transformation with logarithm, and discretization. Then, you will discover more advanced methods that capture information while encoding or transforming your variables, to obtain better features and improve the performance of regression and classification models.
