Feature Engineering for Machine Learning

Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this course, you will learn about variable imputation, variable encoding, feature transformation, discretization, and how to create new features from your data. Master Feature Engineering and Feature Extraction.

In this course, you will learn multiple feature engineering methods that will allow you to transform your data and leave it ready to train machine learning models. Specifically, you will learn:How to impute missing dataHow to encode categorical variablesHow to transform numerical variables and change their distributionHow to perform discretizationHow to remove outliersHow to extract features from date and timeHow to create new features from existing onesCreate useful Features with Math, Statistics and Domain KnowledgeFeature engineering is the process of transforming existing features or creating new variables for use in machine learning. Raw data is not suitable to train machine learning algorithms.

Instead, data scientists devote a lot of time to data preprocessing. This course teaches you everything you need to know to leave your data ready to train your models. While most online courses will teach you the very basics of feature engineering, like imputing variables with the mean or transforming categorical variables using one hot encoding, this course will teach you that, and much, much more.

In this course, you will first learn the most popular and widely used 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 improve the performance of machine learning models. You will learn methods like the weight of evidence, used in finance, and how to create monotonic relationships between variables and targets to boost the performance of linear models.