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 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. You will also learn how to create features from the date and time variables and how to handle categorical variables with a lot of categories.
The methods that you will learn were described in scientific articles, are used in data science competitions, and are commonly utilized in organizations. And what’s more, they can be easily implemented by utilizing Python's open-source libraries! Throughout the lectures, you’ll find detailed explanations of each technique and a discussion about their advantages, limitations, and underlying assumptions, followed by the best programming practices to implement them in Python. By the end of the course, you will be able to decide which feature engineering technique you need based on the variable characteristics and the models you wish to train. And you will also be well placed to test various transformation methods and let your models decide which ones work best.
This course offers:
- Flexible deadlines: Reset deadlines based on your availability.
- Get a Certificate when you complete
- 100% online
- Advanced level
- Approximately 10.5 hours to complete
- Subtitles: English
Udemy Rating: 4.7/5
Enroll here: https://www.udemy.com/course/feature-engineering-for-machine-learning/
