Feature Engineering for Machine Learning

Online Course

Udemy

What is the course about?
Prerequisites & Facts
Cost, Certificate & Degree
Reviews & Experiences

What is the course about?

Feature Engineering for Machine Learning

The course Feature Engineering for Machine Learning is an online class provided by Udemy. It may be possible to receive a verified certification or use the course to prepare for a degree.

Transform the variables in your data and build better performing machine learning models

Course description

Feature Engineering for Machine Learning

Learn multiple techniques for missing data imputation
Transform categorical variables into numbers while capturing meaningful information
Learn how to deal with infrequent, rare and unseen categories
Transform skewed variables into Gaussian
Convert numerical variables into discrete

A Python installation
Jupyter notebook installation
Python coding skills
Some experience with Numpy and Pandas
Familiarity with Machine Learning algorithms

9.5 hours on-demand video
18 articles
5 downloadable resources
Full lifetime access
Access on mobile and TV
Assignments
Soledad Galli

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Prerequisites & Facts

Feature Engineering for Machine Learning

Course Topic

Business, Data Analytics, Decision Making

University, College, Institution

Udemy

Course Skill Level

see course summary

Course Language

English

Place of class

Online, self-paced (see curriculum for more information)

Degree

Certificate

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Degree & Cost

Feature Engineering for Machine Learning

To obtain a verified certificate from Udemy you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.

Udemy

Feature Engineering for Machine Learning

provided by Udemy

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Reviews

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School: Udemy

Topic: Data Science, Development, Machine Learning

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