Zieg Heil
Linkedin.Learning.Applied.Machine.Learning.Feature.Engineering.Online.Class-ZH
Title: Applied Machine Learning: Feature Engineering Online Class
Publisher: Linkedin.Learning
Size: 343M (358802705 B)
Files: 8F
Date: 08/10/2020
Course #: Linkedin.Learning
Type: N/A
Published:
Modified: N/A
URL: www.linkedin.com/learning/applied-machine-learning-feature-engineering
Author: Derek Jedamski
Duration: N/A
Skill: N/A
Exer/Code: [X]
Installation:
Unpack that shit, run that shit
Of course we know its sieg heil, thats how fucking rebelious we are!
Description:
The quality of the predictions coming out of your machine
learning model is a direct reflection of the data you feed it
during training. Feature engineering helps you extract every last
bit of value out of data. This course provides the tools to take a
data set, tease out the signal, and throw out the noise in order to
optimize your models. The concepts generalize to nearly any kind of
machine learning algorithm. Instructor Derek Jedamski provides a
refresher on machine learning basics and a thorough
introduction to feature engineering. He explores continuous and
categorical features and shows how to clean, normalize, and alter
them. Learn how to address missing values, remove outliers,
transform data, create indicators, and convert features. In the
final chapters, Derek explains how to prepare features for
modeling and provides four variations for comparison, so you can
evaluate the impact of cleaning, transforming, and creating
features through the lens of model performance.