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This course introduces Feature Engineering techniques where we will discuss how and when to apply them and the methodologies to preprocess and transform the dataset for optimal use in machine learning models. Feature Engineering is a crucial step into the data science workflow, so in this course you will get hands-on practice choosing features and preprocess them.
Duration
9 hours
Difficulty
professional
INE's world-class IT trainingInstructor for this course
Kristin Day
This course is composed by the following modules
INE's world-class IT training
Module 1
Introduction1 video
Course Introduction
Module 2
Features Introduction7 videos
Intro to Features
Binary Features
Nominal Features
Ordinal Features
Cyclical Features
Date Features
Hands-on Feature Encodings
Module 3
Imbalance and Incongruent Labeling4 videos
Categorical Imbalance
Rarely Occurring Categories
Incongruent Labeling
Categorical features and dirty cats
Module 4
Missing Data2 videos
Missing Data - Part 1
Missing Data - Part 2
Module 5
Derived Features2 videos
Derived Features - Part 1
Derived Features - Part 2
Module 6
Outliers and Data transformation5 videos
Outliers & Skew Defined
Outliers Skew Transform In Use
Scaling Data
Dimensionality Reduction
Feature Engineering on Census Data
Module 7
Conclusion1 video
Course Conclusion
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