Course Report
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Course Features
Duration
4 hours
Delivery Method
Online
Available on
Limited Access
Accessibility
Mobile, Desktop, Laptop
Language
English
Subtitles
English
Level
Intermediate
Teaching Type
Self Paced
Video Content
4 hours
Course Description
Course Overview
Virtual Labs
International Faculty
Post Course Interactions
Hands-On Training,Instructor-Moderated Discussions
Skills You Will Gain
Prerequisites/Requirements
Unsupervised Learning in Python
Supervised Learning with scikit-learn
What You Will Learn
In this final chapter, you will use text data, text mining, and topic modeling to detect fraudulent behavior
Learn how to detect fraud using Python
You will segment customers, use K-means clustering and other clustering algorithms to find suspicious occurrences in your data
You will use classifiers, adjust them, and compare them to find the most efficient fraud detection model
You'll learn about the typical challenges associated with fraud detection, and will learn how to resample your data in a smart way, to tackle problems with imbalanced data
Course Instructors
Charlotte Werger
Director of Advanced Analytics at Nike