Information Technology
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Fraud Detection in Python

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5

(3)

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Fraud costs an average company 5% of its annual revenues. This course will show you how to fight fraud using data. To identify fraud activity, you will be able to use both supervised and unsupervised learning techniques. Learn how to effectively deal with high-stakes data in fraud analytics. This course covers both theoretical and technical aspects. It also teaches you how to implement fraud detection methods. This course will provide you with tips and advice based on real-world experiences that can help you avoid common mistakes in fraud analytics.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Supervised Learning with scikit-learn

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Unsupervised Learning in Python

What You Will Learn

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Learn how to detect fraud using Python

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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

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You will use classifiers, adjust them, and compare them to find the most efficient fraud detection model

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You will segment customers, use K-means clustering and other clustering algorithms to find suspicious occurrences in your data

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In this final chapter, you will use text data, text mining, and topic modeling to detect fraudulent behavior

Course Instructors

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Charlotte Werger

Director of Advanced Analytics at Nike

Dr. Charlotte Werger currently works at Nike as a Director of Advanced Analytics. Charlotte is a data scientist with a background in econometrics and finance. She loves applying Machine Learning to a...

Course Reviews

Average Rating Based on 3 reviews

5.0

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