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Machine Learning Project #5 – Fraud Detection in Credit Payments Online Training


EduCBA

Summary

Price
£99 inc VAT
Or £33.00/mo. for 3 months... Read more
Study method
Online, self-paced
Duration
1 hour
Access to content
Lifetime access
Qualification
No formal qualification
Additional info
  • Certificate of completion available and is included in the price

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Overview

Machine Learning Project #5 – Fraud Detection in Credit Payments

  • 1+ Hours OF HD VIDEOS
  • Verifiable CERTIFICATION
  • Practical SKILLS DEVELOPMENT
  • Accelerate YOUR CAREER
  • Lifetime Access 24*7 Unlimited Access
  • Access through any device
  • Technical support
  • Mobile App Access

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.

Description

Machine Learning Project #5 – Fraud Detection in Credit Payments

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.

Through this training we are going to guide you through the process of understanding the concept of fraud detection in credit payments using a case-study. We have used Kmeans and hierarchical clustering for understanding the data and also used other visualization techniques and methods to compare and understand the flow of Data.

Who is this course for?

  • Anyone who wants to learn about data and analytics
  • Data Engineers
  • Analysts
  • Architects
  • Software Engineers
  • IT operations
  • Technical managers

Requirements

  • Prior knowledge of machine learning required
  • Usage of tools like R and SAS

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