Fight Fraud with Machine Learning (MEAP V06) + Code

In Fight Fraud with Machine Learning you'll learn how to
Detect phishing, card fraud, bots, and more
Fraud data analysis using Python tools
Build and evaluate machine learning models
Vision transformers and graph CNNs
In this cutting-edge book you'll develop scalable and tunable models that can spot and stop fraudulent activity in online transactions, data stores, even in digitized paper records. You'll use Python to battle common scams like phishing and credit card fraud, along with new and emerging threats like voice spoofing and deepfakes.
about the book
Fight Fraud with Machine Learning teaches you to build and deploy state-of-the-art fraud detection systems. You'll start with the basics of rule-based systems, iterating chapter-by-chapter until you're creating tools to stop the most sophisticated modern attacks. Almost every online fraud you might encounter is covered in detail.
Examples and exercises help you practice identifying credit card fraud with logistic regression, using decision trees and random forests to identify fraudulent online transactions, and detecting fake insurance claims through gradient boosted trees. You'll deploy neural networks to tackle Know Your Customer fraud, spot social network bots, catch deepfakes, and more! Plus, you'll even dive into the latest research papers to discover powerful deep learning techniques such as vision transformers.
about the reader
For fraud detection product managers, data scientists, and machine learning engineers confident with Python programming.
about the author
Ashish Ranjan Jha has worked for large technology companies like Oracle and Sony, as well as tech unicorns such as Revolut and Tractable. He has a decade of working experience in the field of Machine Learning using Python.


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