Explore the potential of a supervised learning approach in detecting credit card fraud with machine learning algorithms and understand how it can enhance fraud detection accuracy and minimize financial losses.
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Next Demo Starts In
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Seconds
Don't Want to Wait?
Expert-Taught Videos
Outcome Focus
Auto-Graded Assessments
Guided Hands-On Exercises
Mentorship
Real-Time Insights
Peer-Based Learning
of live instructor-led sessions
On-demand self-paced learning
400+ hours hands-on
Guided hands-on exercises
1. Fraud Detection and how ML can improve the accuracy of systems
2. Steps involved in preprocessing credit card transaction data
3. How to build a ML model using supervised learning techniques
4. Implementing ML model in real-time to detect potential fraud
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8+ years of experience
Machine Learning
Analytics
Pyspark, Python
Akash is an experienced Data Analytics professional with over 8 years of industry experience. He is skilled in developing machine learning models, statistical analysis, and visualizations, and proficient in utilizing tools such as Python.
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In this demo, we follow this process to give you a solid understanding of the topic.
Learn how ML can improve the accuracy of systems
Work on exercises while attending the demo
on implementing ML models real-time to
Learn more about our hands-on industry-validated curriculum
Get all you doubts cleared in the interactive session
By attending and completing the Demo, you can expect to learn about a supervised learning approach that reduces false positives and minimizes losses due to fraud.
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