Amazon Data Scientist Mock Interview - Fraud Model

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====== ✅ Details ======

🤔 Try these machine learning questions asked in Amazon's data science interviews:

"Q1 - What is the variance and bias trade-off?"

"Q2 - What's the difference between boosting and bagging?"

"Q3 - How would you detect seller fraud on Amazon?"

This is a mock interview session covering machine learning questions asked in Amazon's data science interviews. The interviewer was a data scientist at Google and PayPal. The interviewee is a candidate preparing for data science interviews at FAANG companies.

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====== ⏱️ Timestamps ======

0:00 Intro
00:55 Variance & Bias Trade-Off
03:47 Boosting vs Bagging
06:33 Seller Fraud Modeling
26:24 Assessment

====== 📚 Other Useful Contents ======

1. Principles and Frameworks of Product Metrics | YouTube Case Study

Link: https://medium.com/datainterview/principles-and-frameworks-of-product-metrics-youtube-case-study-ff63257a82d3

2. How to Crack the Data Scientist Case Interview

Link: https://medium.com/datainterview/crack-the-data-scientist-case-interview-by-an-ex-google-data-scientist-f44da750cffe

3. How to Crack the Amazon Data Scientist Interview

Link: https://medium.com/datainterview/crack-the-amazon-data-scientist-interviews-ex-faang-data-scientist-78189a5a689e

====== Connect ======

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