Ace your machine learning interviews with Exponent’s ML engineer interview course: https://bit.ly/3wARKQb

This video features an interview where an ML Engineer from Snapchat implements the k-nearest neighbors algorithm. The interviewer and candidate discuss the algorithm's concepts, including distance metrics and binary classification. The candidate outlines their approach, codes the solution in Python with NumPy, and tests the code with different examples and they collaboratively address a minor debugging issue.

Chapters
00:00 - Introduction to K-Nearest Neighbors (KNN) Algorithm Implementation
04:22 - Understanding K and N in KNN Inference Function
06:43 - Calculating Distances Between Data Points with Python
10:56 - KNN Inference Using Euclidean Distance
16:40 - Demonstrating Distance Calculations with NumPy Arrays
20:13 - Handling Errors in Results
23:30 - Code Verification for Euclidean Distance
25:11 - Enhancing Distance Functions with Python Dictionaries
29:59 - Overview of Algorithm Implementation and Debugging Techniques

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