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ML Knowledge

6 months ago

Which techniques did you apply to address multicollinearity in your data?

Machine Learning Engineer

Amazon

Comcast

ZocDoc

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6 months ago

ML Knowledge

6 months ago

Could you define log loss in the context of model evaluation?

Machine Learning EngineerData Scientist

Amazon

Grubhub

Roku

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6 months ago

ML Knowledge

6 months ago

Could you showcase your skills in creating a KNN classifier from scratch?

Machine Learning Engineer

Amazon

Honeywell

WorldRemit

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6 months ago

ML Knowledge

7 months ago

When working on deep learning models, how do you decide whether to use Rectified Linear Unit or another activation function?

Machine Learning Engineer

Amazon

Stripe

Mapbox

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7 months ago

ML Knowledge

7 months ago

Can you walk me through the concept of bias and variance in machine learning, and offer a practical illustration of how this tradeoff can be apparent in a model's performance?

Machine Learning Engineer

Amazon

Bloomberg

Pluralsight

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7 months ago

ML Knowledge

7 months ago

Can you provide some insight into XGBoosting and how it can be applied?

Machine Learning Engineer

Amazon

DocuSign

Boeing

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7 months ago

ML Knowledge

7 months ago

Can you explain the contrast between bagging and boosting? And what effects do they have on bias and variance?

Machine Learning Engineer

Amazon

Deliveroo

Patreon

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7 months ago

ML Knowledge

7 months ago

How would you illustrate the concept of a minimum height tree and detail the construction process?

Machine Learning Engineer

Amazon Web Services

Amazon

Benchling

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7 months ago

ML Knowledge

8 months ago

Could you elaborate on cross-validation and how it's utilized in the field of machine learning?

Machine Learning Engineer

Amazon

Nokia

DoorDash

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8 months ago

ML Knowledge

8 months ago

How do k-means clustering and k-nearest neighbors algorithm differ in machine learning?

Machine Learning Engineer

Amazon Web Services

Amazon

Twitch

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8 months ago

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^*All interview questions are submitted by recent Amazon Machine Learning Engineer candidates, labelled and categorized by Prepfully, and then published after being verified by Machine Learning Engineers at Amazon.

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