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Advanced Machine Learning Engineer - Python Mock Interview

To help you prepare for your Advanced Machine Learning Engineer - Python interview, here are 16 interview questions and answer examples.

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Question 1 of 16

Have you used Tensorflow? Why is tensorflow useful for machine learning?

This question shows the developer's knowledge of machine learning algorithms.

Tensor flow is an open-source machine learning library developed by Google. It is used by many machine learning engineers as it provides incredibly powerful libraries that can be used to build and analyze a vast variety of machine learning algorithms.

Tensorflow is compatible with both javascript and python making it versatile for engineers to create machine learning models for desktop, mobile, web, and cloud.

It is advised to install TensorFlow on a virtual machine as it is a relatively large package.

The technical interviewer may ask this as a "starter" question in order to open a discussion about machine learning algorithms.

Written by on July 6th, 2021

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16 Advanced Machine Learning Engineer - Python Interview Questions & Answers

Below is a list of our Advanced Machine Learning Engineer - Python interview questions. Click on any interview question to view our answer advice and answer examples. You may view six answer examples before our paywall loads. Afterwards, you'll be asked to upgrade to view the rest of our answers.

  • 1. Have you used Tensorflow? Why is tensorflow useful for machine learning?

  • 2. What is syntactic analysis and semantic analysis in the field of Natural Language Processing?

  • 3. What is the 'bag-of-words' algorithm?

  • 4. Demonstrate the 'bag of words' model and state the benefits and limitations of using this model.

  • 5. Write a script to demonstrate stemming.

  • 6. Write a script to demonstrate lemmatization.

  • 7. Explain 'look ahead bias'.

  • 8. Data cleaning - describe how would you 'clean' a dataset?

  • 9. How would you analyze the quality of a dataset ?

  • 10. Demonstrate how to normalize a data set.

  • 11. Explain how you would analyze the performance of a machine learning algorithm.

  • 12. Suggest how you would increase the performance of a machine learning algorithm.

  • 13. Explain a scenario where you used a machine learning algorithm? And why was it necessary to use machine learning?

  • 14. Demonstrate a random forest.

  • 15. I have a data set containing images of cats and dogs. Suggest an algorithm that can be used to classify these images

  • 16. What would you ask an advanced AI oracle/chat bot?