Machine Learning Engineer Mock Interview for Meta with ChatGPT

Overview

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Experience a simulated Machine Learning Engineer interview for Meta (Facebook) using ChatGPT in this 41-minute video. Observe as common interview questions are posed to the AI, covering topics from general machine learning concepts to ML system design and coding in Python and PyTorch. Gain insights into how AI handles questions about specific projects, data preprocessing, model evaluation, precision-recall trade-offs, and feature engineering. Watch as ChatGPT attempts to generate code for classification datasets and neural networks, and designs a restaurant recommendation system. Learn about overfitting causes and solutions, and hear the presenter's thoughts on using ChatGPT for Machine Learning Engineer preparation.

Syllabus

- Intro
- ChatGPT Overview
- Interview Start
- Can you explain a specific machine learning project you have worked on?
- How do you handle missing or incomplete data in a machine learning project?
- How do you evaluate the performance of a machine learning model?
- Can you discuss a situation where you had to balance precision and recall in a model?
- What feature engineering techniques have you used and why?
- Write code in Python to generate a classification dataset with 3 classes and 5 features. You should be able to use the features to classify the examples.
- Use the dataset to train a 2-layer Neural Network with PyTorch
- Design a restaurant recommendation system
- You found that your system is overfitting. What might be the cause of that? What can you do to fix it?
- Will I recommend ChatGPT for Machine Learning Engineer?
- Giving ChatGPT feedback about the interview