andrewekhalel/mlquestions

★ 4,632Audience · developerComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((mlquestions))
    What it does
      Interview prep guide
      Questions with answers
      NLP section
    Topics covered
      Bias and variance
      Gradient descent
      Neural networks
      Computer vision
    Use cases
      Job interview prep
      Self-study quiz
      Skill review
    Audience
      ML engineers
      CV engineers
      Job seekers

mindmap root((mlquestions)) What it does Interview prep guide Questions with answers NLP section Topics covered Bias and variance Gradient descent Neural networks Computer vision Use cases Job interview prep Self-study quiz Skill review Audience ML engineers CV engineers Job seekers

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Things people build with this

USE CASE 1

Prepare for machine learning and computer vision technical interviews at tech companies.

USE CASE 2

Self-quiz on core ML concepts like bias-variance tradeoff, overfitting, and gradient descent.

USE CASE 3

Study neural network and NLP topics using a structured list of questions and written answers.

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

This repository is a curated collection of technical interview questions for people applying to machine learning and computer vision engineering roles. If you are preparing for a job interview at a tech company and the role involves building or working with AI systems, the questions here are the kind you might face in a technical screening or on-site interview round. The questions cover core machine learning concepts at varying levels of depth. Early questions deal with foundational ideas: the trade-off between bias and variance in a model, what gradient descent does, how overfitting and underfitting happen and how to address them, and how to handle datasets with too many features. Later questions go deeper into specific techniques such as neural networks, convolutional networks used in image processing, and natural language processing. Each question typically includes a written answer, sometimes with links to longer articles for further reading. A recently added section covers natural language processing interview questions as a separate topic within the same repository. The project lists a few recommended books and courses for interview preparation alongside the questions themselves, including titles on statistics and machine learning fundamentals. The repository contains no runnable code and no software to install. It is a study guide in document form, organized as a long list of questions and answers. Anyone preparing for a machine learning role can read through it, use it as a self-quiz, or bookmark specific questions for review. The project is maintained by a single contributor and continues to receive updates as new questions are added. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

Quiz me on machine learning interview questions covering bias-variance tradeoff, overfitting, and feature selection, and give me detailed answers.

Prompt 2

Explain gradient descent step by step as if I am preparing for a technical screening interview at a big tech company.

Prompt 3

Give me 5 challenging computer vision interview questions with answers, at the depth expected in an on-site engineering interview.

Prompt 4

Help me review NLP interview concepts including common model types and evaluation metrics so I can answer technical questions confidently.

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