What is mlquestions?

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.