# andrewekhalel/MLQuestions: a Markdown question bank for ML interview prep > MLQuestions is a community-maintained README of 69 machine learning interview questions with answers, plus a separate NLP file with 13 more. It is a reading list with source links, not a runnable project, and its value depends on how you use it. **andrewekhalel/MLQuestions** — Machine Learning and Computer Vision Engineer - Technical Interview Questions - Repository: https://github.com/andrewekhalel/MLQuestions - Stars: 4,913 · Forks: 800 - Language: Python - License: not declared - Published: 2026-09-23 · Updated: 2026-09-23 · Language: en - Canonical page: https://hysenlabs.com/projects/andrewekhalel-mlquestions ## What andrewekhalel/MLQuestions actually is The repository is a Markdown question bank. The README opens by describing itself as "69 machine learning interview questions with answers" spanning ML fundamentals, deep learning, computer vision, NLP, dimensionality reduction, statistics and coding, and says it has been curated and community-maintained since 2018. The top level of the repository contains .github/, .gitignore, NLP/, README.md and scripts/. That is the whole shape of it: one long README, one subfolder with a second README, and a scripts directory. The audience is named explicitly in the README: people preparing for Machine Learning Engineer, Data Scientist, Deep Learning Engineer, Computer Vision Engineer, NLP Engineer and AI or Research Engineer roles. If you are a working engineer brushing up before an interview, or a student building a study plan, that framing fits. If you want a library, a CLI or a service, this is not that. The Python language label reflects the coding questions and the scripts folder, not an installable package. The contents section doubles as a syllabus and as a count. Machine Learning Fundamentals has 7 questions, Algorithms and Ensembles 3, Data Preprocessing and Feature Engineering 4, Optimization and Training 6, Deep Learning and Neural Networks 13, Computer Vision 13, Dimensionality Reduction 7, Model Evaluation and Validation 7, Probability and Statistics 3, Coding and Implementation 6. The separate NLP file holds 13. Those numbers are the README's own, and they are the fastest way to judge coverage before you commit study time. ## How the answers are structured, and where they come from Each question is a Markdown heading, followed by a short answer and one or more source links. The pattern is visible throughout: a question such as "What's the trade-off between bias and variance?" gets a two-sentence explanation and then a link to an external article, in that case a Towards Data Science piece, with a second link to a House of Bots interview-question page underneath. This matters for how you read the file. The answers are summaries, and the links are the actual reading. Some entries carry more prose than others. "Explain over- and under-fitting and how to combat them?" gets a paragraph defining underfitting as insufficient model flexibility and overfitting as memorization of the training data, with a worked illustration (estimating a quadratic with a line versus estimating a line with a tenth-order polynomial). "What is regularization, why do we use it, and give some examples of common methods?" goes further and states a real trade-off: ridge shrinks coefficients toward zero but never to exactly zero, so the final model keeps all predictors, while lasso can force coefficients to exactly zero and therefore performs variable selection. That is the level of detail to expect at the good end. Other entries are thinner. "What's the difference between a generative and discriminative model?" is answered in two sentences and closes with the claim that discriminative models generally outperform generative ones on classification tasks. "What is Turing test?" is three sentences with a source link to an AI interview-question page. The variation in depth is the honest characteristic of this repository: it is a curated index with commentary, not an edited textbook. ## Reading andrewekhalel/MLQuestions for the first time There is nothing to install. The README does not document a package, a build step or a runtime, and the repository has no setup instructions. The intended consumption paths are GitHub itself and the browsable site the README links to at andrewekhalel.github.io/MLQuestions. Two entry points are named in the README. The main question bank is README.md at the repository root. The NLP questions are in NLP/README.md, which the contents list links to as "NLP Interview Questions and Answers". The README also lists a Preparation Resources section and a Contributions section, and the repository has a scripts/ directory, though the README does not document what that directory contains. A sensible first pass is to read the contents list at the top of README.md and pick the two or three sections that match the loop you are preparing for. Each section entry carries its own question count, so you can see before you scroll whether a topic is covered thinly (Algorithms and Ensembles and Probability and Statistics have 3 each) or in depth (Deep Learning and Neural Networks and Computer Vision have 13 each). Then open NLP/README.md separately, because its 13 questions are not in the main file. Because the repository is Markdown only, any text editor or the GitHub file viewer works. There is no index, no tagging and no search beyond what GitHub and the linked website provide, so the contents list is the navigation. ## The coverage gaps you should plan around The README's own contents list is the clearest statement of what is missing. There is no section on ML system design, despite that being a standard interview stage and a common search topic around this kind of material. There is no section on MLOps, deployment, monitoring or data pipelines. Coding and Implementation is 6 questions out of 69, so the coding practice is a small slice, and the answers are prose explanations rather than graded exercises with test cases. The NLP questions are in a separate file rather than in the main README, which means a reader who only opens the top-level file will miss 13 questions entirely. The counts also vary sharply by topic: Algorithms and Ensembles has 3 questions and Probability and Statistics has 3, while Deep Learning and Computer Vision have 13 each. If your loop leans on classical algorithms or statistics, the density here is low. There is a second limitation that is structural rather than topical. Because most answers are short summaries pointing at external links, the repository inherits the link rot of everything it cites. Nothing in the repository pins or archives those sources. A question whose answer is a single link is only as good as that link on the day you read it. ## How it compares with a practice-first alternative The natural alternative for coding-heavy preparation is a practice platform such as LeetCode or a curated problem set like NeetCode, which the related search terms around this repository keep mentioning. The difference in approach is fundamental. MLQuestions is read-only: you open a Markdown file, read a question, read a two-sentence answer, and follow a link. A practice platform gives you a problem statement, an editor, hidden test cases and a verdict. That means they fail in opposite directions. MLQuestions can cover conceptual breadth cheaply, because prose is fast to write and fast to skim, and it can include topics no judge can grade, such as the bias-variance trade-off or the difference between generative and discriminative models. A judge-based platform cannot ask those questions at all. Conversely, MLQuestions cannot tell you whether your implementation is correct, because there is nothing to run. The README's Coding and Implementation section is 6 questions of explanation, not 6 exercises. A second comparison is against the source articles themselves. Because MLQuestions aggregates links to Towards Data Science, Toptal, Springboard, Intellipaat and House of Bots, you could skip the repository and read those directly. What the repository adds is ordering by interview topic and a single place to scan. That is a real convenience and a thin one. If you already know which topics you are weak on, the aggregation saves you little. ## Maintenance, licence and the cost of keeping it current The repository is not archived, and the last push was on 2026-09-21. The README describes the collection as community-maintained since 2018 and includes a Contributions section, and the repository has a .github/ directory, which is where contribution templates and workflows conventionally live. There are no releases, so there is no versioned artifact to upgrade and no changelog to track. Upgrading means pulling the master branch again. The practical maintenance question is not code drift but content drift. Interview questions age as tooling and emphasis change, and this repository's answers are short enough that a stale answer is hard to spot from the heading alone. The scripts/ directory exists but the README does not document what it does, so a reader cannot tell from the README whether any part of the content is generated or checked automatically. The licence is the item to resolve before you reuse anything. The repository metadata does not state a licence, and the README does not carry a licence section, so the terms under which the text can be copied, modified or redistributed are not established by what the repository publishes. The answers also quote and paraphrase third-party articles and link out to them, and those sources carry their own terms. If you plan to fork this into internal training material or a commercial product, treat the licence question as open and get it answered rather than assuming permissive reuse. ## Conclusion Adopt MLQuestions if you want a free, topic-sorted checklist of ML interview questions to skim before a loop, and treat its answers as pointers to the linked sources rather than as a study text. Skip it if you need runnable coding exercises, system design material or a maintained answer key: the repository is a README plus an NLP folder, the licence is not stated in the repository metadata, and the last push was on 2026-09-21. Before relying on it, open README.md and NLP/README.md on the master branch, count the questions in the sections you actually care about, and check whether the linked sources still resolve. ## FAQ ### What is asked in an ML interview, according to MLQuestions? The README organizes its 69 questions into Machine Learning Fundamentals, Algorithms and Ensembles, Data Preprocessing and Feature Engineering, Optimization and Training, Deep Learning and Neural Networks, Computer Vision, Dimensionality Reduction, Model Evaluation and Validation, Probability and Statistics, and Coding and Implementation, with 13 further NLP questions in a separate file. ### What is machine learning ML mcq, and does MLQuestions cover it? The repository is not a multiple-choice question set. It presents its 69 questions as Markdown headings with short written answers and links to external articles, so there are no answer options or scoring. ### What is ML coding, as covered by MLQuestions? The repository has a Coding and Implementation section with 6 questions, and the README lists Coding and Implementation among its topics. The answers are written explanations rather than runnable exercises, so there is no editor or test harness in the repository. ### How does machine learning work, and does MLQuestions explain it? The repository's Machine Learning Fundamentals section covers related ground, including the bias-variance trade-off, overfitting and underfitting, regularization, and the differences between supervised, unsupervised and reinforcement learning, with links to external articles for each. ## Sources - [andrewekhalel/MLQuestions on GitHub](https://github.com/andrewekhalel/MLQuestions) - [Issues](https://github.com/andrewekhalel/MLQuestions/issues) - [README](https://github.com/andrewekhalel/MLQuestions/blob/master/README.md) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/andrewekhalel-mlquestions