Machine Learning Interview Questions: A Link Index, Not a Textbook

What the Repository Actually Contains

The repository is Markdown. There is no code, no test suite, no build step and no dataset. Its content is a Table of Contents with fifteen topic areas, from AI Engineering and Fundamentals of Machine Learning through Algorithms, Optimization, Deep Learning, NLP, Computer Vision, Large Language Model, Model Evaluation, System Design and MLOps, Probability and Statistics, Coding, Maths, and Behavioral and Scenario-Based Questions. Under each heading sit bullet points, each one a question, and beneath many of them a single line beginning with the word Answer followed by a URL. The README names the intended audience directly: AI Engineer, Gen AI Engineer, MLOps Engineer, Machine Learning Engineer, Data Scientist and Deep Learning Engineer. The repository is maintained by Amit Shekhar, described in the README as the founder of Outcome School, and it is licensed Apache-2.0. The README also states that the project will keep being updated with new questions and answers, which tells you the shape of maintenance: an append-only list rather than a released artifact.

The Answer Links Point Away From GitHub

This is the design decision that determines whether the repository is useful to you. The answers are not written into the Markdown. They are hosted on outcomeschool.com blog posts, on YouTube, on X/Twitter threads and on LinkedIn posts. A question such as What is Machine Learning? resolves to a blog URL; Epoch, Batch, Batch Size, Iteration resolves to a YouTube video; Classification Vs Regression resolves to a LinkedIn post. That means the repository is an index and a syllabus, not a reference. It also means the reading experience depends on platforms you do not control. LinkedIn posts are not stable documents, video answers cannot be skimmed or diffed, and a blog post that moves breaks the link silently because nothing in the repository validates the URLs. If you are assembling a study plan, treat the README as the outline and the external pages as the content. If you expected a self-contained answer key, you will be disappointed within the first ten bullets.

Coverage Is Uneven Across the Fifteen Sections

Open the README and compare two sections. Fundamentals of Machine Learning is dense with Answer links: embeddings, Softmax, supervised versus unsupervised learning, reinforcement learning, bias, overfitting and underfitting, L1 and L2 loss functions, regularization, dropout, perceptron, multilayer perceptron, cross-entropy, logits, cross-validation, precision and recall, contrastive learning, generative AI. Almost every question there has somewhere to go. Algorithms is the opposite. Decision trees, random forest, ensemble methods, bagging versus boosting, gradient boosting, XGBoost hyperparameters, R-squared and adjusted R-squared, multicollinearity: the questions are listed, and in the portion of the README available most of them carry no Answer line at all. Only Linear Regression versus Logistic Regression has a link in that section. The same pattern appears in the truncated tail of the file. A reader scanning the Table of Contents will assume uniform coverage because the formatting is uniform, and that assumption is wrong. Count the Answer lines in the section you care about before you commit study time to it.

Getting a Local Copy and Reading It

There is nothing to install. The repository is a Markdown file plus an assets directory holding the banner image referenced at the top of the README. To work with it locally, clone it and open the README in any Markdown viewer or editor. If you want to search the question list without a browser, a plain text search over the file is enough, since every question is a bullet line. The only configuration surface is the content itself: headings, bullets and the Answer lines. There are no environment variables, no package manifest, no CI configuration visible in the supplied material, and no releases have been published, so there is no version number to pin. That absence matters more than it sounds. You cannot say I am studying the March 2026 edition of this list, because no such edition exists. The default branch is main and the last push recorded is 2026-09-05, which is the only timestamp you get.

Where This Fails as a Study Resource

The failure mode is link rot combined with uneven coverage. Because answers live on third-party platforms, the repository can remain perfectly formatted while becoming progressively less useful: the question list stays intact, the destinations decay. A second limitation is depth. A question like Explain Local Loss, Focal Loss, and Gradient Blending in the context of Multi-Task Learning, or Explain the curse of dimensionality and how to address it, is a topic that needs several paragraphs of derivation to be interview-ready. Whether the linked page supplies that depth is something you can only judge by opening it, and the repository gives no indication of answer length, difficulty or recency. Third, the repository is not a substitute for practice. There is no question bank with hidden answers, no spaced repetition data, no self-test mode. It is a list you read, and reading a list is not the same as rehearsing an answer out loud. Finally, the AI Engineering section is largely a pointer to a separate repository, ai-engineering-interview-questions, so if that is your target role, this repository is a signpost rather than the destination.

How It Differs From Chip Huyen's Machine Learning Interviews Book

The obvious alternative for interview preparation is Chip Huyen's Machine Learning Interviews Book, which is a long-form text hosted as a website and repository, with chapters that explain the reasoning behind question categories and include worked discussions. The difference in approach is structural. Huyen's book argues a position about how ML interviews are designed and what interviewers are looking for, and the prose carries the argument. This repository does not argue anything. It enumerates. That makes it faster to scan and easier to use as a checklist, and considerably thinner as an explanation of why a topic appears in interviews at all. If you already know the material and want to confirm you have not missed a topic, the enumeration wins. If you are learning the material for the first time, a book that explains the reasoning will carry you further than a list of links, even though the list is better organised for a quick gap check.

Licence and Maintenance Cost

The repository is Apache-2.0. That permits commercial and private use, modification and redistribution, provided you keep the licence and notice files and state significant changes; it also includes an explicit patent grant. For a Markdown question list this is generous, and it means you can fork it into an internal onboarding document without asking anyone. What the licence does not cover is the linked content. The blog posts, videos and social posts the Answer lines point to are separate works under their own terms, and Apache-2.0 on this repository grants you nothing over them. If you fork the list and strip the links, you have an Apache-2.0 topic outline and no answers. Maintenance cost is low in the sense that there is no build to break, and high in the sense that the value of the repository is entirely in link accuracy, which no automation in the supplied material appears to check. The README's closing note that new questions and answers will keep being added suggests the list grows, but growth does not repair a dead link.

Who Should Clone This and Who Should Not

Clone it if you are preparing for a machine learning, deep learning or MLOps interview and you want a topic checklist organised by area rather than by difficulty, and you are comfortable reading answers on YouTube, LinkedIn and a blog. It is also a reasonable starting point for someone building an internal study group syllabus, since the Apache-2.0 terms let you fork and edit the outline. Do not clone it expecting a self-contained reference, and do not adopt it as the primary text for a course, because the answers are not in the repository and the external destinations can change without notice. The specific thing to verify before you invest time is the density of Answer lines in your target section: open the README, pick the two or three sections that match the role you are applying for, and count how many bullets have a link. If your section looks like Algorithms, where most questions have no destination, this repository is a prompt to go find the material elsewhere, not the material itself.

Editorial conclusion

Use this repository if you want a single index of machine learning interview topics and you are willing to follow links off GitHub to read the actual answers. Do not use it if you need self-contained explanations, offline study material, or a versioned answer set you can cite in a course or onboarding document, because most answers live on external sites and the README carries no version history. Before relying on it, open the Table of Contents and check how many of the questions in your target area actually have a link attached, since several entries in the README list only the question.

Official sources

1. amitshekhariitbhu/machine-learning-interview-questions on GitHub
2. Issues
3. License: Apache-2.0
4. Project website
5. README