amitshekhariitbhu/machine-learning-interview-questions: a Markdown index of ML interview questions and answers

What this repository actually contains

This is a Markdown question bank, not a course and not a book. The README opens with a banner image, a one-line description calling itself a cheat sheet for machine learning interviews, and a list of target roles: AI Engineer, Gen AI Engineer, MLOps Engineer, Machine Learning Engineer, Data Scientist and Deep Learning Engineer. The table of contents then splits the material into fifteen sections, from AI Engineering and Fundamentals of Machine Learning through Algorithms, Data Preprocessing and Feature Engineering, 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.

The structure is the point. If you are preparing for a machine learning interview and you do not know what you do not know, a flat list of questions grouped by topic is a reasonable starting map. The repository is prepared and maintained by Amit Shekhar, described in the README as the founder of Outcome School, and it is licensed Apache-2.0. The top level of the repository holds only .gitattributes, LICENSE, README.md and an assets directory, so the entire content lives in that single README file.

How the question and answer format works in practice

Each entry is a bullet with a question, and in many cases a nested bullet that points to an answer somewhere else. A typical pair looks like this in the README: the question "What is Regularization? Explain L1 (Lasso) and L2 (Ridge) regularization." is followed by an answer link to outcomeschool.com/blog/regularization-in-machine-learning. Other answers point to YouTube videos, LinkedIn posts and X posts. The AI Engineering section goes further and links out to a separate repository, ai-engineering-interview-questions, for the full set of AI engineering questions.

That design has a clear consequence. The repository is an index, and the answers live on the surfaces of a training business. There is no local answer text to read in the repository itself for most questions. Several entries have no answer link at all: in the Fundamentals section, "What is anomaly detection?", "What is the difference between policy-based and value-based methods?", "What is Q-Learning?", "Explain the concept of exploration vs exploitation." and "Explain the curse of dimensionality and how to address it." appear as bare bullets. The same is true across the Algorithms section, where questions about Decision Trees, Random Forest, ensemble methods, bagging versus boosting, Gradient Boosting, XGBoost and multicollinearity are listed without a linked answer. The README does state that the project "will keep updating this with new questions and answers", so the bare bullets read as a backlog rather than an oversight, but the current state is what you study from.

Getting the question list onto your machine

There is no package, no build step and no runtime. The README gives no installation instructions because there is nothing to install. The way to use it is to open the repository page on GitHub and read README.md there, or download the repository and read the same file locally. The repository layout confirms this: the only content files are README.md, LICENSE, .gitattributes and the assets folder holding the banner image.

The README documents no clone command, no install command and no configuration key, so there is no project-specific command to copy here. What the README does give is the internal navigation: the table of contents links jump to each of the fifteen sections, and the answer links take you to outcomeschool.com, YouTube, LinkedIn or X. If you read the file on GitHub, those links are clickable in the rendered view; in a plain text editor you see the raw URLs instead.

The one thing worth knowing before you start is that the AI Engineering section is a pointer rather than a full section. It lists six terms (LLM, RAG, MCP, Agent, Fine-tuning, Quantization), links a single video covering them, and then sends you to the separate ai-engineering-interview-questions repository for the complete question set. So a reader who only needs AI engineering material will end up in a second repository, and the README says so explicitly rather than leaving you to discover it.

Where the repository stops being enough

The biggest limitation is that the repository does not teach. A question like "Explain Gradient Boosting and its advantages over Random Forests" with no linked answer is a prompt, not a resource. You still have to produce the explanation yourself or find it elsewhere, and the list gives you no way to check whether your explanation is complete. For a candidate who already knows the material and wants a coverage checklist, that is fine. For someone learning the material for the first time, the repository is a table of contents for a book that has not been written in one place.

The second constraint is link rot and platform dependency. Answers are spread across outcomeschool.com, youtube.com, linkedin.com and x.com. Those are four different surfaces with four different access models, and LinkedIn and X in particular are awkward to read without an account. A study session that depends on opening a dozen tabs is fragile, and there is no local copy to fall back on. The README does not document any offline export, PDF build or archive of the answers.

A third gap is depth calibration. The list mixes fundamentals ("What is Machine Learning?") with specialised material ("Explain Local Loss, Focal Loss, and Gradient Blending in the context of Multi-Task Learning") and system design topics, but it does not mark which questions belong to which seniority level. The README names roles, not levels, so a fresher and a staff engineer see the same list.

How it compares with a self-contained question bank

The obvious alternative is a question bank that keeps the answers in the same file, such as a community-maintained Markdown or PDF collection where each question is followed by a written answer. The difference is not the questions, which overlap heavily across such projects, but the reading model. A self-contained bank lets you study offline, diff versions, and search answers with the same text search you used on the questions. This repository optimises for a different thing: it stays short, it points at video and blog explanations that the author has produced, and it keeps the maintenance burden low because adding a question costs one line.

That trade-off is defensible for the author and awkward for the reader. If your goal is to revise on a train with no signal, a self-contained bank wins. If your goal is to find out which topics an interviewer might raise and then watch a short explanation of each, the link-first model is faster to scan because the README stays readable in one pass. The AI Engineering section shows the model at its most useful: a short list of terms with a single video link covering all of them, plus a pointer to a dedicated repository. That is a routing decision, and it is honest about being one.

Maintenance, licence and the cost of following along

The repository is not archived and the last push was on 2026-09-05, so it is current. There are no retrieved releases, which is expected for a Markdown-only project: versioning happens in commits to README.md, and there is no changelog to read. Upgrading means pulling the latest main branch and re-reading the file, which costs nothing but also tells you nothing about what changed unless you look at the commit history.

The licence is Apache-2.0, which permits commercial and private use, modification and redistribution provided the licence and notices are preserved and modified files are marked. For a question list that you might paste into internal onboarding notes, that is permissive. It does not, however, cover the linked answers. The blog posts, videos and social posts that the README points to are separate works under their own terms, and the Apache-2.0 grant on this repository does not extend to them. If you plan to redistribute the questions inside a company wiki, keep the attribution and keep the links pointing outward rather than copying answer text you do not have rights to. This is a description of the licence text, not legal advice.

The maintenance cost that matters is not yours, it is the author's: every bare bullet in the Algorithms and Fundamentals sections is a promise the README makes about future work, and the value of the repository depends on those links eventually appearing.

Editorial conclusion

Use this repository if you want a broad, topic-organised checklist of machine learning interview questions and you are willing to open the linked Outcome School, YouTube, LinkedIn or X pages for the actual answers. Skip it if you need an offline, self-contained answer bank, a PDF-style revision document, or anything you can study without a browser. Before relying on it, clone the repo and check how many of the questions in your target section, such as Large Language Model or System Design and MLOps, actually carry an answer link rather than a bare bullet, because that ratio is what determines whether the list saves you time or just tells you what you do not know.

Frequently asked questions

Official sources

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