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ML/AI Interview Prep Guide

$39

Machine learning theory questions, model design scenarios, ML system design, and practical coding challenges for ML roles.

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📁 File Structure 8 files

ml-ai-interview-guide/ ├── LICENSE ├── README.md ├── coding/ │ └── ml_coding_challenges.py ├── questions/ │ ├── deep-learning-and-llms.md │ ├── ml-system-design.md │ ├── ml-theory.md │ └── model-design-scenarios.md └── study-plan/ └── ml-study-plan.md

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ML/AI Interview Prep Guide

85+ interview questions with thorough answers, runnable coding challenges, and ML system design walkthroughs for landing machine learning and AI roles at top tech companies.

Whether you're targeting your first ML engineer position or preparing for a senior/staff ML role, this guide gives you the theory, applied design thinking, deep learning fundamentals, and hands-on coding practice you need — all in portable Markdown and runnable Python.

Table of Contents

  • [What's Included](#whats-included)
  • [How to Use This Guide](#how-to-use-this-guide)
  • [File Index](#file-index)
  • [Theory & Fundamentals](#theory--fundamentals-25-questions)
  • [Model Design Scenarios](#model-design-scenarios-20-questions)
  • [ML System Design](#ml-system-design-15-questions)
  • [Deep Learning & LLMs](#deep-learning--llms-25-questions)
  • [Coding Challenges](#coding-challenges)
  • [Study Plan](#study-plan)
  • [Question Format](#question-format)
  • [FAQ](#faq)
  • [Support](#support)
  • [License](#license)

What's Included

CategoryCountDescription
ML Theory25Bias-variance, regularization, metrics, cross-validation, classic algorithms
Model Design Scenarios20Problem framing, feature engineering, imbalanced data, leakage, validation
ML System Design15End-to-end system designs with ASCII architecture diagrams
Deep Learning & LLMs25Neural nets, CNNs/RNNs, transformers, embeddings, RAG, fine-tuning, eval
Coding Challenges5From-scratch implementations: k-NN, k-means, linear regression, metrics
Study Plan14-week structured prep plan referencing every file in this guide
Total85+ questions + runnable codeEverything you need for ML interviews

How to Use This Guide

For Structured Prep (recommended)

1. Start with the study plan (study-plan/ml-study-plan.md) — it maps out a 4-week schedule

2. Build your theory foundation — work through questions/ml-theory.md first

3. Practice applied thinking — move to questions/model-design-scenarios.md

4. Tackle system design — study questions/ml-system-design.md and practice drawing diagrams

5. Cover deep learning — work through questions/deep-learning-and-llms.md

6. Code from scratch — run coding/ml_coding_challenges.py and implement each algorithm yourself

For Quick Reference

  • Jump to any topic via the [File Index](#file-index) below
  • Each question file is self-contained — no need to read in order
  • Use Ctrl+F / Cmd+F to search for specific topics

For Mock Interviews

1. Pick a random question from any file

... continues with setup instructions, usage examples, and more.

📄 Code Sample .py preview

coding/ml_coding_challenges.py #!/usr/bin/env python3 """ ML Coding Challenges -- From-Scratch Implementations ==================================================== Five classic machine-learning building blocks implemented in pure Python using ONLY the standard library (no NumPy, no scikit-learn). These are the kinds of "implement X from scratch" problems that come up in ML engineering interviews. Implementations: 1. train_test_split -- reproducible data splitting 2. KNNClassifier -- k-nearest-neighbors classification 3. KMeans -- Lloyd's algorithm for clustering 4. LinearRegressionGD -- linear regression via gradient descent 5. classification metrics -- accuracy, precision, recall, F1 Run it: python3 ml_coding_challenges.py Every section has a self-test that prints expected vs. actual and asserts correctness, so a clean run proves all implementations are correct. Requires: Python 3.10+ standard library only. """ from __future__ import annotations import math import random from collections import Counter # --------------------------------------------------------------------------- # 1. Train/test split # --------------------------------------------------------------------------- def train_test_split(X, y, test_size=0.2, seed=None): """Split parallel lists X and y into train/test partitions. Returns (X_train, X_test, y_train, y_test). The split is reproducible when a seed is provided. Shuffling is done on indices so X and y stay aligned. """ if len(X) != len(y): raise ValueError("X and y must have the same length") if not 0.0 < test_size < 1.0: raise ValueError("test_size must be between 0 and 1") n = len(X) indices = list(range(n)) random.Random(seed).shuffle(indices) # ... 277 more lines ...
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