Get Hired: Conquering ML Interview Questions on GitHub

Are you preparing for a machine learning (ML) interview and feeling overwhelmed by the vast array of resources available on GitHub? Look no further! In this comprehensive article, we’ll explore two popular GitHub repositories filled with ML interview questions, tips, and study materials to help you ace your upcoming interviews.

Dive into MLQuestions: A Comprehensive Collection

The first repository we’ll dive into is MLQuestions, a treasure trove of machine learning and computer vision interview questions. This repository is your one-stop-shop for preparing for technical interviews in the ML field.

Key Features of MLQuestions

• Extensive Question Bank: With over 65 questions covering a wide range of ML and computer vision topics, you’ll be well-prepared for any interview scenario.
• Real-World Examples: The questions are based on actual interview experiences, ensuring you’re exposed to the types of problems you’ll encounter in the industry.
• Detailed Answers: Each question is accompanied by a detailed answer, helping you understand the concepts thoroughly and providing a solid foundation for your responses.
• Coding Challenges: In addition to theoretical questions, you’ll find coding challenges to test your practical skills and problem-solving abilities.

Sample Questions from MLQuestions

Here are a few sample questions from the repository to give you a taste of what to expect:

• What’s the trade-off between bias and variance?
• Explain over- and under-fitting, and how to combat them.
• Implement non-maximal suppression as efficiently as you can.
• Given stride S and kernel sizes for each layer of a (1-dimensional) CNN, create a function to compute the receptive field of a particular node in the network.

Embark on the Machine Learning Interview Journey

The second repository we’ll explore is machine-learning-interview, a comprehensive resource created by an experienced software engineer and machine learning expert with offers from top companies like Google, LinkedIn, Coupang, Snap, and StitchFix.

Key Features of machine-learning-interview

• Interview Success Stories: Read inspiring stories from individuals who have secured offers at renowned companies, providing insights into the interview process and preparation strategies.
• Study Guide: Access a well-curated study guide focusing on the essential areas for acing ML interviews, including LeetCode problems, statistics, probability, and deep learning fundamentals.
• ML System Design: Dive into practical ML system design use cases, such as YouTube recommendations, feed ranking, and ad click prediction, to gain hands-on experience with real-world scenarios.
• Coding Practice: Prepare for coding interviews with a categorized list of LeetCode questions tailored specifically for machine learning engineering roles.
• Advanced Topics: Explore advanced topics like Bayesian statistics, big data, and ML in production to set yourself apart from other candidates.

Sample Resources from machine-learning-interview

Here are some valuable resources from the repository:

• ML Fundamentals: Revise key concepts like collinearity, feature scaling, random forests, boosting, logistic regression, and k-means clustering.
• Deep Learning (DL) Fundamentals: Study neural networks, backpropagation, activation functions, loss and optimization, convolutional neural networks, and recurrent neural networks.
• ML System Design Primer: Learn about common ML system design use cases, such as video recommendations and feed ranking.
• LeetCode Questions by Category: Practice SQL, programming, statistics, and probability problems categorized for machine learning engineering roles.

Putting It All Together

By combining the resources from these two GitHub repositories, you’ll have a comprehensive study plan that covers theoretical concepts, practical coding challenges, and real-world ML system design scenarios. With a solid understanding of the fundamentals and exposure to industry-relevant problems, you’ll be well-equipped to tackle any ML interview challenge that comes your way.

Remember, preparation is key to success in ML interviews. Dedicate time to study the materials, practice coding problems, and familiarize yourself with ML system design concepts. Additionally, stay updated with the latest trends and advancements in the field to demonstrate your passion and commitment during the interview process.

Good luck with your ML interview preparations! With the right resources and dedication, you’ll be one step closer to landing your dream job in the exciting world of machine learning.