ML Interview Mastery: 101 Questions to Sharpen Your Skills

More than just an interview preparation guide—this is your hands‑on roadmap to mastering machine learning. Crafted by a former Stanford researcher, and a ML‑consulting founder, it blends rigorous theory with real‑world practice.

Dual Purpose:

Interview Preparation: 101 core questions with bullet‑proof answers, covering everything from the “dying ReLU” problem to empirical scaling laws.

Skill Deepening: Your indispensable ML reference, packed with hard‑won insights from years of real‑world experience you won’t find in university courses or books.

Sample Questions You’ll Conquer

• Beginner: How do you address class-imbalance in a dataset?
• Beginner: What is automatic differentiation (AD)? Name three popular AD frameworks.
• Medium: What is inductive bias in machine learning, and how can you leverage it to improve model performance?
• Medium: If you can only train on K ≪ M samples from a dataset of size M, how would you select those K examples?
• Expert: Describe two approaches for continual learning that avoid catastrophic forgetting.
• Expert: How can you constrain an MLP to represent only convex functions?
  

Level up your ML career and solidify your foundation.

👉 Enroll now and transform from candidate to confident practitioner!