Explain linear regression and Transformer fundamentals
Company: Imc
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Onsite
Overview: This question evaluates core competencies in statistical modeling and deep learning architecture, specifically linear regression (optimization objective, estimation and interpretability under common failure modes) and Transformer fundamentals (self-attention mechanics, positional encodings, multi-head computation and long-sequence scaling trade-offs). It is commonly asked in Machine Learning interviews for Data Scientist roles to probe foundational understanding of modeling assumptions, probabilistic interpretation, model interpretability and algorithmic complexity; domain: Machine Learning; level: primarily conceptual understanding with practical-application reasoning.