Comprehensive resources for machine learning and data science interviews
A collection of guides, checklists and resources for those preparing for machine learning, data science, and statistics interviews. What you need to know before applying to ML positions.
Key statistical concepts for ML interviews: probability, distributions, hypothesis testing, A/B testing, Bayesian statistics, and common statistical pitfalls.
Core machine learning algorithms and concepts: supervised vs unsupervised, model evaluation, bias-variance tradeoff, regularization, and gradient descent variations.
Data structures, algorithms, and Python proficiency for technical interviews. Includes common patterns in ML coding questions and data manipulation challenges.
How to approach ML system design interviews: requirement gathering, feature engineering, model selection, evaluation strategies, and production considerations.
Neural network architectures, optimization algorithms, backpropagation, CNNs, RNNs, transformers, and common practices in training deep models.
Walkthrough of real-world ML problems and how to solve them in interviews. Examples from recommendation systems, anomaly detection, and forecasting.
These resources are based on my experience preparing for and conducting ML interviews. I hope they help you navigate the challenging landscape of technical interviews in the field.
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