This comprehensive guide covers 50 essential Machine Learning interview questions that every ML engineer should know. These questions cover fundamental algorithms, model evaluation, feature engineering, optimization, and practical ML concepts commonly asked in technical interviews.

## Core ML Algorithms

Understanding core machine learning algorithms is essential. These questions test your knowledge of linear regression, logistic regression, decision trees, random forests, and ensemble methods.

## Model Evaluation & Metrics

Proper evaluation is crucial for ML models. Master these questions to demonstrate your understanding of accuracy, precision, recall, ROC curves, cross-validation, and model selection.

## Feature Engineering

Feature engineering is often more important than algorithm choice. These questions cover feature selection, transformation, encoding, handling missing values, and feature scaling techniques.

## Optimization & Training

Understanding how models learn is key. These questions cover gradient descent variants, learning rates, regularization techniques, and optimization algorithms used in ML.

## Advanced ML Concepts

Advanced topics include bias-variance tradeoff, overfitting prevention, ensemble methods, and production ML considerations. These questions test your deep understanding of ML principles.

Comprehensive collection of 50 essential Machine Learning interview questions covering algorithms, model evaluation, feature engineering, and ML best practices. Free ML, Machine Learning interview questions with answers. ML AI interview prep guide.

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