Machine Learning Interview Questions & Answers
Machine Learning Interview Online Test
1. Explain the difference between a classification problem and a regression problem
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2. What is the purpose of the support vector machine (SVM) algorithm
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3. Describe the concept of regularization and its types
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4. What is cross-validation and why is it used
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5. Explain the difference between bagging and boosting
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6. What is the purpose of feature engineering in machine learning
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7. Describe the concept of gradient descent and its variants
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8. What is the difference between a generative model and a discriminative model
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9. Explain the concept of a confusion matrix and its components
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10. What is the purpose of using dropout in neural networks
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11. Describe the concept of hyperparameter tuning and its importance
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12. What are some common metrics for evaluating regression models
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13. Explain the concept of Principal Component Analysis (PCA) and its use
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14. What is the role of activation functions in neural networks
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15. Describe how k-Nearest Neighbors (k-NN) algorithm works
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16. What is the purpose of feature scaling and its methods
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17. Explain the concept of ensemble learning and provide examples
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18. What is the role of model evaluation metrics like F1 score
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19. Describe the difference between L1 and L2 regularization
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20. What is the purpose of data augmentation in machine learning
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21. Explain the difference between online and offline learning
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22. What is the importance of feature selection in machine learning
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23. Describe how Support Vector Machines (SVM) can handle non-linearly separable data
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24. What is clustering and what are some common clustering algorithms
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25. Explain the concept of cross-entropy loss function and its use
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26. What is the significance of the ROC curve in binary classification
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