Job Interview Experience For The Role Of Data Scientist at FISERV
1. How would you check if the model is suffering from multi Collinearity?
2. What is transfer learning? Steps you would take to perform transfer learning.
3. Why is CNN architecture suitable for image classification? Not an RNN?
4. What are the approaches for solving class imbalance problem?
5. When sampling what types of biases can be inflected? How to control the biases?
6. Explain concepts of epoch, batch, iteration in machine learning.
7. What type of performance metrics would you choose to evaluate the different classification models and why?
8. What are some of the types of activation functions and specifically when to use them?
9. What is the difference between Batch and Stochastic Gradient Descent?
10. What is difference between K-NN and K-Means clustering?
11. How to handle missing data? What imputation techniques can be used?
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