Preparing for roles like Data Scientist, ML Engineer, Research Engineer, and Research Scientist is a rigorous process. One must prepare for leetcode coding rounds, system designing, read through recent research projects, and brush up on ML concepts. (datasciencepreparation)[https://www.datasciencepreparation.com/] aims to help you crack the machine learning interviews at major product-based companies and start-ups such as Amazon, Google, Microsoft, Meta, etc. If you are applying for machine learning roles, it is crucial to know what kind of Machine Learning interview questions interviewers may generally ask, and this blog comprises such “15 most commonly asked Machine Learning Questions with Answers”.
1. What is bias-variance trade-off? How does it impact model performance?
2. What is biased data? How to detect biasness in data?
3. What is an activation function? What are commonly used activation functions?
4. Why we don’t use the sigmoid activation function for all layers??
5. What is overfitting? How to detect and avoid overfitting?
6. What is Dropout? Implement dropout using Python.
7. What is Cross-Entropy Loss?
8. What is F1-Score? Define it in terms of Precision and Recall.
9. What loss functions can be used for regression? Which one is better for outliers?
10. What is Regularization? When do we need it?
11. What are Type I and Type II errors? How to avoid them?
12. What is a P-value, and how to interpret them?
13. What is ROC Curve? What is the interpretation of an ROC area under the curve?
14. What is Linear Regression? What are the assumptions of a Linear Regression Model?
15. Explain Logistic Regression and it’s optimization.
16. Explain K Nearest Neighbor classification Algorithm in detail? Implement KNN from scratch without using scikit-learn.
17. Explain K-Means Algorithm? How to select optimal value of “k” it? Does k-means converge to a global solution?
We hope this helps!