20 Quirky and Interesting Machine Learning Interview Questions

The answers to these machine learning questions will benefit data scientists and analysts.

Machine learning is a part of two of the most important technologies of our time, artificial intelligence and data science. Machine learning engineers are able to leverage AI capabilities and contribute to one of the biggest innovative tech fields in the world. Right from robotics, deep learning, to augmented reality, virtual reality, and virtual assistants, machine learning engineers are in high demand for their skills. Similar is the case with data science. Data science is one of the hottest tech professions right now and it is required for data scientists and analysts to know machine learning fundamentals, if not in-depth concepts.

Strictly speaking from a data science point of view, many data scientists study ML and learn about its new packages, frameworks, and techniques, rather than core theoretical concepts. But with the right set of questions, one can contemplate the deeper aspects of this technology. Analytics Insight has dug the internet to find experienced professionals in this field for interesting questions about machine learning that can pique the interest of skilled data professionals.

Interesting Machine Learning Questions

1. What is the similarity between Hadoop and K?

2. If a linear regression model shows a 90% confidence interval, what does that mean?

3. A single-layer perceptron or a 2-layer decision tree, which one is superior in terms of expressiveness?

4. How can a neural network be used for dimensionality?

5. Name two utilities of the intercept term in linear regression?

6. Why do a majority of machine learning algorithms involve some kind of matrix manipulation?

7. Is time series really a simple linear regression problem with one response variable predictor?

8. Can it be mathematically proven that finding the optimal decision trees for a classification problem among all decisions trees is hard?

9. Which is easier, a deep neural network or a decision tree model?

10. Apart from back-propagation, what are some of the other alternative techniques to train a neural network?

11. How can one tackle the impact of correlation among predictors on principal component analysis?

12. Is there a way to work beyond the 99% accuracy mark on a classification model?

13. How can one capture the correlation between continuous and categorical variables?

14. Does k-fold cross-validation work well with time-series model?

15. Why can't simple random sampling of training data set and validation set work for a classification problem?

16. What should be a priority, a model accuracy or model performance?

17. What is your preferred approach for multiple CPU cores, boosted tree algorithm, or random forest?

18. What algorithm works best for tiny storage, logistic regression, or k-nearest neighbor?

19. What are the criteria to choose the right ML algorithm?.

20. Why can't logistic regression use more than 2 classes?