The answers to these machine learning questions will benefit scientists and data analysts.
Machine learning is one of two of the most important technologies of our time, artificial intelligence and data science. Machine learning engineers are able to leverage the capabilities of AI and contribute to one of the world’s largest areas of innovative technology. From robotics to deep learning, augmented reality, virtual reality and virtual assistants, machine learning engineers are in great demand for their skills. This is the case with data science. Data science is one of the hottest tech professions today, and data scientists and analysts need to know the fundamentals of machine learning, if not in-depth concepts.
From a strictly scientific point of view, many data scientists study ML and discover its new packages, frameworks and techniques, rather than basic theoretical concepts. But with the right set of questions, one can contemplate the deeper aspects of this technology. Analytics Insight has scoured the internet for experienced professionals in this field for interesting questions about machine learning that may pique the interest of skilled data professionals.
Interesting questions about machine learning
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-layered perceptron or a 2-layered decision tree, which is superior in terms of expressiveness?
4. How can a neural network be used for dimensionality?
5. Name two uses of the intercept term in linear regression?
6. Why do the majority of machine learning algorithms involve some sort of matrix manipulation?
7. Are time series really just a linear regression problem with one response variable predictor?
8. Can we mathematically prove that it is difficult to find the optimal decision trees for a classification problem among all the decision trees?
9. Which is simpler, a deep neural network or a decision tree model?
10. Apart from back propagation, what are the other alternative techniques for training a neural network?
11. How to approach the impact of the correlation between the predictors on the principal component analysis?
12. Is there a way to work beyond the 99% accuracy mark on a classification model?
13. How can we grasp the correlation between continuous and categorical variables?
14. Does k-fold cross-validation work well with the 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, the accuracy of a model or the performance of a model?
17. What is your preferred approach for multiple processor cores, a boosted tree algorithm, or a random forest?
18. Which algorithm works best for tiny storage, logistic regression, or nearest neighbor k?
19. What are the criteria for choosing the right ML algorithm?
20. Why can’t logistic regression use more than 2 classes?
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