The answers to these machine learning questions will benefit scientists and data analysts.
Machine learning is part of two of the most important technologies of our time, artificial intelligence and data science. Machine learning engineers can leverage AI capabilities and contribute to one of the world’s largest innovative technology fields. From robotics, deep learning, 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 most popular technology professions at the moment and it is necessary for scientists and data analysts to know the basics of machine learning, if not in-depth concepts.
Speaking strictly from the point of view of data science, many data scientists study ML and learn about 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 to find professionals with experience in this field for interesting questions about machine learning that may arouse the interest of qualified 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-layer perceptron or a 2-layer decision tree, which is superior in terms of expressiveness?
4. How can a neural network be used for dimensionality?
5. Name two utilities of the term interception in linear regression?
6. Why do most machine learning algorithms involve some kind of array manipulation?
7. Are time series really a simple linear regression problem with a predictor of response variables?
8. Can it be shown mathematically that it is difficult to find the optimal decision trees for a classification problem among all decision trees?
9. Which is easier, a deep neural network or a decision tree model?
10. Aside from posterior propagation, what are some of the other alternative techniques for training a neural network?
11. How can the impact of correlation between predictors be addressed in the analysis of key components?
12. Is there any way to work beyond 99% accuracy in a classification model?
13. How can the correlation between continuous variables and categorical variables be captured?
14. Does cross-validation k-fold work well with the time series model?
15. Why can’t simple random sampling of the training dataset and validation dataset work for a classification problem?
16. What should be a priority, model accuracy, or model performance?
17. What is your preferred approach for multiple CPU cores, driven tree algorithm, or random forest?
18. Which algorithm works best for a small warehouse, a logistic regression, or a closer neighbor?
19. What are the criteria for choosing the right ML algorithm ?.
20. Why logistic regression cannot use more than 2 classes?
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