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Machine Learning Intermediate Python Interview Questions

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1. Is the SVM machine learning method supervised or unsupervised?
2. State a real life use case of a neural network.
3. What is a neural network?
4. What is a perceptron? use the Perceptron function from the scikit learn library to demonstrate this technique
5. Where do you source/find data in order to build machine learning algorithms?
6. Give an example of a supervised machine learning method and implement the algorithm using python. You may use scikit learn
7. Is a Naive Bayes Classifier algorithm an example of a supervised or unsupervised algorithm? and state a use case for the Naive Bayes Classifier
8. What are Naive Bayes Classifiers?
9. Define Bayes Theorem.
10. What is a decision tree and when might they be used?
11. Demonstrate how a linear SVM works and give an example of a real world use case.
12. What is gradient descent and how is it used in Linear Regression?
13. What is a confusion matrix and how are they used in machine learning?
14. What is the F1 score of an algorithm? And demonstrate how it may be calculated?
15. Define recall and use a scikit learn library to demonstrate how you would calculate this metric.
16. Define and calculate the precision of the algorithm. How does this differ from the accuracy of the algorithm?
17. What is meant when referring to the 'accuracy' of a machine learning algorithm? and how might you calculate the accuracy of an algorithm?
18. When might you use the k-nearest neighbor algorithm (KNN)?
19. Demonstrate the k-nearest neighbor algorithm (KNN) using the following data
20. What is the difference between linear regression and multiple linear regression?
21. Outline the basic steps or stages you use when implementing a machine learning algorithm
22. Implement the train test model for splitting data using the scikit learn python library

Machine Learning Intermediate Python Interview Questions

1. Is the SVM machine learning method supervised or unsupervised?

2. State a real life use case of a neural network.

3. What is a neural network?

4. What is a perceptron? use the Perceptron function from the scikit learn library to demonstrate this technique

5. Where do you source/find data in order to build machine learning algorithms?

6. Give an example of a supervised machine learning method and implement the algorithm using python. You may use scikit learn

7. Is a Naive Bayes Classifier algorithm an example of a supervised or unsupervised algorithm? and state a use case for the Naive Bayes Classifier

8. What are Naive Bayes Classifiers?

9. Define Bayes Theorem.

10. What is a decision tree and when might they be used?

11. Demonstrate how a linear SVM works and give an example of a real world use case.

12. What is gradient descent and how is it used in Linear Regression?

13. What is a confusion matrix and how are they used in machine learning?

14. What is the F1 score of an algorithm? And demonstrate how it may be calculated?

15. Define recall and use a scikit learn library to demonstrate how you would calculate this metric.

16. Define and calculate the precision of the algorithm. How does this differ from the accuracy of the algorithm?

17. What is meant when referring to the 'accuracy' of a machine learning algorithm? and how might you calculate the accuracy of an algorithm?

18. When might you use the k-nearest neighbor algorithm (KNN)?

19. Demonstrate the k-nearest neighbor algorithm (KNN) using the following data

20. What is the difference between linear regression and multiple linear regression?

21. Outline the basic steps or stages you use when implementing a machine learning algorithm

22. Implement the train test model for splitting data using the scikit learn python library