Data Science

Think you have mastered machine learning? Put your skills to the test with this 20-question MCQs Machine Learning Quiz covering core concepts like supervised vs. unsupervised learning, neural networks, model evaluation, and more! Whether you are a student, data scientist, researcher, or ML enthusiast, this machine learning quiz will challenge and sharpen your understanding. This machine learning quiz is perfect for exam preparation, interviews, or self-assessment. Let us start with the online machine learning quiz now.

Online Machine Learning Quiz with Answers

1. Suppose that we have created a machine learning algorithm that predicts whether a link will be clicked with 99% sensitivity and 99% specificity. The rate the link is clicked is 1/1000 of visits to a website. If we predict the link will be clicked on a specific visit, what is the probability it will be clicked?

 89.9%

 0.009%

 9%

 50%

2. Which Python library is used for machine learning?

 Scikit-learn

 Matplotlib

 NumPy

 Pandas

3. The best way to solve a problem using machine learning is by using the technique with the highest probability of solving it.

 Yes

 No

4. ABC runs a successful clothing business. He’s heard a bit about machine learning and thinks it could help him make some of his day-to-day tasks more efficient. How do you think machine learning could help his business? Sort new clothing stock according to audience preference.

 Yes

 No

5. Which of the following describes the way machine learning solves real-world problems?

 Imitating a singular solution to solve a given problem

 Learning from real examples and observations

 Generalizing from unsolved behaviours and patterns

 Writing complex code to break problems down

6. Select the scenarios where Machine Learning is particularly beneficial compared to traditional programming.

 Solving complex mathematical equations

 Automating repetitive and well-defined tasks

 Detecting anomalies in large datasets

 Providing personalized recommendations

 Predicting customer behavior based on past data

7. Machine learning is a combination of different capabilities all working together and cannot be defined in a singular way.

 No

 Yes

8. ABC runs a successful clothing business. He’s heard a bit about machine learning and thinks it could help him make some of his day-to-day tasks more efficient. How do you think machine learning could help his business? Recommending clothing budgets for customers based on their socio-economic status.

 Yes

 No

9. ABC runs a successful clothing business. He’s heard a bit about machine learning and thinks it could help him make some of his day-to-day tasks more efficient. How do you think machine learning could help his business? Predicting future fashion trends so he can plan for new designs and products sooner.

 Yes

 No

10. What are some common error rates for predicting binary variables (i.e., variables with two possible values like yes/no, disease/normal, clicked/didn’t click)?

 Median absolute deviation

 Correlation

 Sensitivity

 $R^2$

 Root mean squared error

11. Machine learning is a breakthrough system whereby solutions to complex problems, such as human and environmental errors, can be programmed directly into machines.

 Yes

 No

12. Real-world problems can be highly complex and should only be solved by complex logical rules

 Yes

 No

13. XYZ is developing an app that reads text messages out loud from a screen in Spanish. What machine learning approach would you recommend to help Jake make his app a success?

 Give the machine similar reading tasks that have been repeated thousands of times already, so it has enough data to get started

 Give the machine the least amount of reading examples possible, so it can find the best solutions and minimize machine errors

 Give the machine multiple examples of texts being read out in various languages, so it learns the same behaviour.

14. Suppose we build a prediction algorithm on a data set, and it is 100% accurate on that data set. Why might the algorithm not work well if we collect a new data set?

 We have too few predictors to get good out of sample accuracy

 Our algorithm may be overfitting the training data, predicting both the signal and the noise

 We have used neural networks which have notoriously bad performance

 We may be using a bad algorithm that doesn’t predict well on this kind of data

15. Which of the following best describes machine learning?

 A single technique with the highest probability of solving complex problems

 A combination of different capabilities orchestrated and working together

 The direct programming of solutions into machines to solve human and environmental errors

 A set of complex logical rules for solving highly complex real-world problems

16. Which of the following are components in building a machine learning algorithm?

 Artificial intelligence

 Training and test sets

 Statistical inference

 Creating features

17. What is the primary task of model training in machine learning?

 Identifying patterns in the data

 Evaluating the model

 Preparing the data

 Making predictions

18. What are the typical sizes for the training and test sets?

 100% training set, 0% test set.

 90% training set, 10% test set

 60% in the training set, 40% in the testing set.

 20% training set, 80% test set.

19. Which of the following businesses could potentially benefit the most from machine learning?

 Those with low sales and poor profits

 Those with already successful automated processes

 Those with high employee retention

 Those with simple informational processes

20. ABC runs a successful clothing business. He’s heard a bit about machine learning and thinks it could help him make some of his day-to-day tasks more efficient. How do you think machine learning could help his business? Using customers’ measurements to automatically recommend the right size.

 No

 Yes

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Online Machine Learning Quiz

• Real-world problems can be highly complex and should only be solved by complex logical rules
• The best way to solve a problem using machine learning is by using the technique with the highest probability of solving it.
• Machine learning is a combination of different capabilities all working together and cannot be defined in a singular way.
• Machine learning is a breakthrough system whereby solutions to complex problems, such as human and environmental errors, can be programmed directly into machines.
• ABC runs a successful clothing business. He’s heard a bit about machine learning and thinks it could help him make some of his day-to-day tasks more efficient. How do you think machine learning could help his business? Predicting future fashion trends so he can plan for new designs and products sooner.
• ABC runs a successful clothing business. He’s heard a bit about machine learning and thinks it could help him make some of his day-to-day tasks more efficient. How do you think machine learning could help his business? Using customers’ measurements to automatically recommend the right size.
• ABC runs a successful clothing business. He’s heard a bit about machine learning and thinks it could help him make some of his day-to-day tasks more efficient. How do you think machine learning could help his business? Sort new clothing stock according to audience preference.
• ABC runs a successful clothing business. He’s heard a bit about machine learning and thinks it could help him make some of his day-to-day tasks more efficient. How do you think machine learning could help his business? Recommending clothing budgets for customers based on their socio-economic status.
• XYZ is developing an app that reads text messages out loud from a screen in Spanish. What machine learning approach would you recommend to help Jake make his app a success?
• Which of the following best describes machine learning?
• Which of the following describes the way machine learning solves real-world problems?
• Which of the following businesses could potentially benefit the most from machine learning?
• Which of the following are components in building a machine learning algorithm?
• Suppose we build a prediction algorithm on a data set, and it is 100% accurate on that data set. Why might the algorithm not work well if we collect a new data set?
• What are the typical sizes for the training and test sets?
• What are some common error rates for predicting binary variables (i.e., variables with two possible values like yes/no, disease/normal, clicked/didn’t click)?
• Suppose that we have created a machine learning algorithm that predicts whether a link will be clicked with 99% sensitivity and 99% specificity. The rate the link is clicked is 1/1000 of visits to a website. If we predict the link will be clicked on a specific visit, what is the probability it will be clicked?
• Select the scenarios where Machine Learning is particularly beneficial compared to traditional programming.
• Which Python library is used for machine learning?
• What is the primary task of model training in machine learning?

Take a Quiz about Data Science

Machine Learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. From recommendation systems to self-driving cars, ML powers modern innovations. It uses algorithms like neural networks, decision trees, and regression to analyze data and improve accuracy over time.

Think you know machine learning? These machine learning quizzes contain 20 multiple-choice questions. Take these interactive ML quizzes to challenge yourself on key concepts—from supervised vs. unsupervised learning to neural networks, model evaluation, and beyond! Perfect for students, data scientists, researchers, and practitioners, these machine learning quizzes cover fundamentals to advanced topics in ML. These quizzes are Great for exam prep, interviews, or self-assessment.

Online Machine Learning Quizzes

Practice Data Mining Quizzes

Machine Learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. From recommendation systems to self-driving cars, ML powers modern innovations. It uses algorithms like neural networks, decision trees, and regression to analyze data and improve accuracy over time.