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Machine Learning Basics Knowledge Test
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Machine learning (ML) is a sub-category of artificial intelligence (AI) focused on building computer systems that learn from data. The four commonly used types of machine learning algorithms are: supervised, semi-supervised, unsupervised and reinforcement.

Machine Learning Basics Knowledge Test
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25 Questions

1. In reinforcement learning, what is the function that estimates the expected future reward given a specific state and action pair?
2. Which type of machine learning algorithm is trained on labeled data to make predictions on new, unseen data?
3. In the context of neural networks, what is the term for the process of updating the model’s weights to minimize the error during training?
4. What is the primary drawback of the k-nearest neighbors (k-NN) algorithm?
5. What is the name of the technique used to deal with overfitting in machine learning models?
6. Which evaluation metric is commonly used for binary classification problems and measures the proportion of true positive predictions among all positive examples?
7. Which machine learning algorithm is designed to handle sequential data and has been widely used in speech recognition and natural language processing?
8. The loss function in a machine learning model measures:
9. In unsupervised learning, the primary task is:
10. Which machine learning algorithm is commonly used for classification tasks and is based on finding the best hyperplane that separates data points into different classes?
11. What is the primary purpose of a validation set in the context of model training?
12. What is the process of feeding a machine learning model with data to adjust its internal parameters and improve performance?
13. What is the primary advantage of using a deep learning architecture for machine learning tasks?
14. Which method is used for reducing the learning rate during the training of neural networks to avoid overshooting the optimal weights?
15. What is the primary advantage of using gradient boosting algorithms like XGBoost or LightGBM?
16. Which type of neural network architecture is used for sequence data, such as natural language processing and time series analysis?
17. Which technique is used for handling missing data in machine learning datasets?
18. Which machine learning technique allows models to make decisions based on past experiences and feedback from their environment?
19. What is the primary objective of the term “bias” in machine learning?
20. In machine learning, an ensemble model combines the predictions of multiple individual models to:
21. Which machine learning algorithm is used for both regression and classification tasks and is based on averaging the predictions of multiple weak learners?
22. Which machine learning algorithm is particularly well-suited for dealing with textual data and is based on probability theory?
23. What is the main purpose of cross-validation in machine learning?
24. Which approach is used for handling imbalanced datasets in classification tasks, where one class has significantly fewer samples than the others?
25. What is the main goal of machine learning?

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