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

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