Machine learning interview questions now focus on both theory and real-world applications.
Understanding basics like overfitting, bias, and regression improves interview performance.
Concepts like MLOps, transfer learning, and model explainability are rising in importance.
Machine learning is now a key part of many apps and tools used every day, from search engines to shopping apps. Companies are hiring people who understand how machine learning works, and the questions asked in interviews are getting more detailed. Here are 30 important questions that are expected to come up in 2025 interviews, explained in a simple way.
1. What is machine learning?
It is a method of learning by computers from data without being provided with precise instructions.
2. What are the categories of machine learning?
There are three primary categories: supervised learning, unsupervised learning, and reinforcement learning.
3. What is overfitting?
When the model works well on training data but not on new data.
4. What is underfitting?
When a model is too simple and does not learn from the patterns of data.
5. What is the distinction between classification and regression?
Classification is employed to forecast categories such as pass or fail. Regression is employed to forecast values such as marks or prices.
6. What is bias and variance?
Bias is a mistake due to incorrect assumptions. Variance is a mistake due to excessive complexity. Both must be controlled.
7. What is a feature in machine learning?
Feature is an input used to assist in making a prediction, such as hours studied to forecast exam score.
8. How does feature engineering work?
Creating new features or enhancing the ones that already exist to assist the model in achieving better performance.
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9. What is linear regression?
A way of forecasting a number from a linear relationship.
10. What is logistic regression?
Used to predict if something is part of a group, such as yes or no.
11. What is a decision tree?
A type that asks a set of yes or no questions to obtain a conclusion.
12. What is a random forest?
A collection of decision trees combines forces to provide an improved answer.
13. What is K-Nearest Neighbours (KNN)?
Considers close data points to determine the conclusion, such as asking peers for answers depending on comparable marks.
14. What is a support vector machine (SVM)?
Finds the optimal line to split two groups in the data.
15. What is clustering?
Puts similar items into groups without labels.
16. What is principal component analysis (PCA)?
Reduces feature count while retaining the most significant ones.
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17. What is a confusion matrix?
Table that displays accurate and inaccurate predictions in classification issues.
18. What are precision and recall?
Precision indicates how many selected items were correct. Recall indicates how many correct items were actually selected.
19. What is the F1-score?
A figure that combines precision and recall to determine accuracy.
20. What is cross-validation?
Testing a model on various sections of the data to see how well it performs.
21. What is regularization?
A technique employed to prevent overfitting by keeping the model simple.
22. What is AUC-ROC?
A plot that illustrates how well the model can distinguish between various categories.
23. What is a neural network?
A model consisting of layers of interconnected units, as the brain does.
24. What is backpropagation?
The way used to fine-tune the model is by altering its weights after verifying errors.
25. What is an epoch?
One complete cycle of training the model with every piece of data.
26. What is a batch?
A small portion of the data is used to train the model at each step.
27. What is dropout?
A method to prevent overfitting by turning off components of the model randomly while training.
28. What is transfer learning?
Applying a model that has been trained to do one task to assist with another task.
29. What is MLOps?
It is similar to DevOps but for machine learning. It is about training, testing, and deploying models into production.
30. Why is explainability important in ML?
Models sometimes produce correct answers, but the reason is not known. In practical problems, it is extremely crucial to know why a decision was reached.
These questions cover both basic ideas and more advanced topics. Anyone preparing for a job or internship in machine learning should understand them clearly. In interviews, answering these confidently shows both knowledge and interest in the subject.