Top 25 Machine Learning Interview Questions in 2025
Table of Contents
- Jaro Education
- 16, March 2025
- 4:00 pm
The field of machine learning is constantly evolving with the onset of rapid technological advancements over the past few years. This has made it an extremely exciting yet challenging field. It doesn’t matter if you’re a fresher or an experienced professional, jobs in the field of machine learning are booming, and you could be the one who lands one of the best offers!
However, before landing a job, comes the tasking feat of answering the machine learning interview questions you’ll be definitely asked once your application makes it through. These questions assess your theoretical knowledge as well as your practical experience and ultimately decide if you’re the candidate they should really hire for the role. This blog covers the top 25 machine learning interview questions that will help you ace your next interview.
1. What is machine learning?
This is one of the machine learning basic interview questions that every candidate should be prepared for. Understanding the fundamentals of machine learning is key to tackling more advanced questions.
Answer: Machine learning is a subset of artificial intelligence that enables computers to learn from data without being explicitly programmed. It involves creating algorithms that can improve their performance over time as they are exposed to more data.
2. What are the different types of machine learning?
Another fundamental question in machine learning interview questions for freshers. The answer to this question will test your understanding of the primary categories in ML.
Answer: There are three main types of machine learning:
- Supervised learning: The algorithm learns from labeled data to make predictions.
- Unsupervised learning: The algorithm works with unlabeled data and identifies patterns.
- Reinforcement learning: The algorithm learns by interacting with an environment and receiving feedback from actions taken.
 *InterviewBit
3. What is the difference between classification and regression?
One of the common machine learning algorithms interview questions that assess your knowledge of basic ML techniques.
Answer: Classification is the task of predicting a discrete label (e.g., classifying emails as spam or not), while regression involves predicting a continuous value (e.g., predicting housing prices).
4. Explain the concept of overfitting and underfitting.
This is a common topic in AI and machine learning interview questions because it is essential to evaluate a candidate’s understanding of model performance.
Answer:
- Overfitting occurs when the model learns the noise in the training data, causing it to perform poorly on new data.
- Underfitting happens when the model is too simple to capture the underlying patterns in the data, leading to poor performance on both training and test sets.
5. What are the different evaluation metrics for classification problems?
Evaluating classification models is a critical step in ML, and this question tests your knowledge of metrics used in machine learning interview questions.
Answer: Common evaluation metrics for classification include accuracy, precision, recall, F1 score, and ROC-AUC.
6. What is cross-validation, and why is it important?
Cross-validation is often tested in machine learning interview questions for freshers as it is a basic yet crucial concept in evaluating model performance.
Answer: Cross-validation is a technique used to assess how the results of a model generalize to an independent dataset. It helps reduce overfitting by using multiple data splits for training and testing.
7. Explain the bias-variance tradeoff.
The bias-variance tradeoff is a critical concept that often comes up in machine learning basic interview questions.
Answer:
- Bias refers to the error introduced by approximating a real-world problem with a simplified model.
- Variance refers to the error caused by the model’s sensitivity to small fluctuations in the training data. The tradeoff is about balancing the two to achieve the best model performance.
8. What are decision trees, and how do they work?
Decision trees are one of the most fundamental machine learning algorithms. They often appear in machine learning algorithms interview questions.
Answer: A decision tree is a supervised learning algorithm that splits the data into subsets based on feature values. It builds a tree-like structure with decision nodes and leaf nodes that represent the output predictions.
9. What is a random forest?
This question is commonly asked when discussing machine learning algorithms, interview questions and tests your knowledge of ensemble methods.
Answer: A random forest is an ensemble of decision trees that work together to improve the accuracy of predictions. It reduces overfitting by averaging the predictions of multiple trees.
10. What are k-nearest neighbors (KNN)?
KNN is another common topic that often comes up in AI and machine learning interview questions.
Answer: KNN is a supervised learning algorithm that classifies a data point based on the majority class of its k-nearest neighbors. It is simple but effective for classification tasks.
11. How does gradient descent work?
Gradient descent is a critical optimization technique and a key part of many ML algorithms. This question is frequently asked in machine learning interview questions.
Answer: Gradient descent is an optimization algorithm used to minimize the cost function by iteratively adjusting the parameters of the model in the direction of the steepest gradient.
12. What is the difference between L1 and L2 regularization?
This is a machine learning basic interview question that assesses your understanding of techniques to prevent overfitting.
Answer:
- L1 regularization adds the absolute value of the coefficients to the cost function.
- L2 regularization adds the square of the coefficients to the cost function. Both techniques help reduce overfitting by penalizing large coefficients.
13. What is the role of a confusion matrix?
A confusion matrix is essential for understanding the performance of a classification model, and this question is often asked in machine learning interview questions for freshers.
Answer: A confusion matrix is a table that shows the true positive, true negative, false positive, and false negative values for a classification model. It helps evaluate the accuracy and other metrics.
14. Explain the difference between bagging and boosting.
Machine learning algorithms interview questions often test your understanding of ensemble techniques like bagging and boosting.
Answer:
- Bagging involves training multiple models independently and then combining their predictions, which helps reduce variance.
- Boosting trains models sequentially, with each model learning from the errors of the previous one, helping to reduce bias.
15. What is the purpose of support vector machines (SVM)?
Support Vector Machines are frequently tested in AI and machine learning interview questions due to their widespread use in classification tasks.
Answer: SVM is a supervised learning algorithm that finds the hyperplane that best separates the data points into different classes. It works well for both linear and non-linear classification tasks.
16. How would you handle missing data in a dataset?
This is a practical question that tests your ability to preprocess data, commonly asked in machine learning interview questions.
Answer: Missing data can be handled by:
- Imputing with mean, median, or mode.
- Using algorithms that support missing values.
- Dropping rows or columns with missing data, depending on the extent of missing values.
17. What is feature selection, and why is it important?
Feature selection is a crucial part of machine learning and is often covered in machine learning interview questions for freshers.
Answer: Feature selection is the process of choosing the most important features for your model. It helps improve performance by reducing overfitting, simplifying the model, and decreasing computation time.
18. What is principal component analysis (PCA)?
PCA is an essential technique for dimensionality reduction and is often tested in machine learning basic interview questions.
Answer: PCA is a statistical technique that transforms high-dimensional data into fewer dimensions by finding the principal components that capture the most variance in the data.
19. Can you explain the term "Deep Learning"?
Deep Learning is an area that is becoming increasingly relevant in machine learning interview questions.
Answer: Deep learning is a subset of machine learning that uses neural networks with many layers to model complex patterns in data. It is especially effective for tasks like image and speech recognition.
20. What is the role of neural networks in machine learning?
This is another common machine learning interview question that evaluates your understanding of advanced machine learning techniques.
Answer: Neural networks are used to model complex relationships between inputs and outputs. They are particularly useful for tasks involving unstructured data, such as images, audio, and text.
21. How would you deal with imbalanced datasets?
In many practical machine learning problems, datasets are imbalanced, and this question tests how you approach such scenarios in AI and machine learning interview questions.
Answer: Techniques for dealing with imbalanced datasets include:
- Resampling techniques such as oversampling the minority class or undersampling the majority class.
- Using appropriate evaluation metrics like the F1 score or balanced accuracy.
- Using algorithms like Random Forest or XGBoost that are less sensitive to class imbalance.
22. What is transfer learning?
Transfer learning is an important concept in deep learning, and this question may appear in machine learning interview questions for freshers.
Answer: Transfer learning involves using a pre-trained model on a new task. It leverages the knowledge gained from the original task and adapts it to a similar but different task, reducing the need for a large dataset.
23. How do you assess the performance of a regression model?
In machine learning basic interview questions, candidates are often asked to evaluate the performance of regression models.
Answer: Performance of regression models can be assessed using metrics like Mean Squared Error (MSE), R-squared, and Root Mean Squared Error (RMSE).
24. What is the difference between a generative and a discriminative model?
This is a more advanced question you might encounter in machine learning algorithms interview questions.
Answer:
- Generative models model the joint probability distribution of the input and output data (e.g., Naive Bayes).
- Discriminative models focus on modeling the conditional probability of the output given the input data (e.g., logistic regression, SVM).
25. What are some common challenges in deploying machine learning models?
Finally, this machine learning interview question tests your ability to move from the theoretical to the practical side of machine learning.
Answer: Common challenges in deploying machine learning models include handling real-time data, ensuring model scalability, model interpretability, and managing the lifecycle of models in production.
Final Thoughts on Machine Learning Interview Questions
Machine learning continues to evolve rapidly, and staying updated with the latest techniques and theories is crucial for success in 2025. The interview questions on machine learning discussed above cover fundamental concepts, algorithms, and practical applications that will be key to your success in any machine learning role. Preparing for these questions will give you the confidence to handle the most common challenges you may face during your interviews.
If you are looking to enhance your machine learning knowledge or need assistance in preparing for your next machine learning interview questions, consider enrolling in the Advanced Certificate Programme in Machine Learning, Gen AI & LLMs for Business Applications – IITM Pravartak Technology Innovation Hub of IIT Madras, offered in partnership with Jaro Education. This programme will provide you with a comprehensive overview of everything you need to know about machine learning. All the best!
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