Machine Learning Interview Questions and Answers

The following Machine Learning Interview Questions and their accompanying answers will help you handle any query that comes your way, be it about the core concepts of Deep Learning or handling corrupted data in a dataset. Let’s dive in

1. What is Machine Learning and how does it function?

Machine Learning, a subset of Artificial Intelligence (AI), enables computers to learn from data without being strictly programmed. It identifies patterns within datasets, builds models through training, and makes predictions or decisions when presented with new data.

2. What are the different types of Machine Learning?

Machine Learning has three main types:

Supervised Learning (Labelled data): It involves training a model using a dataset where both input features and their corresponding correct outputs (labels) are provided.

Unsupervised Learning (Unlabelled data): This is used when the dataset lacks labelled outputs. The model identifies hidden patterns, relationships, or structures in the data without explicit supervision.

Reinforcement Learning (Reward-based learning): It follows a trial-and-error method where the agent interacts with an environment, takes actions, and receives rewards or penalties based on the outcomes.

3. What is Deep Learning?

Deep Learning refers to a specialised form of Machine Learning that utilises artificial neural networks to process complex data, like images and speech. It mimics the human brain’s way of learning and is widely used in AI applications such as self-driving cars.

4. How do you handle missing or corrupted data in a dataset?

Here’s how you can answer this:

“I can handle missing data by removing rows, filling gaps with mean/median values, using predictive models, or leveraging techniques like K-Nearest Neighbours (KNN) imputation. Of course, choosing the right method depends on the dataset and the impact of missing values.”

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5. How do you select an appropriate classifier based on the size of the training dataset?

For small datasets, simpler models like Naïve Bayes or logistic regression work well. For larger datasets, Deep Learning models and ensemble methods such as Random Forest or XGBoost can provide higher accuracy.

6. What is overfitting, and how can you avoid it?

Overfitting occurs when a model learns noise instead of patterns. To avoid it, we can use techniques like cross-validation, dropout, regularisation (L1/L2), pruning (in decision trees) or increasing the dataset size.

7. Why do input sizes in computer vision problems tend to be large? Provide an example to illustrate

Computer vision inputs become huge because images consist of millions of pixels, each acting as an individual feature. For instance, a 1080p image has over 2 million pixels, leading to high-dimensional data. This increases computational complexity, requiring techniques like Principal Component Analysis (PCA) or Convolutional Neural Networks (CNNs) to reduce dimensions and extract meaningful patterns efficiently.

8. How can you effectively train a Convolutional Neural Network (CNN) with a small dataset?

We can use data augmentation (flipping, rotating images), transfer learning (pre-trained models), or synthetic data generation to expand the dataset. Fine-tuning an existing CNN like ResNet often works well.

9. What is the state-of-the-art object detection algorithm known as YOLO?

You Only Look Once (YOLO) is a real-time object detection algorithm that processes images in a single pass, making it extremely fast and efficient for real-world applications like autonomous driving.

10. How is supervised Machine Learning applied in modern business environments?

Supervised Machine Learning helps in fraud detection, customer segmentation, demand forecasting, spam filtering and recommendation systems in businesses. It helps automate decision-making based on historical data.

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11. What is semi-supervised Machine Learning?

Semi-supervised ML is a hybrid approach using a mix of labelled and unlabelled data. This method is useful when labelling is expensive, such as in medical imaging, where a small portion of data is annotated.

12. What is KNN Imputer and how does it work?

K-Nearest Neighbours (KNN) imputation fills missing values by finding the nearest neighbours and averaging their values. It’s especially useful for handling missing numerical data while maintaining relationships within the dataset.

13. What are unsupervised Machine Learning techniques?

Popular unsupervised Machine Learning techniques include clustering (K-Means, DBSCAN), dimensionality reduction (PCA, t-SNE), and anomaly detection (Isolation Forest). These methods uncover hidden patterns in unlabeled data.

14. What is the main distinction between supervised and unsupervised Machine Learning?

Supervised learning uses labelled data, where the model learns from input-output pairs to make accurate predictions, such as in classification and regression tasks. In contrast, unsupervised learning identifies hidden structures in unlabelled data, commonly used in clustering and anomaly detection, without predefined categories or outcomes.

15. What is the main difference between k-means and KNN algorithm?

K-Means is an unsupervised clustering algorithm which partitions data points into K clusters depending on similar attributes, without requiring predefined labels. In contrast, KNN is a supervised classification or regression algorithm that predicts labels by finding the majority vote or average of the nearest neighbours in the dataset.

16. What is Linear Discriminant Analysis?

Linear Discriminant Analysis (LDA) is a technique related to dimensionality reduction that finds linear combinations of features to maximise class separation. This is commonly used for classification problems.

17. What is the difference between inductive Machine Learning and deductive Machine Learning?

Inductive learning extracts patterns from specific data samples and generalises them to make future predictions, commonly used in traditional Machine Learning models. Deductive learning, on the other hand, starts with known general principles or rules and applies them to specific cases, often seen in expert systems and rule-based AI.

18. What are the key distinctions between Machine Learning and Deep Learning?

Machine Learning includes various algorithms like decision trees and Support Vector Machines (SVMs), working with structured data and smaller datasets. Deep Learning is a subset of Machine Learning (ML) which employs artificial neural networks to process complex patterns. It requires large datasets and high computational power for applications like Natural Language Processing (NLP) and image recognition.

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19. How can we visualise high-dimensional data in 2-D?

High-dimensional data can be visualised in 2D using dimensionality reduction techniques like:

Principal Component Analysis (PCA): It captures key variance.

t-distributed Stochastic Neighbor Embedding (t-SNE): It preserves local structures

Uniform Manifold Approximation and Projection (UMAP): It maintains global and local relationships.

These methods help reveal patterns and clusters in complex datasets effectively.

20. Explain the XGBoost model's working procedure

XGBoost is an advanced boosting algorithm that builds decision trees sequentially, where each tree rectifies the errors of the previous ones. It uses gradient boosting to minimise errors. It handles missing values, feature importance, and large datasets while preventing overfitting with regularisation techniques.

21. Explain the SMOTE method that's used to handle data imbalance

Synthetic Minority Over-sampling Technique (SMOTE) generates synthetic samples for the minority class by interpolating between existing points. This helps us balance the dataset, prevent bias in Machine Learning models, particularly in classification tasks where one class significantly outnumbers the other.

22. Is the accuracy score always a reliable metric for evaluating the performance of a classification model?

No, accuracy alone can be misleading, especially in imbalanced datasets. A model predicting the majority class may show high accuracy but poor actual performance. Metrics such as precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) provide a more comprehensive evaluation of classification effectiveness.

23. What is a confusion matrix, and what makes it so useful?

A confusion matrix visually represents actual vs predicted classifications, detailing true positives, false positives, false negatives, and true negatives. It us helps assess model performance using precision, recall, F1-score, and allows for deeper evaluation beyond a single metric like accuracy.

24. State the main difference between parametric and non-parametric models?

Parametric models, like linear regression, assume a predefined functional form and fixed parameters. They are computationally efficient but less flexible. Non-parametric models, like decision trees, do not assume a strict structure. They adapt better to complex data but often require more computation.

25. What is the reason behind the curse of dimensionality?

As the number of features increases, data points become sparse, making distance-based calculations less meaningful. This impacts model performance, increases computational complexity and reduces generalisation. Dimensionality reduction techniques like Principal Component Analysis (PCA) help mitigate this issue in high-dimensional datasets.

26. Out of MAE, MSE, or RMSE, which metric is more robust to outliers?

Mean Absolute Error (MAE) is more robust to outliers because it calculates the absolute differences between actual and predicted values. Root Mean Squared Error (RMSE), and Mean Squared Error (MSE) penalise large errors more due to squaring, making them sensitive to outliers.

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27. What is Syntactic Analysis?

Syntactic Analysis, also called parsing, examines sentence structure and grammar in NLP applications. It determines how words relate to each other in a sentence. It helps machines understand meaning, improve search engines and improve chatbot interactions by structuring text data.

28. How do content-based filtering and collaborative filtering differ in recommendation systems?

Content-based filtering recommends items based on user preferences and item attributes, like genre or keywords. Meanwhile collaborative filtering suggests items based on user interactions and preferences similar to other users, making it more dynamic but requiring large datasets.

29. How would you evaluate the goodness-of-fit of a linear regression model? Which metrics are most important, and why?

Here’s how you can answer this:

“I would measure goodness-of-using R² (coefficient of determination), Adjusted R² (for multiple predictors), Root Mean Squared Error (RMSE) and residual analysis. These metrics assess how well the model explains variance and ensure it neither underfits nor overfits the data.”

30. What is null hypothesis in the linear regression problem?

In linear regression, the null hypothesis assumes no relationship between the independent and dependent variables, meaning the regression coefficients are zero. If the p-value is low, the null hypothesis gets rejected, which indicates a statistically significant relationship between the variables.

31. Can You Use SVMs for both classification and regression tasks?

Yes, Support Vector Machines can be used for both tasks. Support Vector Classification (SVC) separates classes using a hyperplane, while Support Vector Regression (SVR) finds a best-fit hyperplane to predict continuous values while minimising error.

33. What is feature importance in Machine Learning, and how can it be identified?

Feature importance ranks the significance of input variables in a model’s predictions. It helps in feature selection, reducing overfitting and improving interpretability. I go with techniques like decision trees, permutation importance and SHapley Additive exPlanations (SHAP) values to identify the most influential features.

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34. What Is ‘naive' in the Naive Bayes Classifier?

The "naive" assumption in Naive Bayes refers to the belief that all features are independent given the class label. While often unrealistic in real-world scenarios, this assumption simplifies probability calculations and makes the algorithm computationally efficient for text classification and spam detection.

35. What is a radial basis function?

A Radial Basis Function (RBF) is a kernel function employed in Support Vector Machines (SVMs) and neural networks. It transforms data into a higher-dimensional space, helping separate non-linear relationships by measuring similarity based on distance from a central point.

36. How do you determine the appropriate Machine Learning algorithm for a classification problem?

Choosing the right ML algorithm depends on factors like dataset size, feature types, interpretability, and accuracy needs. For structured data, decision trees or regression work well, while Deep Learning is better for unstructured data like images or text.

37. How does Amazon generate product recommendations, and how does its recommendation engine function?

Amazon’s recommendation system combines the following:

Collaborative filtering (user behaviour-based)

Content-based filtering (product attributes)

Deep Learning (personalised predictions)

It analyses browsing history, purchase patterns, and user similarities to suggest relevant products dynamically.

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38. What is the bias-variance tradeoff in the field of Machine Learning?

The bias-variance tradeoff represents the balance between bias (underfitting, overly simple models) and variance (overfitting, overly complex models). High bias models generalise poorly, while high variance models are sensitive to noise. A well-tuned model finds an optimal balance between the two.

39. Do we need to scale feature values when there is a significant variation among them?

Yes, scaling is necessary when feature values have different ranges, especially for algorithms like SVMs, KNN, and gradient descent-based models. Methods like Min-Max Scaling or Standardisation (Z-score normalisation) ensure numerical stability, improving convergence and preventing features with large values from dominating.

40. Your trained model exhibits low bias and high variance. How can you address this issue?

To handle high variance, I’d reduce overfitting by using regularisation (L1/L2), increasing training data, feature selection, or ensemble methods. Cross-validation and simplifying the model architecture (e.g., pruning in decision trees) also help in improving generalisation while maintaining predictive performance.

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41. What are the assumptions of linear regression?

Linear regression assumes the following:

Linearity which is a straight-line relationship between variables.

independence of errors.

Homoscedasticity which is a constant variance of residuals.

Normality of residuals.

No multicollinearity between independent variables.

Violating these assumptions can affect model performance and lead to unreliable predictions.

42. What are some common similarity measures used in Machine Learning?

Common similarity measures include:

Euclidean Distance for continuous features in clustering and KNN.

Cosine Similarity for text and high-dimensional data.

Jaccard Similarity for categorical or binary data.

Manhattan Distance when absolute differences matter.

These help in classification, clustering, and recommendation systems.

43. Which is more robust to outliers: Decision Tree or Random Forest?

Random forests are more robust to outliers than decision trees. Decision trees can be sensitive to noise, leading to overfitting. Random forests, being an ensemble of multiple trees, reduce variance, making them more stable against extreme values in training data.

44. What is the difference between L1 and L2 regularisation? What is their significance?

L1 Regularisation (Lasso) shrinks coefficients and can eliminate irrelevant features, making models sparse. L2 Regularisation (Ridge) reduces coefficients gradually, preventing overfitting without feature elimination. Both improve model generalisation, with L1 suited for feature selection and L2 for reducing variance.

45. What do false positives and false negatives mean, and why are they important in Machine Learning?

False Positive (Type I Error) incorrectly predicts a positive outcome (e.g., marking a legitimate email as spam). False Negative (Type II Error) fails to detect a positive instance (e.g., missing a disease in a medical test). Their impact varies by application, affecting decision-making and model evaluation.

46. What are the three key phases involved in crafting a Machine Learning model?

The three main stages of building a Machine Learning (ML) model are:

Data Preprocessing: Cleaning, feature engineering, and splitting data.

Model Training & Tuning: Selecting algorithms, training models, hyperparameter tuning, and cross-validation.

Evaluation & Deployment: Assessing performance with metrics, refining, and deploying the model in real-world applications.

47. What are ‘training set’ and ‘test set’ in a Machine Learning model? How should data be distributed among the training, validation, and test sets?

The training set trains the model, while the test set evaluates it. Typically, data is split as 70-80% training, 10-15% validation, and 10-20% test. Validation helps fine-tune hyperparameters, ensuring the model generalises well to unseen data.

48. What strategies can be used to optimise the inference time of a trained transformer model?

For reducing the inference time of a trained transformer model, I’d go with the following process:

Quantisation: Reducing precision of weights.

Pruning: Removing less important neurons.

Knowledge Distillation: Training a smaller model with a larger one’s knowledge.

Efficient architectures: Like DistilBERT or MobileBERT for lighter deployment.

These methods enhance speed without significantly affecting accuracy.

49. What are Stemming and Lemmatization?

Stemming and Lemmatization are text preprocessing techniques in NLP that reduce words to their base forms. Stemming trims words by removing suffixes (e.g., "running" → "run"), while lemmatisation considers context and converts words to their dictionary form (e.g., "better" → "good"), improving Text Analysis accuracy.

Conclusion

Brushing up on the most asked Machine Learning Interview Questions is your key to landing that dream role! From core algorithms to real-world applications, the questions outlined in this blog will help you with the essential knowledge. So, keep learning and refining your problem-solving skills because in ML, growth is the only constant.

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Top 50 Machine Learning Interview Questions and Answers in 2025

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