Machine Learning Interview Questions: 50 Most Asked in 2025

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Businesses are implementing cutting-edge technology like artificial intelligence & machine learning in an effort to increase people’s access to information and services. The increasing use of these technologies is seen in a number of industrial areas, including manufacturing, healthcare, retail, banking, and finance. Preparing for the Machine Learning Interview Questions in 2025 is necessary if you want to become a competitive machine learning engineer.

Among the in-demand organizational jobs that are adopting AI are data scientists, machine learning engineers, artificial intelligence engineers, and data analysts. Understanding the kind of machine learning engineer interview questions that hiring managers and recruiters could ask is essential if you want to apply for these kinds of positions.

Keep reading and learning to learn what is a machine learning interview like and how you can prepare for the ML interview questions in 2025.

Table of Contents

50 Top Machine Learning Interview Questions & Answers in 2025

Without any further ado, let’s discuss how you can prepare for machine learning engineer interview questions to secure different machine learning engineer jobs.

Basic Machine Learning Interview Questions

First, we will start with a basic questions quiz on machine learning.

What Is Machine Learning?

The goal of machine learning (ML), a subfield of artificial intelligence (AI), is to create systems that can learn from data and get better without explicit programming. In order to optimize predictions or choices based on a specified objective function, algorithms are used to find patterns and correlations within data. Applications like computer vision, reference systems, and natural language processing make extensive use of machine learning.

What is Semi-Supervised Machine Learning?

The combination of supervised & unsupervised learning is known as semi-supervised learning. Moreover, a mixture of labeled & unlabeled data trains the algorithm. You can typically use it when we have a huge raw dataset and a very small raw dataset.

Simply put, you can create clusters using the unsupervised approach, and the remaining unlabeled data is labeled using the labeled data that already exists. Continuity, cluster, and manifold assumptions are made via a semi-supervised method.

It is typically employed to reduce the expense of purchasing labeled data. For instance, self-driving automobiles, free facial recognition search, automated voice recognition, and protein sequence categorization.

What is The Process For Selecting An Algorithm For A Dataset?

This is one of the most common machine learning interview questions, so you don’t panic when an interviewer asks you such a question. You need a business case study or application requirements in addition to the dataset. You can use the same data for both supervised and unsupervised learning.

In general:

Labeled data is necessary for supervised learning algorithms.
Continuous numerical objectives are necessary for regression methods.
Categorical goals are necessary for classification algorithms.
Algorithms for unsupervised learning need unlabeled data.
Labeled and unlabeled datasets must be combined for semi-supervised learning.
Data on the environment, agents, states, and rewards are necessary for reinforcement learning algorithms.

Also Read: Mastering Data Engineering: Common Data Engineer Interview Questions You Should Know

Explain The K Nearest Neighbor Algorithm

A classifier for supervised learning is the K Nearest Neighbor (KNN). It predicts how to arrange particular data pieces or classifies labels depending on proximity. You may apply it to both classification and regression. Since the KNN method is non-parametric, it does not assume anything about the distribution of the data.

In the classifier KNN:

The K-neighbors closest to the white point originate. In the following example, we choose k=5.
At K=5, there are two green and three red spots. We give the red a red label since it has a majority.

What Are Some Practical Uses Of Clustering Algorithms?

Applications for clustering algorithms in real life include:

Segmenting customers for effective advertising
Systems of recommendations for specific suggestions
Detecting anomalies to avoid fraud
Compressing images to save storage
Healthcare for assembling people with comparable ailments
Search engine classification of documents

Describe Feature Engineering. What Impact Does It Have On The Model’s Functionality?

This is also one of the most common machine learning interview questions in 2025. Here’s how you can answer it: Feature engineering is the process of generating new features by combining or transforming existing ones. Occasionally, certain features have a very subtle mathematical relationship that, with the right investigation, may create new features.

Occasionally, multiple pieces of information are combined and presented as a single data column. In certain situations, creating and utilizing additional features enables us to gain a profound understanding of the data, and if the features are significant enough, they also significantly enhance the model’s performance.

How Does Overfitting Work? What Are The Ways To Prevent It?

When a model overfits, it performs poorly on unknown data because it has learned the noise and particulars of the training set.

Preventing Overfitting:

Data splitting into different folds to verify performance on unseen data is known as cross-validation.
For big coefficients in the model, regularization adds a penalty (e.g., L1 for high sparsity, L2 for smaller weights).
Pruning is the process of eliminating unnecessary branches from complex models, such as decision trees.
Early Stopping: When the validation error no longer decreases, training is no longer available.
Dropout: To avoid dependence on specific nodes during training, neurons in neural networks are randomly deactivated.

Explain The Bias-Variance Tradeoff

The link between model complexity and simplicity is:

Underfitting results from bias, which is a consequence of oversimplified assumptions (e.g., using linear models on nonlinear data).
Variance: Overfitting (e.g., large-degree polynomial models) is caused by sensitivity to changes in training data.

By balancing bias and variance through strategies such as regularization and cross-validation, optimal performance is achieved.

How Do Parametric And Non-Parametric Models Vary From One Another?

Preparing for such machine learning interview questions is necessary for you in 2025. Here’s how you can answer:

Parametric Models:

Assume that the underlying data has a certain shape or distribution (e.g., linear relationships in Linear Regression).
In terms of calculations, they need fewer variables and are effective.
Limited ability to capture intricate patterns

Non-Parametric Models:

Don’t assume anything about the distribution of the data; instead, adjust to the data’s structure (e.g., Decision Trees, KNN).
It may require more data and processing power, but it can effectively manage complex datasets.

How Does Supervised Learning Differ From Unsupervised Learning?

Supervised Learning: The target variable is known when the model is trained using labeled data. Regression and categorization are two examples.
Unsupervised Learning: To identify patterns or groups, the model is trained on unlabeled data. Dimensionality reduction and clustering are two examples of this approach.

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Describe a Confusion Matrix And Explain Its Purpose

A confusion matrix is a table that assesses a classification model’s performance. The numbers of the false positives, false negatives, true positives, & true negatives are displayed. Metrics such as precision, recall, accuracy, and F1 score may be computed with its help.

What is Gradient Descent?

An optimization technique called gradient descent iteratively modifies model parameters along the path of the steepest descent to minimize the cost function.

The learning rate (α\alphaα) determines the size of each step.
There are three different types of gradient descent: mini-batch, stochastic, and batch.

What Does The Naive Bayes Classifier Mean By “Naive”?

In Naive Bayes, the “naive” assumption is the conviction that, given the class label, every characteristic is autonomous. This assumption streamlines probability calculations and makes the method computationally efficient for text categorization and spam detection, even though it is frequently impractical in real-world situations.

What Is The Purpose Of A Cost Function?

It is also one of the best and most frequently asked machine learning interview questions in 2025.

By quantifying performance, the cost function guides model training by calculating the error between expected and actual outputs. Among the examples are:

For regression problems, use Mean Squared Error (MSE).
For classification jobs, use cross-entropy loss.
During training, the model seeks to minimize this function.

How Can The Size Of A Training Set Affect The Classifier Selection Process?

A model with a right bias and low alteration appears to perform better when the training set is small, since it is less likely to overfit.

For instance, a big training is ideal for Naive Bayes. High-variance and low-bias models typically perform better because they can handle complex connections.

Top Intermediate-Level Machine Learning Interview Questions and Answers in 2025

Here, we will discuss the top intermediate-level Machine Learning Engineer Jobs interview questions and answers, assuming you have already covered the basic ones.

What is Feature Engineering?

Feature engineering is the process of developing or modifying input variables, or features, to enhance a model’s predictive capabilities. It connects machine learning algorithms with unprocessed data.

What is Dimensionality’s Curse?

The difficulties encountered when evaluating data in high-dimensional environments, where there are numerous characteristics, are referred to as the Curse of Dimensionality.

What Distinguishes Type I Error From Type II Error?

False positives are categorized as Type I errors, whereas false negatives are categorized as Type II errors. It indicates that something was claimed to have occurred even if it wasn’t a Type I mistake.

A Type II mistake, however, is the reverse. Claiming that something is not happening when it is, is a Type II mistake. Telling a man he is pregnant is more akin to the type I mistake. Conversely, a Type II mistake would be equivalent to informing a pregnant lady that she is not carrying a child.

Do You Know About The Fourier Transform?

During their data scientist interview, candidates may also encounter the most recent machine learning interview questions related to the Fourier transform. One popular method for decomposing generic operations into a superposition of symmetric functions is the Fourier transform. In short, it’s similar to deriving the recipe from a food that is presented to us.

The collection of cycle speeds, phases, and amplitudes that correspond to a given time signal may be found using the Fourier transform. It is simple to extract characteristics from audio signals and other series, such as sensor data, by converting the signal across the time domain to the frequency domain employing the Fourier transform.

What Distinguishes Machine Learning From Deep Learning?

You may find this question in practically every list of typical machine learning engineer interview questions. Deep learning is a subset of machine learning that has a strong connection to neural networks. Backpropagation and some neuroscience concepts are used in deep learning.

Deep learning applications aid in the precise modeling of large, unlabeled, or semi-structured data sets. The unsupervised learning algorithm is represented by deep learning. Deep learning AI models use neural networks to learn data representations, which sets them apart from conventional machine learning techniques.

How Can We Recognize Data Leakage And What Does It Entail?

Data leaking is what happens when there is a strong association between the input attributes and the target variable. This is due to the fact that when we train our model using that highly correlated feature, the model only has to learn the majority of the target variable’s information during the training phase in order to attain high accuracy. In this case, the model performs rather well on both training and validation data, but its performance falls short when we utilize it to generate actual predictions. We can detect data leaks in this way. Therefore, it is one of the most common machine learning interview questions in 2025.

Describe How The XGBoost Model Operates

XGBoost is a sophisticated boosting algorithm that constructs decision trees one after the other, fixing the mistakes of the earlier ones. Gradient boosting is used to reduce mistakes. Using regularization approaches, it manages big datasets, missing values, and feature significance while avoiding overfitting.

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Describe How Data Imbalance Is Handled Using The SMOTE Approach.

By interpolating between existing points, the Synthetic Minority Over-sampling Technique (SMOTE) creates synthetic samples for the minority class. In classification problems, when one class greatly outnumbers the other, this helps us balance the dataset and avoid bias in machine learning models.

Is The Accuracy Score Consistently A Trustworthy Indicator Of A Classification Model’s Performance?

No, accuracy by itself can be deceptive, particularly when datasets are unbalanced. Even when a model predicts the majority class with great accuracy, its performance may be subpar. A more thorough assessment of classification efficacy is offered by metrics like accuracy, recall, F1-score, and AUC-ROC.

What is Syntactic Analysis?

In NLP applications, syntactic analysis—also known as parsing—looks at syntax and sentence structure. It establishes the relationships between words in a phrase. Organizing text data enhances chatbot interactions, search engine optimization, and machine comprehension.

Is It Accurate To Say That When Feature Values Differ Significantly, We Must Scale Them?

Indeed. Since the majority of algorithms rely on the Euclidean distance between data points, the outcomes will change significantly if the feature value fluctuates significantly. Outliers typically result in poorer performance from machine learning models on the test dataset.

To speed up convergence, we also employ feature scaling. When features have no normalization, gradient descent will take longer to achieve a local minimum. It is one of the most frequently asked machine learning interview questions you must prepare for different machine learning jobs.

How Can Machine Learning Deal With Faulty Or Missing Data?

Maintaining model correctness requires handling missing data. Typical methods include:

Eliminate any rows or columns that have missing values; this is not the best option for huge missing sections.
Mode, Median, and Mean Imputation: Use statistical techniques to fill in the missing values.
Forward/Backward Filling: For time series data, use nearby values.
KNN Imputation: To estimate missing values, use comparable data points.
Predictive Imputation: To forecast missing data, apply a regression or classification model.

Enumerate The Steps Involved In Creating A Machine Learning Model.

Define the problem and comprehend the objective.

Data Collection: Compile raw data.
Data preprocessing includes handling outliers, missing values, and normalizing the data.
Feature engineering is the process of extracting significant characteristics.
Model Selection: From a variety of machine learning models, select the best algorithm.
Training: Match training data to the model.
Hyperparameter tuning is the process of using test data to optimize model parameters.
Evaluation: Make use of measurements such as recall, accuracy, and precision.
Deployment: Putting the model into production.
Monitoring & Maintenance: Depending on actual performance, continuously improve.

What Is the Null Hypothesis In The Linear Regression Problem?

The null hypothesis in linear regression states that there is no correlation between the independent and dependent variables, which translates into zero regression coefficients. A statistically significant connotation between the variables is indicated by a low p-value, which rejects the null hypothesis.

Is It Possible To Use SVMs for Both Regression And Classification Tasks?

It is possible to employ Support Vector Machines for both tasks. Support Vector Regression (SVR) identifies a best-fit hyperplane to predict continuous values while minimizing error, whereas Support Vector Classification (SVC) uses a hyperplane to divide classes.

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Additional Machine Learning Interview Questions in 2025

After getting basic, intermediate, and advanced level ML interview questions and answers for 2025.

Why May Computer Vision Tasks Have Such Large Inputs? Give An Example To Illustrate It.

Consider a 250 by 250 picture with 1000 hidden units in a completely linked concealed first layer. The weight matrix at the first unseen layer will comprise a 187,500 X 1000 dimensional matrix, while the input computer vision features for this picture are 250 X 250 X 3 = 187,500. We employ convolution procedures to address the issue of these numbers being enormous for computation and storage.

Provide A Method For Training A Convolutional Neural Network Using A Limited Dataset.

You may use transfer learning to train your model and achieve state-of-the-art outcomes if you don’t have sufficient data to train a neural network. A pre-trained model that was trained on a broader yet more comprehensive dataset is required. The last layers of the models will then be trained on more recent data to refine them.

Data scientists may use less computing power, storage space, and resources while training models on smaller data sets, thanks to transfer learning. Open-source pre-trained models for a variety of use cases are readily available, and most of them have a proper license under a commercial license, allowing you to utilize them to develop your own application. Moreover, you must prepare these machine learning interview questions in 2025.

What Is One-Shot Learning?

In machine learning, one-shot learning is the process of training a model to identify patterns in datasets using just one sample rather than extensive datasets. This is helpful when we don’t have a lot of data. You can use it to determine how similar and different the two photos are from one another.

Which Is More Resilient To Outliers: Random Forests Or Decision Trees?

Both random forests and decision trees are comparatively resistant to outliers. An aggregate of several decision trees is the result of a random forest model, which is an ensemble of several decision trees.

Therefore, the likelihood of overfitting has reduced when we average the findings. The random forest models are therefore more resilient to outliers.

What Distinguishes Entropy From Information Gain?

Entropy decreases as one gets closer to the leaf node and indicates the degree of disarray in your data. However, after dividing a dataset according to an attribute, information gain is contingent upon a decrease in entropy. The closer you are to the leaf node, the more information you acquire.

Which Categories Make Up The Sequence Learning Process?

One of the popular questions in machine learning interviews is about the sequential learning process. Moreover, sequence prediction, sequence creation, sequence recognition, and sequential judgment are the four categories.

What Are The Various Elements Of Relationship Assessment Methods?

The significance test, score metric, query type, and data gathering are important elements of relational evaluation methodologies. Moreover, the collection of ground truth and a cross-validation procedure are the other crucial elements.

Identify Two Ensemble Techniques Paradigms.

Parallel and sequential ensemble techniques are the two key ensemble method concepts.

What Elements Make Up A Program Using Bayesian Logic?

It is also one of the top machine learning interview questions in 2025. Here’s how you can answer:

There are two parts to the Bayesian logic program. With a collection of Bayesian sentences, the first part is the logical part. Moreover, the quantitative component comes in at number two. The logical component addresses the qualitative aspect of the domain.

What Are Neural Networks’ Advantages And Disadvantages?

One of the most often asked questions in machine learning interviews is this one. Performance improvements for unstructured information, such as audio, video, and photos, can be achieved by neural networks. Neural networks are more supple than other machine learning methods, which aids in pattern recognition. Neural networks’ drawbacks include the need for a lot of training data. Additionally, neural networks have limitations with regard to architectural selection and comprehension of internal layers.

Conclusion

It is clear from our examination of key machine learning interview questions that a combination of theoretical understanding, real-world expertise, and familiarity with emerging trends and technology is necessary for success in these types of interviews. Moreover, from understanding basic concepts like semi-supervised learning and algorithm selection to delving into the complexity of particular algorithms like KNN and other factors, the range is great. However, overcoming role-specific challenges including natural language processing, reinforcement learning, or computer vision. Comment below if you have any more questions you have encountered in your machine learning jobs interview.

FAQs (Frequently Asked Questions)

What Kind Of Jobs Can You Get With Machine Learning?

You can get jobs as a machine learning (ML) engineer, data scientist, AI research scientist, natural language processing (NLP) engineer, or AI product manager.

Is ML a High Paying Job?

Indeed, many people believe that working in machine learning (ML) pays well, particularly for seasoned specialists and in IT centers.

Is ML Better Than AI?

AI excels in efficiently finishing difficult human tasks. Finding patterns in big data sets to address particular issues is where machine learning excels. Moreover, numerous techniques, including rule-based, neural network, computer vision, and others, can be used in artificial intelligence.

Can You Get An ML Job With No Experience?

A solid portfolio of real-world projects and the development of fundamental abilities through alternative learning pathways are necessary to land an entry-level machine learning (ML) job without any formal experience.

Machine Learning Interview Questions: Top 50 Questions And Answers For 2025