I met an old friend a few weeks ago who is an expert in AI and machine learning. We started talking about how interviews for AI and ML jobs have changed over coffee. He said that most candidates get ready for interviews by memorizing answers, but they forget that interviewers want to know how you think.
He gave me a list of 25 questions that come up a lot in interviews while we were talking. He not only told us what the questions meant, but also how to answer them in a smart and organized way. This article was inspired by that talk.
I have put together a list of 25 AI and Machine Learning interview questions along with some tips on how to answer them.
These questions will help you think and answer better in your next interview, whether you are a new graduate, an AI engineer, or getting ready for a viva.
How to Approach AI and ML Interviews
Before we get to the questions, it’s important to know how to handle an interview in this field. Interviewers want to see that you can solve problems, think clearly, and understand the main ideas.
They want to know if you can explain things in a way that anyone can understand and use them to solve problems in the real world. This is the base of AI and Machine Learning interview questions.
If you’re getting ready for ML interview questions for freshers, focus on the basics. Find out how algorithms work and where they are used. If you want to do well on advanced AI engineer interview questions, practise explaining your projects and why you made the choices you did.
The most important thing is to talk out loud. Show the interviewer how you think and how you link different ideas together.
25 AI and Machine Learning Interview Questions (and How to Answer Them)
Below are 25 common interview questions on machine learning and AI. After each one, you will find a note on how to approach or structure your answer.
1. What is Machine Learning and how is it different from Artificial Intelligence?
How to answer: Start with simple definitions. Explain that AI is a broad field focused on creating intelligent systems, while Machine Learning is a part of AI that uses data to learn patterns. Give one short example.
2. What are the main types of Machine Learning?
How to answer: Mention supervised, unsupervised, and reinforcement learning. Briefly explain how each works and what kind of problems they solve.
3. What is supervised learning?
How to answer: Say that it uses labeled data. Explain that the model learns from examples where both input and output are known.
4. What is unsupervised learning?
How to answer: Explain that it uses data without labels. Talk about clustering or grouping similar data points.
5. What is reinforcement learning?
How to answer: Explain that it involves learning from feedback through rewards or penalties. Mention examples like gaming or robotics.
6. Explain overfitting and underfitting.
How to answer: Describe overfitting as when a model performs well on training data but poorly on new data. Underfitting is the opposite. Mention that the goal is to find a balance using techniques like regularization.
7. What is a confusion matrix and how is it useful?
How to answer: Explain that it is a table showing model performance on classification tasks. Mention terms like precision, recall, and accuracy.
8. How do you handle missing data in a dataset?
How to answer: Mention different strategies such as filling missing values with averages, using predictive models, or removing incomplete rows.
9. What is the bias and variance trade-off?
How to answer: Define both terms and explain that good models balance them. Too much bias means oversimplification. Too much variance means overfitting.
10. What are activation functions in neural networks?
How to answer: Explain that activation functions decide how signals move through the network. Mention examples like ReLU, Sigmoid, and Tanh.
11. What is gradient descent?
How to answer: Explain it as an optimization method that helps the model reduce errors by adjusting weights in small steps.
12. What is regularization and why is it important?
How to answer: Say that regularization helps prevent overfitting by adding penalties to large coefficients. Mention L1 and L2 regularization.
13. Explain the difference between classification and regression.
How to answer: Classification predicts categories. Regression predicts continuous numbers. Give one example for each.
14. What is deep learning and how is it related to Machine Learning?
How to answer: Say that deep learning is a type of ML that uses neural networks with multiple layers to learn complex patterns.
15. What are convolutional neural networks (CNNs)?
How to answer: Explain that CNNs are used for image-related tasks. They extract features from images using filters and layers.
16. What is natural language processing (NLP)?
How to answer: Describe NLP as a field of AI that teaches machines to understand human language. Give examples like chatbots and translation tools.
17. What is transfer learning?
How to answer: Explain that it uses knowledge from one model to improve another task. Mention that it saves time and data.
18. What are hyperparameters?
How to answer: Explain that these are model settings defined before training. Examples are learning rate, batch size, or number of layers.
19. What is the difference between batch and online learning?
How to answer: Batch learning trains the model on all data at once. Online learning updates the model as new data arrives.
20. What is the purpose of cross-validation?
How to answer: Explain that it helps test the model on different data splits to ensure it performs well in general.
21. How do you evaluate a regression model?
How to answer: Mention metrics like Mean Absolute Error, Mean Squared Error, and R-squared score.
22. What are some common ML algorithms?
How to answer: List a few like Linear Regression, Decision Trees, Random Forest, and Support Vector Machines.
23. What are AI ethics and why are they important?
How to answer: Explain that AI ethics ensure fairness and transparency. Mention responsible use of data and bias control.
24. How can you explain your project to a non-technical person?
How to answer: Use simple examples and focus on results, not code. Mention how your model solves a real-world problem.
25. How can you stay updated with AI and ML advancements?
How to answer: Suggest reading research papers, following AI communities, and experimenting with new tools like TensorFlow, PyTorch, and Hugging Face.
How to Practice for AI ML Interviews
Don’t just read the questions to get ready. Use open datasets to do small projects. Practice explaining your project in a way that is easy to understand. When you study for ML viva questions. make sure you can explain what each algorithm does and when to use it.
People who are new to machine learning should start with basic machine learning interview questions and then move on to more difficult ones. AI engineers should practice explaining real-world examples like chatbots, recommendation systems, or computer vision projects.
Do practice interviews with friends. This helps you learn how to organize your thoughts and feel more sure of yourself.
Quick Recap Table
| Category | Question Example | What to Focus On |
|---|---|---|
| Basics | What is ML? | Simple definitions and examples |
| Algorithms | Explain KNN or SVM | Logic and use case |
| Model Evaluation | What is confusion matrix? | Performance metrics |
| Deep Learning | What is CNN? | Structure and application |
| Applied AI | How does NLP work? | Real-world examples |
You should also read this What Is the Main Goal of Generative AI?
Bonus Tips from My AI Expert Friend
My friend gave me some very helpful advice after we finished talking.
He said that clarity should always be your goal. Interviewers want to know if you really get what you’re saying.
Don’t try to remember definitions. Instead, figure out the logic and put it in your own words. Use short examples from your college work or experiments you’ve done on your own.
If someone asks you a question you don’t know the answer to, be honest and tell them how you would find the answer.
He also told me that a lot of “AI interview questions” are more about how well you can explain something than what you know. People always like simple, confident answers better than long, complicated ones.
Final Thoughts
That conversation with my friend changed how I think about interview preparation. He showed me that doing well in AI and Machine Learning interview questions is not about having every answer memorized. It is about understanding the ideas behind them and expressing them clearly.
If you can explain concepts simply, with confidence and examples, you already stand out from the crowd. Keep practicing, stay curious, and keep learning. The more you understand how AI and ML actually work, the easier your interviews will become.
FAQs on AI and Machine Learning Interview Questions
1. Are these questions enough for freshers?
Yes. These 25 questions cover the most common AI and Machine Learning interview questions and basic AI concepts.
2. How should I prepare for an AI ML interview?
Review theory, practice coding, and go through past interview questions on machine learning. Practice explaining your logic step by step.
3. Are ML viva questions and interview questions the same?
They are similar. Viva questions often focus on short, direct explanations instead of detailed coding answers.
4. Will there be an article with answers too?
Yes. In the next part, we will cover model answers to the most asked AI and Machine Learning interview questions.
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