Machine learning is one of the formidable technological advancements in recent years. The popularity of machine learning has major support for a change in the focus of organisations on data-driven decisions. And so the rate of individuals preparing for Machine Learning interviews is also increasing. But here are the top 50 Q&As to crack your ML interview in 2025.
Why has Machine Learning Caught the Hype?
Since the technological perspective of machine learning is evolving gradually, the interview process also involves certain changes. A few years back, knowledge about designing a convolutional network could have granted you access to promising jobs in machine learning. However, times have changed. Machine learning now places wider expectations on algorithms, probability, statistics, data structure, and many more. Therefore, candidates need comprehensive preparation with machine learning tools and top machine learning interview questions.
As we all know, machine learning and data science are closely related disciplines. A machine learning engineer is one of the top job roles in machine learning and data science. So, our attention on the top machine learning interview questions is not futile. In 2019, machine learning engineers can earn $146,085 on average per year with a splendid annual growth rate of 344 per cent. Therefore, rapid growth in salary and opportunities for promising job roles implies the need for better preparation for machine learning interviews.
Machine Learning Interview Questions for Data Engineers
Check out these questions and answers for Machine Learning Interview to crack Data Engineering roles.
- What is a Bias error in ML algorithms?
Candidates with experience in data engineering can find this entry in the latest machine learning interview questions. Bias is the general error in ML algorithms primarily because of simplistic assumptions. As the name implies, Bias error involves negligence for certain data points, thereby resulting in lower accuracy. Bias error is responsible for complicating the process of generalising knowledge from the training set to the test set.
2. What is the meaning of Variance Error in ML algorithms?
Variance error is found in machine learning algorithms that are highly complex and pose difficulties in understanding them. As a result, you can find a greater extent of variation in the training data. Subsequently, the machine learning model would overfit the data. In addition, you can also find excessive noise in the training data, which is completely inappropriate for the test data.
3. Can you define the bias-variance trade-off?
Bias-variance trade-off is one of the top machine learning interview questions for data engineers. Bias-variance trade-off is the instrument for managing learning errors as well as noise caused by the underlying data. The trade-off between bias error and variance error can increase the complexity of the model. However, you can also observe a considerable reduction of errors with the bias-variance trade-off.
4. How can you differentiate supervised from unsupervised machine learning?
Supervised learning implies the requirement of data in a labelled form. An instance of supervised learning is labelling data and classifying it when you have to categorise the data. However, unsupervised learning does not require any form of explicit data labelling. This simple point can separate supervised learning from unsupervised learning quite easily. Candidates could easily expect this question among the latest machine learning interview questions.
5. What is the difference between a k-nearest algorithm and k-means clustering?
This is one of the frequently asked machine learning interview questions for data engineers. The k-nearest algorithm comes under the scope of supervised learning, and the k-means clustering comes under the scope of unsupervised learning. Both techniques appear similar in terms of appearance, but the difference lies in the two technologies related to supervised and unsupervised learning.
The K-nearest algorithm implies supervised learning, thereby suggesting the need for labelling data explicitly. On the other hand, K-means clustering does not require any form of data labelling. Therefore, you can implement any technology based on the needs of a project.
6. What is the ROC curve, and how does it work?
The Receiver Operating Characteristic (ROC) curve represents the contrast level between false-positive rates and true-positive rates. The estimates of true and false positive rates are taken at multiple thresholds. The ROC is ideal as a proxy for measuring trade-offs and sensitivity associated with a model. According to the measurements of sensitivity and trade-off, the curve can trigger false alarms.
7. What is the importance of Bayes’ theorem in ML algorithms?
Candidates should have adequate preparation for such frequently asked machine learning interview questions in data engineer interviews. Bayes’ theorem can help in measuring the posterior probability of an event according to previous knowledge. Bayes’ theorem can inform us about the true positive rate of conditions after division by the total of false rates. The formula for Bayes’ theorem is,
P (A|B) = [P(B|A) P(A)] / [P(B)]
Bayes’ Theorem is an ideal instrument in mathematics for the calculation of conditional probability. A renowned mathematician named Thomas Bayes was the creator of this theorem. Many people can find Bayes’ theorem confusing. However, it also helps for in-depth understanding and gaining productive insights regarding a topic
8. What is precision, and what is a recall?
The recall is the number of true positive rates identified for a specific total number of datasets. Precision involves predictions for positive values claimed by a model as compared to the number of actually claimed positives. You can assume this as a special case of probability in mathematics.
9. Can you explain the difference between L1 and L2 regularisation?
Candidates can face this question in their interview as it’s one of the latest machine learning interview questions. L2 regularisation is more likely to transfer error across all terms. On the other hand, L1 regularisation is highly sparse or binary. Many variables in L1 regularisation involve the assignment of 1 or 0 in weighting to them. The case of L1 regularisation consists of the setup of a Laplacian before the terms. In the case of L2, the focus is on the setup of a Gaussian prior on the terms.
10. What is Naive Bayes?
Naive Bayes is ideal for practical application in text mining. However, it also involves an assumption that it is not possible to visualise real-time data. Naive Bayes involves the calculation of conditional probability from the product of individual probabilities of different components. The condition in such cases would imply complete independence for the features that are practically impossible or very difficult. Candidates should expect this type of follow-up machine learning interview question.
Machine Learning plays a vital role in business, being an application of Artificial Intelligence, brings so many benefits with Big Data.
Machine Learning Interview Questions for Data Scientists
The role of a data scientist is truly skill-based and related to big data analysis with machine learning. In this category, we have focused on machine learning interview questions for data scientists.
- What is the F1 score, and how to use it?
F1 score measures the performance of machine learning models. It is the weighted average of precision and recall of a specific machine learning model. The results can vary from a scale of 0 to 1, with 1 as an indicator of best performance. The applications of the F1 score are ideal for classification tests, which do not focus on true negatives very much.
2. Is it possible to manage an imbalanced dataset? If yes, how?
This is probably one of the toughest machine learning interview questions in data scientist interviews. The imbalanced dataset is found in cases of classification tests, and the allocation of 90% of the data in one class. As a result, you can encounter problems. Without any predictive power over the other data categories, the accuracy of around 90% could be skewed. However, it is possible to manage an imbalanced dataset.
You can try collecting more data to compensate for the imbalances in the dataset. You could also try resampling the dataset to correct imbalances. Most important of all, you could try another completely different algorithm on the dataset. The important factor here is the understanding of the negative impacts of an imbalanced dataset and approaches for balancing the irregularities.
3. How is Type I error different from Type II error?
Don’t panic when you find such a basic question in an interview for data scientists. As the interviewer tests your knowledge of basic ML concepts, A Type I error is classified as a false positive, and a Type II error is classified as a false negative. It means that claiming something happened when it hasn’t been classified as a Type I error.
On the other hand, a Type II error is the opposite. Type II error happens when you claim something is not happening when it is happening. The Type I error is more like informing a man he is pregnant. On the other hand, a Type II error is like telling a pregnant woman that she doesn’t carry a baby.
4. Do you know about the Fourier transform?
Candidates can also find the latest machine learning interview questions on the Fourier transform in their data scientist interview. The Fourier transform is a common tool for breaking down generic functions into a superposition of symmetric functions. Simply put, it’s like figuring out the recipe from a dish served to us.
The Fourier transform helps in finding out the set of cycle speeds, phases, and amplitudes for matching any particular time signal. The Fourier transform converts the signal from the time domain to the frequency domain, which makes it easy to extract features from audio signals and other series, like sensor data.
5. What is the difference between deep learning and machine learning?
This is one of the common machine learning interview questions that you can find in almost every list. Deep learning develops as a subset of machine learning and involves a prominent relationship with neural networks. Deep learning involves the use of backpropagation and specific principles of neuroscience.
The applications of deep learning help in the accurate modelling of massive sets of semi-structured or unlabeled data. Deep learning provides a representation of the unsupervised learning algorithm. In contrast to other machine learning algorithms, deep learning uses neural networks for learning data representations.
6. How is the generative model different from the discriminative model?
The generative model will review the data categories. However, a discriminative model would review the difference between various data categories. Generally, discriminative models have better performance than generative models in classification tasks.
7. Is model accuracy important or model performance?
This question is ideal for testing the fluency of an individual regarding the achievement learning model performance. Models with higher accuracy could not perform well in terms of predictive power. How does this happen? Generally, the model accuracy is a subset of model performance, and it can also be misleading at certain times. If you have to detect fraud in large datasets with a sample of millions, the more accurate model would not predict any fraud.
This condition is possible in only a large minority of cases, involving fraud. As for a predictive model, this condition would be inappropriate. Imagine that a model designed for fraud shows that there is no sign of fraud! Therefore, we can ascertain that model accuracy is not the sole determinant of model performance. Both elements are highly significant in machine learning.
8. In which situation is classification better than regression?
Classification results in the generation of discrete values and a dataset according to specific categories. On the other hand, regression provides continuous results with better demarcations between individual points. Classification is better than regression when you need the results to reflect the presence of data points in explicit categories in the dataset. Classification is better if you just want to find whether a name is female or male. Regression is ideal if you want to find out the correlation between the name with male and female names.
9. Can you provide an example of using ensemble techniques?
Ensemble technique involves a combination of learning algorithms for optimisation of improved predictive performance. Ensemble techniques help in reducing overfitting issues in models. As a result, the model becomes more robust and faces fewer chances of influence from trivial changes in training data.
10. Explain your favourite algorithm in less than a minute.
You can also come across such machine learning interview questions based on your experience. In the case of such questions, you need to develop skills for explaining the complex technical aspects of algorithms. Most important of all, you have to maintain your poise and present your summary briefly and quickly. Ensure that you give explanations for other algorithms to the interviewer for a better advantage. In addition, you should also make sure that even a five-year-old could understand your explanation of machine learning algorithms.
Machine Learning Interview Questions for Freshers
The next category in the list is Machine Learning interview questions for freshers. The interviewer mainly asks some simple questions to the fresher candidate in order to check their basic knowledge and understanding of machine learning concepts. Not only freshers but the experienced candidates should prepare themselves with these questions, so let’s move to the questions.
- What is machine learning?
This is the most basic question that you can find generally at the start of every machine learning interview. Machine learning is a computer science discipline that relates to the use of system programming for automatic learning and improvement. The basic idea in machine learning is to predict suitable actions in specific scenarios based on experience. Robots perform tasks based on data from their sensors.
2. How is data mining different from machine learning?
Machine learning studies, designs, and develops algorithms that help computers to learn without explicit programming. Data mining is all about observing patterns or knowledge extraction from unstructured data. Data mining uses machine learning algorithms to finish tasks.
3. What are the types of machine learning?
There are three types of machine learning: supervised, unsupervised, and reinforcement learning. Supervised learning uses labelled data. Unsupervised learning involves training models using unlabeled data or without proper guidance.
The model has to find patterns and relationships automatically from a dataset by creating clusters. Reinforcement learning depends on an agent for interaction with the environment through certain actions that help in recognising rewards or errors. So, you can assume reinforcement learning as the ‘hit and trial’ method for machine learning.
4. Define overfitting in machine learning.
Overfitting happens in machine learning in scenarios where the statistical model does not describe the underlying relationship. On the contrary, the model describes random error and noise in overfitting. Overfitting is common in cases of highly complex models due to the excessive parameters than several training data. Models with overfitting generally tend to show poor performance.
5. Name the five most popular machine learning algorithms.
The most popular machine learning algorithms are decision trees, probabilistic networks, and neural networks. The other two popular ML algorithms are support vector machines and neural networks, or backpropagation networks.
6. What are the different approaches to machine learning?
This question is one of the most common machine learning interview questions for freshers. Candidates can present the response in the form of three distinct approaches. The first approach refers to the concept vs. classification learning. The second approach refers to inductive vs. analytical learning. The third approach for machine learning refers to symbolic vs. statistical learning.
7. Can you outline the functions of supervised and unsupervised learning?
Supervised learning performs functions such as classification, speech recognition, regression, time series prediction, and string annotation. On the other hand, unsupervised learning performs functions such as identifying data clusters. In addition, unsupervised learning also helps in finding low-dimensional data representations. Unsupervised learning also helps in finding interesting directions in data, as well as novel observations or database cleaning requirements. Unsupervised learning also identifies interesting coordinates and correlations.
8. Which areas can implement pattern recognition?
Candidates should find this entry among the frequently asked machine learning interview questions for freshers. Pattern recognition is applicable in areas such as computer vision, data mining, and speech recognition. In addition, you can also find its applications in bioinformatics, statistics, and information retrieval.
9. What is dimension reduction in Machine learning?
Dimension reduction refers to the process of reducing random variables with considerations. As a result, the random variables are found in two groups such as feature selection and feature extraction. Dimension reduction is an important concept in machine learning as well as statistics. Feature extraction techniques such as PCA, ICA, and KPCA are ideal for dimensionality reduction. PCA denotes Principal Components Analysis. ICA denotes Independent Component Analysis. KPCA stands for Kernel-based Principal Component Analysis.
10. Name some methods for Sequential Supervised Learning.
The different ideal methods for Sequential Supervised Learning include sliding-window methods and recurrent sliding windows. In addition, Hidden Markov models and Maximum Entropy Markov models help in sequential supervised learning. The other two methods involve conditional random fields and graph transformer networks.
Machine Learning Interview Questions for Experienced
Let’s move ahead – here, let’s see Machine Learning Interview questions for experienced candidates. With considerable experience in machine learning, candidates are asked some deeper and difficult questions. Prepare yourself for these most common machine learning interview questions for experience.
1.Explain the differences between a linked list and an array.
Candidates will generally find this mentioned among machine learning interview questions for experienced professionals. A linked list contains a series of objects with pointers for guidance on sequential processing. On the other hand, an array is simply an organised collection of objects. Every element in an array follows the assumption of the same size, while a linked list does not have this trait.
Organic growth in a linked list is easier than in n array, which can require pre-definition or re-definition. Shuffling of a linked list involves less memory consumption due to pointers. On the other hand, the reshuffling of an array can consume more memory.
2. What is a hash table?
A hash table is a data structure meant for the production of a supporting array. The hash function helps in mapping a key to certain values. Hash tables are ideal for different tasks, such as database indexing.
3. How can you implement a recommendation system for a company’s users?
You can find different interview questions like this commonly for experienced machine learning candidates. You have to deal with these machine learning interview questions by referring to the use of machine learning models. The best response would be to identify the company’s problems through in-depth research on the company and industry.
Some of the important data points for the machine learning models to develop a recommendation system are important here. Revenue drivers of the company or the types of users in the company can provide the required data points.
4. How is gradient descent (GD) different from Stochastic gradient descent (SGD)?
Each algorithm helps in identifying a set of parameters for reducing a loss function. This happens through the evaluation of parameters against data, followed by adjustments. In the case of standard gradient descent (GD), evaluation of all training samples in each set of parameters happens.
So, you will take big yet slower steps towards the solution. The stochastic gradient descent (SGD) involves the evaluation of one training sample for a set of parameters before their updates. This seems similar to taking small yet quick steps towards the solution.
5. What is the use of the Box-Cox transformation?
Box-Cox transformation is a type of power transformation that helps in the transformation of data for normalising distribution. Box-Cox transformation is also ideal for stabilising variance by eliminating heteroskedasticity.
6. Name three data preprocessing techniques for managing outliers.
Candidates can find this entry in machine learning interview questions for experienced candidates. The first technique involves Winsorizing or capping at the threshold. The second technique involves Box-Cox transformation for reducing skew. The third technique involves removing outliers that are measurement errors or anomalies.
7. What is the right amount of data to allocate for training, validation, and test sets?
Candidates will get this entry from the top machine learning interview questions for experienced professionals. The exact amount is not possible as we have to find the perfect balance. In the case of the too-small test set, we can have unreliable estimates for model performance.
In the case of the excessively small training set, actual model parameters can have high variance. The best-recommended practice, in this case, is the 80/20 or train/test split. Subsequently, you can split the train set into train/validation splits or partitions to ensure cross-validation.
8. How can you select a classifier based on training set size?
In the case of a small training set, high-bias and low-variance models show better performance. The sole reason is that such models do not overfit easily. In the case of a large training set, low-bias and high-variant models show better performance. This is possible because of their capability to reflect more complex relationships.
9. What is Latent Dirichlet Allocation (LDA)?
LDA is one of the common topics in machine learning interview questions on unsupervised learning. LDA is a general method for topic modelling as well as classification of documents according to the subject matter. It is a generative model providing representation for documents as a combination of topics with their probability distribution. LDA involves documents that are distributions of topics, which, in the first place, are distributions of words.
10. How could you improve the efficiency of our marketing team?
You shall always depend on the type of company to answer such a question. The first recommendation could be the use of clustering algorithms for building custom customer segments. The second recommendation involves the prediction of conversion probability based on the user’s website behaviour. The next recommendation could be natural language processing for headlines to predict performance.
Machine learning has revolutionised the world, and the cloud computing field is not untouched by it. If you are a data engineer on the Google Cloud Platform, get familiar with Machine Learning on the Google Cloud Platform.
Other Machine Learning Interview Questions
Whether you are a fresher, an experienced data engineer or a data scientist, you may come across these machine learning interview questions. Do check it out.
- What is the difference between inductive and deductive learning?
Inductive learning involves using observations to reach conclusions. Deductive learning involves concluding based on observations.
2. What is the difference between Information Gain and Entropy?
Entropy shows the level of disorganisation in your data and reduces with increasing proximity to the leaf node. Information gain depends on a reduction in entropy following the splitting of a dataset on an attribute. Information gain increases with proximity to the leaf node.
3. What are the different categories in the sequence learning process?
The sequential learning process is one of the common topics in machine learning interview questions. The four categories include sequence prediction, sequence generation, sequence recognition, and sequential decision.
4. What are the different components of relational evaluation techniques?
The significant components of relational evaluation techniques include data acquisition, query type, significance test, and scoring metric. The other important components include a cross-validation technique and ground truth acquisition.
5. Name two paradigms in ensemble methods.
Answer: The two important paradigms of ensemble methods are parallel and sequential ensemble methods.
6. What are the components of a Bayesian logic program?
The Bayesian logic program involves two components. The first component is the logical component, containing a set of Bayesian clauses. The second component is the quantitative component. The logical component deals with the qualitative aspect of the domain.
7. What are the pros and cons of neural networks?
You cannot miss this entry among common machine learning interview questions. Neural networks can provide performance breakthroughs for unstructured datasets like video, images, and audio. The higher flexibility in neural networks helps in learning patterns better than other ML algorithms. The cons of neural networks imply the requirement for a large amount of training data. Furthermore, neural networks also have setbacks in terms of selecting architecture and understanding of underlying internal layers.
8. What are the pros and cons of decision trees?
Answer: Decision trees help in easy interpretation and a limited number of parameters for tuning. Decision trees are nonparametric, and hence they are not vulnerable to outliers. On the other hand, decision trees are highly vulnerable to overfitting. However, you can choose ensemble methods such as boosted trees or random forests to deal with such issues.
9. Can you explain bagging?
Answer: Bagging is the short form for bootstrap aggregating. Bagging is a meta-algorithm that takes M subsamples from the initial dataset as inputs. Subsequently, the algorithm trains a predictive model on the subsamples. The final model is a product of averaging bootstrapped models and provides better results.
10. What is a recommendation system?
Answer: A recommendation system is a subclass of the information filtering system for predicting the preference that a user would assign to an item. The best techniques for a recommendation system are collaborative filtering and content-based filtering.
Final Words
We have brought the major questions and answers for you to crack your Machine Learning Interview. Based on the category, we have listed the important ones. The blog outlines the best practice approach for responding to each question. However, you need to expand the scope of your preparation and research further to find out additional ML interview questions. If you are a Machine Learning engineer or data engineer on any platform, AWS, GCP,Databricks, enrol now for the Machine Learning courses in Whizlabs. We have a set of practice tests, video content, Hands-on labs and sandboxes to ease your preparation for certification and interview. What more? With the necessary bit of effort and dedication, a promising career is never far away! Get started now.
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