Top 60 Machine Learning Interview Questions And Answers [2024]

Machine learning is a part of AI and computer science. It mimics human learning by enhancing its accuracy through data and algorithms. A Machine Learning engineer studies, constructs, and creates autonomous or self-running artificial intelligence systems. In addition, they create artificial intelligence algorithms that can learn and make predictions. In this blog, we have compiled Machine Learning interview questions and answers for freshers and experienced Engineers that could be great to start your preparation for interview rounds.

Responsibilities of a Machine Learning Engineer 

The responsibilities of an ML engineer vary from organization to organization. Generally, the duties of an ML engineer include the following: 

• Creating ML systems 
• Creating, researching, and implementing ML algorithms
• Analyzing data sets 
• Running Machine Learning tests 
• Improve models based on the test results 
• Training and retraining systems 
• Creating custom Machine Learning applications according to the user’s requirements 

Machine Learning Basic Interview Questions For Freshers

If you’re trying for a job in the field of AI/ML, you must be prepared with certain interview questions. Here are some basic Machine Learning interview questions for interns, freshers, and people who are just starting out in this field.

Q1. Can you explain the distinct types of Machine Learning?

There are three types of Machine Learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised Machine Learning, the model makes a decision based on the previously labeled data. 

In unsupervised machine learning, there is no labeled data present. This, input data is analyzed by a model to identify patterns, associations, and outliers.

In reinforcement Machine Learning, a model learns from previous mistakes by comparing the rewards it obtained from its prior behavior. 

Q2. Do you understand what semi-supervised Machine Learning is?

It is a blend of supervised and unsupervised learning. In this case, an algorithm is trained with a mix of labeled and unlabeled data. The labeled data marks the remaining unlabeled data for further analysis and use. 

Q3. Why was Machine Learning introduced?

Machine learning was introduced to make living easier. It automates different tasks with artificial intelligence applications and solutions. 

Q4. What is the difference between inductive and deductive learning?

In inductive learning, the model uses a set of observed instances, facts, and information to conclude. While in deductive learning, observations are formed using conclusions. 

Q5. What is the difference between data mining and Machine Learning?

Data mining is the process of analyzing structured data to uncover abstract information and unknown patterns using Machine Learning algorithms. Meanwhile, Machine Learning is a process that represents the research, creation, and development of these algorithms. It makes processors learn something without human programming.

Q6. Do you know the meaning of overfitting in Machine Learning and why it occurs?

Overfitting happens when a statistical model shows a random error instead of an underlying association. In an overfit model, the machine gives inaccurate predictions or random errors for new data sets. It usually occurs due to reasons such as:

• When we have a small training dataset and a model tries to learn from it.
• Training data contains inaccurate information.
• The model is highly complex.

Q7. How can you avoid being overfitted?

• By simplifying data
• Training with more data
• Using Data Augmentation
• Removing unnecessary features 

Q8. What is the difference between classification and regression?

Classification is used to produce discrete results. It is used to classify data as primary emails, spam and non-spam emails, males or females, and true or false emails. Regression is used when dealing with continuous data, such as predicting stock prices at a specific time or salary, etc.

Q9. What are the different stages of model building in Machine Learning?

There are three stages of model building: building, testing, and implementation.

Q10. Can you name some supervised Machine Learning applications?

There are multiple applications of supervised Machine Learning. A few significant ones are:

• Fraud identification
• Healthcare
• Sentiment analysis
• Email identification

Q11. What is deep learning?

It is a branch of Machine Learning that is relevant to neural networks. In deep learning, a system can think and learn like humans using artificial neural networks. 

Q12. Can you explain unsupervised Machine Learning techniques?

There are two techniques used in unsupervised learning:

• Clustering: It requires data to be divided into subsets, known as clusters. These subsets contain data of the same type.
• Association: In this technique, patterns of associations or relationships are discovered between different variables.

Q13. Explain a linked list and an array.

This is one of the most asked Machine Learning basic interview questions. 

Array: It is a data type widely implemented as the default type for all modern programming languages. It is used to store data of similar types.

Linked list: A linked list is an ordered group of the same type of elements that are connected using pointers. 

Q14. Explain linear regression in Machine Learning.

Linear regression is a supervised Machine Learning algorithm that is used to find a linear relationship between the dependent and independent variables for predictive analysis. The linear regression equation is Y = A + B.X.

Q15. How will you explain a decision tree in Machine Learning?

Decision trees are defined as supervised Machine Learning. In decision trees, a data set is divided according to the parameters. It develops a classification or regression model similar to the tree structure when databases are broken into smaller subsets.

Q16. What do you know about Bayesian networks?

Bayesian networks are also known as belief networks or casual networks. They are used to represent the graphical model for the probability relationship among a variable set. 

Q17. What are type 1 and type 2 errors?

Type I error is called a “false positive error”. It comes when the outcome shows a false result even though the result is true. 

Type II error is also known as a “false negative error”. It comes when the outcome of a test shows the acceptance of a false condition.

Q18. What exactly is a hypothesis in Machine Learning?

The hypothesis is a function of machine learning. It helps in approximating the target function and performs the required input-output mappings.

Q19. Explain the similarities between bagging and boosting.

• They both are used for voting.
• They improve the stability of the machine-learning model.
• They combine models of the same type.
• Both are effective at reducing variance and increasing stability.
• In both cases, the final decision is made by combining the predictions of N learners.

Q20. Explain the differences between bagging and boosting.

Bagging	Boosting
Individual models are constructed separately.	Each new model is influenced by the performance of the previous one.
Classifiers are trained in parallel.	Base classifiers are trained in this process.
Bagging should be used if the classifiers are unstable.	If the classifiers are stable, simply boost them.
The original dataset is divided into several subsets.	The new subset includes the components previously restricted by the model.
It is used to combine predictions that belong to the same type.	It is used to combine predictions that belong to different types.
It is used to reduce variance.	It is used to reduce bias.

Q21. Can you please explain bagging and boosting?

Bagging is another word for bootstrapping aggregation. It improves the strength and accuracy of Machine Learning algorithms used for classification and regression purposes. 

Boosting is a method used to reduce bias and variance. It is a family of Machine Learning algorithms that convert weak learners into strong ones. 

Q22. What do you mean by genetic programming?

A type of Machine Learning technique that includes automatic programming without any need for manual interaction is known as genetic programming. It is used in conjunction with other forms of Machine Learning. It performs symbolic regressions and feature classifications.

Q23. Explain correlation and covariance.

Correlation represents how strongly two random variables are associated with each other. The value of correlation lies between -1 and +1.

On the other hand, covariance represents the direction of the linear relationship between two random variables. The value of covariance lies between minus infinity and plus infinity.

Q24. What is more important to you, model accuracy or model performance?

There are certain models with higher accuracy that perform worse in predictive power. Therefore, there is no sense in using such models with higher accuracy when their performance level is zero. Model accuracy is a subset of model performance. Therefore, you should keep performance as your priority over accuracy.

Q25. Can you explain how you would build a data pipeline?

Data pipelines are the bread and butter of Machine Learning engineers. They take data science models and discover innovative and creative ways to automate and scale them. To build a data pipeline, one should be familiar with the tools such as Apache or Airflow and the platforms where you can easily host models and pipelines. These platforms include Google Cloud, AWS, or Azure. 

Q26. What is the meaning of Epoch in Machine Learning?

The term “epoch” is used to detect the count of passes in a training data set. It refers to the entire passing of training data through the algorithm. 

Q27. How can you explain Machine Learning to a kid?

This is one of the best machine learning interview questions for freshers. With the help of machine learning, you can teach a computer how to make decisions without any human interference. Just like we learn new things from our experiences, a computer can also learn from the data it is given. Through machine learning, the computer can figure out patterns and make predictions.

Q28. Can you please explain precision and recall?

Precision and recall retrieve data from the database. They are performance matrix data used in Machine Learning for pattern recognition and classification. These concepts are used to develop models that provide precise and accurate results.

Q29. How is KNN different from K-means?

KNN (K’s nearest neighbors) is a supervised algorithm for classification purposes. At the same time, K-means is an unsupervised algorithm mainly used for clustering. 

Q30. What is the difference between Machine Learning and deep learning?

Machine Learning is an application of artificial intelligence that allows the system to learn from experience and improve without being programmed. 

Deep learning is a subset of Machine Learning where the artificial neural network and the recurrent neural network come together. It replicates just like the human brain. 

Also Read: Deep Learning Vs Machine Learning

Q31. Explain the confusion matrix in Machine Learning algorithms.

A confusion matrix is a specific table used to measure the performance of an algorithm. The matrix has two parameters: actual and predicted. To understand the concept, consider this confusion matrix: 

	Yes	No
Yes	12	3
No	1	9

For actual values (vertical): Yes = 12 + 1 = 13; No = 3 + 9 = 12.

Similarly, for predicted values (horizontal): Yes = 12 + 3 = 15; No = 1 + 9 = 10.

To make a model accurate, the values at the diagonal should be high.

This matrix contains a total of 25 observations.

Q32. What is a false positive or false negative, and why are they important?

False positives and false negatives in Machine Learning refer to the results incorrectly predicted by a model. They are important as they can have serious consequences in applications such as medical diagnosis and fraud detection. Hence, it is important to minimize them to improve model accuracy and performance.

Q33. How do you handle missing or corrupted data in a dataset?

It includes actions like dropping missing data or filling in missing values with a constant or calculated value. We may also use interpolation or Machine Learning algorithms to predict missing values. The choice of technique depends on the type of dataset we are dealing with.

Q34. What are the different functions of supervised and unsupervised learning?

The functions of supervised learning are:

• Classification
• Speech recognition
• Regression
• Annotate strings
• Time series prediction

Functions of unsupervised learning include:

• Discovering data patterns
• Methods for representing data into different parameters
• Investigate the data for potentially valuable insights
• Searching for unusual evidence present in the database
• Exploring potential correlations and coordinates

Q35. What are bias and variance in Machine Learning?

Bias: Bias is the difference between the average prediction value and the actual correct value. The lower the bias value, the better performance a model offers.

Variance: A variance is a number that tells the difference between the prediction over a training set and its anticipated value. High variance leads to large output fluctuations. 

Q36. What is PCA in Machine Learning?

PCA stands for Principal Component Analysis. It is a popular unsupervised learning technique for reducing the dimensionality of data. It is used to visualize multidimensional data. Its purpose is to discover a collection of uncorrelated dimensions and rank them based on variance.

Q37. Can you please explain the SVM algorithm?

SVM is a powerful and versatile supervised Machine Learning model that can perform linear or non-linear classification and regression. For example, we have a set of data points, and each belongs to a certain class. The goal is to separate these points into classes.

In this algorithm, a data point is viewed as a p-dimensional vector where we can easily separate points with a p-1-dimensional hyperplane. This is known as a linear classifier. There are many hyperplanes used in the algorithm to divide data into classes. The best can be chosen based on the present data.

Q38. What is cross-validation?

Cross-validation is a method of dividing all the present data into three parts: training, testing, and validation data.

Advanced Machine Learning Engineer Interview Questions

Following are some advanced Machine Learning engineer interview questions.

Q39. What is Machine Learning bias?

A bias in the data represents the inconsistency of the data. The inconsistency can occur for several reasons that are not mutually exclusive. If you are seeking a career in Machine Learning, you can opt for this in-depth Machine Learning course.

Q40. What is a neural network?

A neural network is a kind of human brain. It has neurons linked together in the same way that the human brain does.

It recognizes patterns and solves common problems in Machine Learning, artificial intelligence, and deep learning without the intervention of humans.

Q41. What is ensemble learning?

Ensemble learning combines multiple Machine Learning models to develop more powerful models that can work with great efficiency.

Q42. How do you make certain that the best Machine Learning algorithm is selected?

It depends on the data sets we have. If the data is discrete, then the SVM algorithm is great. If it is continuous, then linear regression is a good option.

Based on the analysis, such as whether the variables are continuous, categorical, or discrete, one can choose the best-fit algorithm for a particular data set.

Q43. Explain the random forest and how it works.

Random forest is a versatile machine-learning method capable of performing both classification and regression tasks. It works like bagging and boosting, in which data sets are combined. It builds a tree from a random sample of columns in the data set for testing. 

Q44. Explain the variance in the inflation factor.

The variance inflation factor is an estimate of the volume of multicollinearity in a collection of many regression variables. 

Q45. Describe your favorite Machine Learning algorithm.

Some of the best algorithms that you can include in your answer are linear regression, logistic regression, decision trees, naive Bayes, etc. Remember that it is not important which algorithm you mention in your answer; the reasons why you selected it should be described in the best way possible. Be sure to show your expertise in that area.

Q46. What do you mean by “naive” in the “naive Bayes” classifier?

It makes assumptions that may or may not turn out to be true. In this algorithm, it has been assumed that the feature present in one class is not related to the other features given in another class. 

For example, you can describe a fruiting cherry in terms of its red color and round shape while ignoring the other characteristics of the fruit. However, this assumption may or may not be correct because an apple matches the same description as well. Therefore, the naive assumption can be right or wrong based on the condition applied.

Q47. What method will you choose to know which Machine Learning algorithm is the best fit for a classification problem?

There is no strict rule for choosing an algorithm for a classification problem. However, here are some guidelines that you can use.

• Choose different algorithms and cross-validate them if accuracy is the primary goal.
• If the training data set is small, models with a high bias and low variance can be used. 
• If the training data set is large, you can use models with a high variance and a low bias value.

Q48. Do you know how the Amazon engine works to recommend other things to buy?

When a user buys anything from Amazon, the company stores the purchase data for future reference. It also finds other related products that the person might be interested in buying in the future. It is possible with the association algorithm that identifies the patterns in the given data set. It helps big organizations increase their sales in the future.

Q49. In what cases can you use classification over regression?

Classification is used when the target is categorical, and regression is used when the target value is continuous. When we want to predict a categorical variable, i.e., the output is one of a limited number of classes. In this case, we use classification. 

When we want to predict a continuous variable, i.e., the output may be a real number or a set of real numbers. In this case, we use regression. 

Q50. How can you generate revenue using Machine Learning skills?

There are several ways in which machine learning skills could generate revenue. These include: developing predictive models for businesses, building recommendation systems, creating chatbots, building fraud detection systems, and developing personalized marketing campaigns. The options are endless, you just need a unique idea to bring it to life.

Q61. What is the difference between the Training dataset and the Test dataset?  

Training Dataset	Test Dataset
The training set is a subset of the original data.	It is another subset of the original dataset but unrelated to the training dataset.
It is meant to train the machine learning model.	It checks for the accuracy of the model.
The training data is larger in size.	It is small compared to the training set.

Q62. How is linear regression performed?

Linear regression analysis is about more than just finding the best linear fit for a set of data points. It consists of three main phases:

• Examining the data for correlation and directionality.
• Finding the best-fit line to estimate the model.
• Evaluating the applicability and validity of the model.

Q63. What is the function of a Recommender System?

A recommendation engine anticipates users’ preferences and suggests products based on explicit user ratings, search engine queries, and purchase histories. It uses relevant information about users and items to create a personalized recommendation system.

Q64. What exactly are kernels in SVM? Name some of the kernels used in SVM.

The function of a kernel is to receive data input and transform it into the desired form. Some common kernels used in SVM are Linear, Sigmoid, Polynomial, Hyperbolic, Laplace, etc.

Q65. What does the SVM kernel trick mean?

The SVM kernel trick is a mathematical formula that uses data points to determine classification ranges between two classes. Classifiers are created based on linear or radial functions that depend on the data distribution.

Q66. What is the advantage of using Kernel SVM?

SVM algorithms provide complexity advantages since they can build non-linear decision surfaces and can be used with the kernel trick. They outperform logistic regression in many cases and are computationally cheaper, with a classifier that only depends on a subset of points, as opposed to logistic regression, which requires O(N^3). This makes SVM a more practical choice for maximizing the distance between the closest points in two classes.

Q67. How to manage overfitting and underfitting?

To tackle overfitting, resample the data and evaluate model accuracy using approaches such as k-fold cross-validation. On the other hand, underfitting can be managed by either fitting more data points into the model or changing the algorithm.

Q68. What are the different types of clustering methods in Machine Learning?

• Partitioning Clustering
• Density-Based Clustering
• Distribution Model-Based Clustering
• Hierarchical Clustering
• Fuzzy Clustering

Q69. What are Parametric and Non-Parametric models?

Parametric Model	Non-Parametric Model
It has fixed parameters and hence, requires lesser data to build the model.	It uses any number of parameters and requires more data to build a model.
It handles interval data and ratio data.	It can handle only the original data.
Parametric analysis tests the mean of the group and is applicable only to variables.	Non-parametric analysis tests the medians and is applicable to both variables and attributes.

Q70. What is the difference between Sigmoid and Softmax functions?

Sigmoid Function	Softmax Functions
This function is used for binary classification in the logistic regression method.	This function is used for a multi-classification logistic regression model.
It receives only one input and the output is a single number.	It is vectorized and produces a vector with entries indicating class probabilities.

Conclusion

The world has changed a lot with the introduction of artificial intelligence and machine learning. So, having a career in these fields is great, but before you go for an interview, be sure to prepare yourself. It is crucial to know what kind of machine learning interview questions a recruiter can ask. This blog will help you cover the major machine learning questions.