Machine Learning Interview Questions & Answers

In today’s fast-paced business landscape, the adoption of innovative technologies has become essential for organizations to stay competitive. Artificial intelligence (AI) and machine learning are at the forefront of this revolution, enabling businesses to enhance the accessibility of their services and information to consumers. With the rising acceptance of these game-changing technologies across diverse industries such as banking, finance, healthcare, retail, and manufacturing, the demand for skilled professionals is also increasing rapidly. 

Machine Learning Interview Questions & Answers

Among the sought-after roles are data scientists, artificial intelligence engineers, machine learning engineers, and data analysts who are driving innovation through their expertise. As a job seeker, understanding the potential machine learning interview questions that hiring managers and recruiters may ask is crucial if you aspire to land a position in these fields.

Let’s Discuss Interview Questions for Machine Learning  

This post walks you through some of the machine learning interview questions and responses you may run against while pursuing your ideal position.

Here are a few questions and answers that are frequently asked about machine learning during interviews.

Q1. Name three types of machine learning 

Supervised Education : 

In supervised machine learning, predictions or choices are made by a model using historical or labeled data. Data sets that have been given tags or labels and so become more meaningful are referred to as labeled data.

Unsupervised Education :

Without labeled data, unsupervised learning is impossible. A model can spot trends, oddities, and connections in the supplied data.

Learning through Reinforcement : 

The model can learn by using reinforcement learning and the rewards it got for its prior behavior.

Q2. What is machine learning that is semi-supervised?

Unsupervised learning lacks any training data, whereas supervised learning makes use of fully labeled data.

In the case of semi-supervised learning, the training data is made up primarily of unlabeled data, and only a tiny portion of it is tagged.

Q3. In machine learning, what does “overfitting” mean?

In machine learning, overfitting occurs when a statistical model explains random error or noise rather than the underlying relationship. When a model is very sophisticated, overfitting is frequently seen. It occurs as a result of having an excessive number of parameters governing the variety of training data types. The model performs poorly despite being overfitted.

Q4. What does the term “reinforcement learning” mean to you?

An algorithmic approach used in machine learning is reinforcement learning. It involves an agent that communicates with its surroundings by taking actions and identifying successes or failures. Different programs and computers use reinforcement learning to find the most appropriate behavior or path to take in a given situation. It typically picks up new skills based on the reward or punishment it receives for each action.

Q5. What three steps go into creating a model or hypothesis in machine learning?

In machine learning, there are three stages to creating hypotheses or models:

Model construction:

It selects an appropriate method for the model and trains it in accordance with the specifications of the issue.

Applying the model: 

Applying the model It is your responsibility to use the test data to verify the model’s accuracy.

Model evaluation:

After testing, it makes the necessary adjustments and then applies the final model.

Q6. What does ILP mean to you?

Inductive Logic Programming is referred to as ILP. Logic programming is used in this area of machine learning. It looks for data patterns that can be used to create prediction models. The logic programs are taken as a given in this process.

Q7. How well do you understand Bayesian networks?

‘Belief networks’ or ‘casual networks’ are other names for Bayesian networks, which are used to illustrate the graphical model for the probability relationship among a group of variables.

The probabilistic correlations between illnesses and symptoms, for instance, can be represented using a Bayesian network. The network can also determine the likelihood of the presence of different diseases based on the symptoms.

Inference or learning in Bayesian networks can be carried out using effective algorithms. Dynamic Bayesian networks are Bayesian networks that link the variables (such as protein sequences or speech signals, for example) together.

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Q8. How does the Central Limit Theorem work? Elaborate on its significance.

According to the Central Limit Theorem, if you pick a sizable random sample from the population with a replacement for a given population mean and standard deviation, regardless of whether the population is normal or skewed, the distribution of the sample mean will be roughly normally distributed.

Remember that for CLT, the sample size must be bigger than 30.


Simple to make decisions about the population since it enables the use of conventional statistical procedures to examine the data, even when the population data is not normal.

As a result, we can rely on the majority of the time that the sampling distribution of the mean will be normal.

Q9. What are some of the machine learning algorithms that are most frequently used?

Based on supervised and unsupervised machine learning, the most widely used machine learning algorithms are:

  • Regular Regression
  • Decision Tree
  • SVM using Logistic Regression
  • Simple Bayes
  • Dimensionality Reduction Algorithms 
  • Using KNN K-Means
  • Increasing algorithms

Q10. How would you manage datasets that are unbalanced?

There are several ways we can deal with uneven datasets:

Oversampling/Undersampling: As an alternative to sampling from the training dataset with a uniform distribution, we can employ oversampling or undersampling. This will make the dataset appear more balanced.

Data augmentation: By including data from the less common categories, we can modify the current data in a regulated manner.

Use of the right metrics: If an unbalanced dataset is being used, using metrics like precision, recall, and F-score might help to better define the model’s accuracy.

Q11. Describe the ROC curve. 

It is a Receiver Operating Characteristic (ROC curve), a crucial instrument for assessing diagnostic tests. The ROC curve is a graph that contrasts the sensitivity and specificity of potential diagnostic test cut-off points. The discrepancy between genuine positive rates and false positive rates at various thresholds is depicted graphically.

Q12. What is Linear Regression in Machine Learning? 

Linear Regression is a supervised machine learning algorithm used to predict continuous data. It is trained on labeled datasets.

A linear relationship between the continuous independent variable (x) and the dependent variables is discovered via linear regression models (Y). Y = mx + c, where m is the line’s slope and c is its intercept, is used to determine the relationship between the dependent and independent variables.

Q13. Which five common algorithms are employed in machine learning?

These are the top five algorithms:

  • Choose Trees
  • Statistical Networks
  • Networks of neural systems
  • SVMs
  • Proximate neighbor

Q14. What does the term “decision tree” mean in machine learning?

In supervised machine learning, where the input is continually divided based on a certain parameter, decision trees are known. The decision tree is developed by breaking down the datasets into ever-smaller subgroups when classification or regression models are built, resembling a tree structure. Decision nodes and leaves can be used to define the tree. Decision nodes are where the data is divided, whereas leaves represent the decisions or results. Both categorical and numerical data can be managed using decision trees.

Q15. What does collaborative filtering as well as content-based filtering mean?

A tried-and-true method for creating tailored content recommendations is collaborative filtering. A form of recommendation system called collaborative filtering foretells fresh material by balancing user preferences with shared interests.

User preferences are the only thing that content-based recommender systems consider. In light of the user’s prior selections, new recommendations are provided from related material.


The concepts of machine learning are the ones mentioned above. New concepts will emerge as a result of how quickly machine learning is developing. Thus, to stay current on it, get involved in communities, go to conferences, and read research papers. This will enable you to succeed in any ML interview.

Frequently Asked Questions

1. How can bias affect machine learning?

The answer is that data bias in machine learning is a form of error that denotes some data discrepancy. This inaccuracy frequently shows that some dataset components have higher weights than others. The contradictions do not hold true in isolation.

2. Are jobs in machine learning in demand?

Jobs in machine learning are in high demand as businesses across all industries implement the procedures and technology. For instance, machine learning enables the finance sector to safeguard against fraud through automated authentication and fraud pattern identification, and the supply chain business to automate materials planning.

3. How do you begin with machine learning?

To use machine learning technology in your current position or to develop the skills necessary for future employment, you can take a number of actions. Then, locate a problem that this technology might be able to help you solve and increase your confidence to study this technical knowledge by breaking it down into many tiny steps. To demonstrate your abilities and track your progress, choose a programming language, practice on any available datasets, and create a portfolio.

4. How are machine learning and big data related?

The creation of systems that learn from experience is known as machine learning. The majority of references to machine learning concern the development of predictive models that are trained on past data to generate predictions on future data. Big data, or extremely massive datasets that can’t fit on a single machine, such as a single hard drive or in RAM, may be used to represent that historical data.

5. How much machine learning be used in my business or sector?

If you work in data science, you might consider a challenge the business is facing to determine if machine learning algorithms can perform as well as or better than other approaches. In order to get started, look up machine learning applications in the literature. In order to get ideas and practice, you can also look for datasets from the business or your sector.

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