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10 Machine Learning Interview Questions – Frequently Asked


Artificial intelligence, machine learning, and data science are three powerful technological advances that have transformed how we live yet their fullest potential is yet to be realized. There is a huge demand for professionals in these fields. As such, recruiters are always out to get the best talent in their teams. Away from the traditional and often expensive courses offered in institutions of higher learning, today data scientists have more options of stepping into the field including learning through the widely accessible Massive Open Online Courses (MOOC) or through an AI and Machine Learning Bootcamp.

A Bootcamp makes professionals hire-ready. However, it is important to get the right one for your specific needs and one that is relevant in your domain.

10 Machine Learning Interview Questions

Today, we focus on the interview. Interviews test your skills and knowledge but are never the easiest to go through thanks to stiff competition among candidates. In addition to knowledge of machine learning basics like algorithms and data structures, interviewers will often test your ability to apply various machine learning techniques, your problem-solving, and technical skills. This may take a series of interviews starting from a screening test, for instance, solving questions on platforms like HackerRank. To help you prepare for your interview, we have listed ten frequently asked machine learning interview questions.                 

1. Differentiate between artificial intelligence, machine learning, and deep learning.

Artificial intelligence (AI) is a broad field concerned with developing intelligent machines that think and work like human beings.

Machine learning (ML) is a sub-branch of AI in which machines are developed to process data while automatically learning and improving from experience without being explicitly programmed. In ML, algorithms are trained to learn from patterns in data and use decision trees to make decisions.

Deep learning (DL) is a subset of machine learning that has been considered closest to the way human beings learn and make conclusions. This is because it uses layers of neural networks that are designed to simulate the interconnectedness and functioning of the human brain cells (neurons). DL models analyze data continually to discover patterns and make decisions on their own. Because they adapt with input data, these models deliver improved output over time.

To cap it, DL is a subset of ML, while both DL and ML fall within the machine learning domain.

2. What are the different types of Learning/ Training models in ML?

Machine learning algorithms are classified into three main categories.

  • Supervised learning. In supervised learning, machines learn using labeled data. An existing dataset is used to train the machine. Thereafter, it learns and makes decisions using new datasets. 
  • Unsupervised learning. In unsupervised learning, ML models draw inferences from input data without labels by forming clusters and finding hidden patterns and relationships in the clusters.
  • Reinforced learning. In reinforced learning, machine learning models are trained to make a series of decisions. The model is exposed to an environment alongside an interpreter. As the model interacts with the environment it produces a series of decisions. The interpreter then determines whether the decisions are favorable or not. Favorable decisions are rewarded (reinforced) and the unfavorable decisions penalized. The model learns through trial and error and improves by attempting to maximize the rewards.

3. What are the popular algorithms of Machine Learning?

Common machine learning algorithms are:

Decisions tree

Random forest

Linear regression

Logistic regression

Naive Bayes

KNN (K-Nearest Neighbors)


Support vector machine

4. Explain Classification and Regression

Classification and regression are predictive models applied in supervised learning. 

Classification is a predictive model that estimates a mapping function (f) from input variables (x) to discrete or labeled output variables (y). In classification, an algorithm can be labeled using two or more variables where an algorithm with two variables is known as a binary classification and an algorithm with more than two variables is known as multi-class classification.

Regression, on the other hand, attempts to predict continuous value based on one dependent variable. In this case, it will estimate the mapping function (f) from input variables (x) to a series of output variables (y) which usually are numerical quantities. A regression task with multiple input variables is known as a multivariate regression problem. 

5. What is the difference between KNN and k-means clustering?

KNN (K-Nearest Neighbors) is a supervised learning algorithm used for classification and regression problems. KNN works with labeled data in which k refers to the nearest neighbor or point which will be used to classify unknown/unlabeled data. In essence, it uses the data points around the selected k-data point to determine which group it belongs to.

K-means is an unsupervised learning algorithm used for clustering problems. K-means uses unlabeled data points in which case K refers to the number of clusters that the algorithm is trying to identify from a dataset. This algorithm works by computing the mean distance between data points and assigning them to one of the k groups.

6. What is Bias and variance in Bias-Variance Tradeoff?

Bias and variance are prediction errors in machine learning algorithms.

Bias error comes as a result of erroneous or oversimplified assumptions in a learning algorithm. This makes the algorithm less flexible to accurately deduce observations or patterns from a training dataset and make it hard for you to generalize the knowledge from the training set to the test set in what is known as underfitting. 

On the other hand, variance error occurs as a result of the learning algorithm being overly complex such that it becomes sensitive to very small fluctuations in the training set. This results in overfitting where the model picks up unnecessary noise rather than the expected output. 

To maintain a balance, a tradeoff between the bias and variance errors is required to achieve low bias and variance errors for accurate prediction.

7. What are the ROC curve and AUC (AUROC)

A ROC (receiver operating characteristic curve) is a graph used to perform diagnostic tests on a classification model at all thresholds. The ROC curve plots the true positive rate (y-axis) against the false positive rate (x-axis). Its purpose is to indicate the test accuracy and the correlation between sensitivity and specificity. In this case, an increase in sensitivity leads to a corresponding decrease in specificity.

AUC, also known as AUROC, refers to the area under the ROC curve. The AUC is a measure of the test accuracy most commonly for binary classification models. A high AUC is an indication that the model is highly accurate in distinguishing between positive and negative variables.

8. What is the difference between Type I and Type II errors?

Type I error or a false positive occurs when a null hypothesis is true and the machine learning model rejects it as false when tested.

Type II error (false negative) occurs when a null hypothesis is false and the machine learning model accepts it as true when tested.

The better error will, then, be the one with lesser consequences within a specific context.

9. What is a confusion matrix and what is its purpose in machine learning

Confusion Matrix, also known as an error matrix, is a table that describes the performance of an ML classification model or a classifier with two or more output classes on a test dataset.

In the table, each row represents the predicted class while the columns represent the actual class to give a breakdown of the types of errors and the number of each type of errors made by a classifier. The confusion matrix helps you calculate the predictive parameters; sensitivity, specificity, recall, and accuracy of a classification model.

10. What is Bayes’ Theorem and how is it useful in machine learning?

The Bayes’ Theorem is used to calculate conditional probability. This is the probability of an event occurring based on past known probabilities of related events. Bayes Theorem provides a way for improving existing predictions based on new conditional evidence.

In machine learning, Bayes Theorem classifier is applied to real-time prediction, multi-class prediction, recommender system, text classification, spam filtering, and sentimental analysis problems.


At the core, machine learning interviews will mostly cover popular machine learning algorithms and their applications. Additionally, it is vital to research in advance about the company you are applying to and the position you are applying for.

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