20 Most Important Machine Learning Interview Questions

October 26th, 2020
machine learning interview questions

Acing a machine learning interview questions could get difficult at times. The field is extremely vast and ever-expanding, with a plethora of topics which could be a part of interview questions. The nature and amount of topics that require your skills could vary for different recruiters/ interviewers. However, that does not mean you do not prepare for it.

In this blog, we have listed some of the most frequently asked machine learning interview questions. This list of questions prepared here is not exhaustive and is totally based on the personal experience of many candidates who have appeared in such interviews. These questions have certainly been asked more times than others and answering them will certainly play a role in helping them clear the interview.

But is reading this blog enough? Honestly, no! Our advice is to enroll yourself in a leading machine learning course online to get the in depth answers of latest 20 machine learning interview questions. This is a great way to prepare for the job of your dreams as you learn by getting trained under industry experts.

Top 20 Machine Learning Interview Questions & Answers

Here is a list of the top 20 Machine Learning interview questions that will benefit you in the future.

Q1. What are the three different types of machine learning techniques?

Ans: Machine Learning is broadly divided into three different categories - supervised, unsupervised, and reinforcement learning.

A. Supervised learning is where a data set in which we apply supervised techniques needs to be labeled information. A labeled data often has two parts - independent and dependent variables.

B. In the case of unsupervised learning, we only have the independent variables in our data set with the help of which we have to proceed with model building exercises.

C. Reinforcement learning, on the other hand, is an area of ML concerned with how an agent ought to take actions in an environment in order to maximize the notion of cumulative reward.

Q2. Name the different types of supervised learning?.

Ans: Supervised learning is divided into two types, depending on the type of the target variable.

We have regression-based methods for continuous and classification methods for discrete target variables. Additionally, there are different types of classification and regression techniques too.

Q3. Can you name some of the most commonly used supervised and unsupervised techniques?

Ans: Some of the most commonly used supervised techniques are:

  1. Multiple linear regression
  2. Logistic regression
  3. Random forest
  4. Naive Bayes'
  5. K nearest neighbor
  6. Support Vector Machines

Some of the commonly used unsupervised techniques are:

  1. Principal Component Analysis
  2. Clustering techniques
  3. Recommendation systems
  4. Association rules

Q4. How do we decide whether we need to apply a classification or a regression technique?

Ans: Classification and regression are supervised learning techniques, which means that the data set would also be labeled. Classification segregates data points into predetermined categories. In the case of classification, the target variable would be discrete in nature like binary labels (yes or no) or multi-level (the class I, class II and class III). For instance

  1. Predicting whether a person would buy a car or not
  2. Predicting whether it would rain or not
  3. Whether customers will open an email or not
  4. Will or will not a customer payback credit card dues
  5. If the insurance claim is fraud or genuine

However, in the case of regression, the target variable would be continuous in nature like the age of a person, sales figures, domestic growth, GDP, population, etc. For instance

  1. Prediction of the amount of rainfall
  2. Predicting the sales of new mobile connections
  3. Predicting revenue of a company
  4. Footfall in a mall
  5. Total retail spend by different customers

Q5. What is dimension reduction in machine learning?

Ans: Dimensionality reduction is a feature selection method that is used to reduce the number of variables under consideration in a data set. Dimensionality reduction can be performed by using PCA or TSNE. After applying dimensionality reduction, we are left with variables that are statistically more significant. Hence, it is more helpful for model building exercises.

Q6. State the various dimensionality reduction techniques.

Ans: Some of the most commonly used dimensionality reduction techniques are:

  1. Factor Analysis
  2. Principal Component Analysis (PCA)
  3. Random forest
  4. Missing value ratio
  5. Forward feature selection
  6. Backward feature elimination
  7. Factor analysis
  8. Low variance filter

Q7. What is NLP and how is it related to ML?

Ans: Nat­ur­al Lan­guage Pro­cessing (NLP) is a field that cov­ers com­puter un­der­stand­ing and ma­nip­u­la­tion of hu­man lan­guage. It is a field of study that is concentrated on the interactions between computers and human language.

NLP can be considered to be the intersection of computer science, artificial intelligence, and computational linguistics. NLP developers perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.

It is one of the fastest growing fields in the area of AI and ML, owing to the large amount of natural language that gets generated in the digital world of today.

Q8. How do you handle imbalanced data in machine learning?

Ans: Imbalancement in data is a characteristic of supervised learning. When the ratio of a level in the target variable is proportionately larger than the other, data is said to be imbalanced.

In the case of a binary target variable with ‘yes' or ‘no' levels, if the proportion of any one of them is significantly more than the other, we say the data is imbalanced. Data could be imbalanced for categorical variables with more than two levels.

The above phenomenon in data sets often results in skewed model results, if not handled properly. We can handle data imbalance by applying these techniques:

  1. Collecting more data to even out the imbalances in the dataset.
  2. Resampling the dataset to correct any imbalances.
  3. Applying upsampling and downsampling methods.

Q9. What are the assumptions of the Ordinary Least Square (OLS) regression technique?

Ans: The assumptions when applying the OLS regression technique are -

  1. The sample data should represent a huge chunk of the population.
  2. The input and output variable should share a linear relationship.
  3. The input variable must show homoscedasticity.
  4. No multicollinearity among independent/ input variables.
  5. There should be any autocorrelation in the output/ dependent variable.

Q10. How does ML different from deep learning?

Ans: Machine learning (ML) is an application of artificial intelligence that provides systems the ability to automatically learn and improve from existing data and experience. The need to be explicitly programmed everytime is eliminated. ML is concentrated on the development of computer programs to access data.

Machine starts learning/analyzing with observations or data (examples or instruction) to look for patterns and make better decisions in the future on the provided data. Computers are crafted to learn automatically without any human assistance or intervention, and adjust their actions accordingly.

Machine learning focuses on analyzing and learning from data based on features/variables fed into the model to make better decisions.

Deep Learning, on the other hand, is a subset of machine learning techniques. It constructs artificial neural networks (ANNs), which copy and reconstruct the function and structure of the human brain. The focus here is on feature extraction. Information is deduced from multiple layers and each layer propagates the information to another layer for the final outcome.

In practice, deep learning, also known as deep structured learning or hierarchical learning, uses a large number of hidden layers of nonlinear processing to extract features from data. This data is then transformed into different levels of abstraction.

You May Also Read - Frequently used Machine Learning Tools

Q11. How is missing data handled in a dataset?

Ans: Handling missing values is common when preparing the data for building models. An important step here is to understand the type of data that has missing values and decide which techniques to be used accordingly.

Data types could either be discrete or continuous and hence, the missing values too. There are a few ML models that could handle missing values, but most of them cannot. Additionally, it is a good practice to handle missing values before model building. Some of the basic techniques to handle missing values are:

  1. Continuous Variables: Replace missing with mean
  2. Ordinal Variables: Replace missing with the median
  3. Categorical Variables: Replace missing with the mode
  4. Dropping: When the proportion or the count of missing values is comparatively very less, we can also drop them

Q12. What are some of the most common steps for building an end-to-end ML solution?

Ans: Common steps for building an end-to-end ML model include:

  1. Business Problem: Understand business objectives and convert it into analytical problems
  2. Data Sources: Identify the required data sources. Extract and aggregate the data
  3. Exploratory Analysis: Understand the data, and examine all the variables for errors, missing values, and outliers. Conclude the relationship between different types of variables. Check for assumptions
  4. Data Preparation: Exclusions, type conversions, outlier treatment, missing value treatment, derived variables, binning variables, dummy variables creation, etc.
  5. Feature Engineering: Avoid multicollinearity and optimize model complexity by reducing the number of input variables – variable cluster, correlation, factor analysis, etc.
  6. Data Split: Split the data into training and testing samples as per a suitable ratio
  7. Building Model: Fit, check accuracy, cross-validate, and tune the model with the help of parameters and hyperparameters
  8. Model Testing: See the model on the testing sample, iterate the model, and run diagnostics, if required
  9. Model Implementation: Prepare final model results- present the model and identify the limitations of the model
  10. Performance Tracking: Track model performance periodically and update it as required

Q13. What are some of the real life applications of ML algorithms?

Ans: Real life applications of machine learning include:

  1. Bioinformatics
  2. Robotics Process Automation
  3. Natural Language Processing
  4. Sentiment Analysis
  5. Fraud detection
  6. Facial & Vocal recognition systems
  7. Anti-money laundering

Q14. How is data mining different from ML?

Ans: In data mining, we extract information to build insights from different types of sources and data. It is an exhaustive process where one can use statistical and visualization techniques to extract meaningful insights.

Machine learning, on the other hand, is a field of study that deals with developing algorithms and methodologies on its own.

Q15. What was the last book or research paper that you read on machine learning?

Ans: Candidates must always be well-read and aware of the latest developments being made in ML by reading published research papers and scientific journals. https://arxiv.org/ and https://www.kdnuggets.com/2017/04/top-20-papers-machine-learning.html are good sources to find various research papers in the field of machine and deep learning. This is a field where you will have to keep learning and this question checks whether you like to stay updated or not.

Q16. What is the significance of F1 score in machine learning algorithms?

Ans: F1 score is a performance measuring metric for supervised classification algorithms. It is the weighted average or the harmonic mean of the Recall and Precision values of a model. It is considered a robust technique to evaluate model performance.

Q17. What is pruning in decision tree algorithms and how do you prune a decision tree?

Ans: Pruning is a method that is applicable to tree-based methods. Hence, it can be observed in supervised algorithms. Replacement of nodes of a decision tree in a top-down or bottom-up way is carried out during pruning. It becomes very helpful in increasing the accuracy of the decision tree while also reducing its complexity and overfitting.

The objective of pruning is to reduce the size of a tree without affecting the accuracy as measured by cross-validation. The two commonly used pruning methods are:

  1. Error based
  2. Cost complexity based

Q18. Why is ensemble learning used?

Ans: Ensemble learning is used to improve the predictive performance of a model. Ensemble methods are usually considered to be better than individual models.

Q19. When to use ensemble learning?

Ans: Ensembling techniques are applied to improve the accuracy of machine learning techniques. During ensembling, a set of statistical methods are used, which leads to improvement of model performance.

Q20. What are the two paradigms of ensemble methods?

Ans: There are two paradigms of ensemble methods, namely

  1. Sequential ensemble methods
  2. Parallel ensemble methods


As technology continues to change, more jobs in the domain of artificial intelligence and data science are bound to emerge. This is the right time to upskill yourself to become at par with the current job trends. Gaining a machine learning skill set will give your career a boost in the right direction, and for this, you can take the aid of a machine learning course online.

These machine learning interview questions will help you get a little closer to your dream of being a part of the expanding field.

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