Top Machine Learning Interview Questions and Answers

When it comes to digitisation, the world has evolved significantly since the inception of Deep Learning, Machine Learning and AI technology. The global pandemic further accelerated the digital transformation journey of every industry by five to ten years.

Artificial Intelligence and Machine Learning are highly advanced domains within the IT industry and ensure rewarding careers and future-proof skills. If you are preparing for a career in Machine Learning, take a look at some of the top Machine Learning interview questions you should prepare for to get a high-paying job in a top global organisation. 

Top ML Interview Questions:

Q. What is Overfitting? Is there a way to avoid it?

Overfitting refers to a scenario where an ML model learns its training set much better than expected and interprets random training data fluctuations as concepts. Overfitting negatively affects a model’s generalisation ability and doesn’t apply to fresh data. When a Machine Learning model gets assigned training data, it displays 100% accuracy. But when it uses test data, it could show low efficiency and a relatively higher error rate. This is what ML professionals call overfitting. 

There are several ways to avoid this scenario.

  • Regularisation: This means using a cost term for features that are related to the objective function.
  • Using simple models: Using fewer parameters and variables reduces variance. 
  • Cross-validation methods: K-Folds are in avoiding overfitting.
  • Penalising certain parameters: If there are certain parameters of the given ML model that have a greater chance of causing overfitting, regularisation techniques such as LASSO could penalise them.

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Q. What are Training Sets and Test Sets in ML Models? How much data should you allocate to training and test sets?

You can create an ML model in three steps.

  1. Train the ML model.
  2. Test the model once it’s ready.
  3. Deploy it when the tests return positive

Training Set

Test Set

  • A training set combines all the examples given to a mode to learn and analyse.
  • The training dataset comprises 70% of all the available data.
  • This data trains the ML model.
  • Test data helps developers in testing how accurate the hypothesis is that the ML model generates.
  • It comprises the other 30% of the total data.
  • During tests, data is not labelled, but the result verification uses labels.

Consider a situation where data has been labelled for a thousand records. A proven way to train models is by exposing them to all 1000 records through its training. Then, trainers and developers use a small subset of the same data and test the model. This will give strong results in this situation.

However, this method of testing is not accurate. So, the ideal way to train an ML model is to set one portion of data aside before the training process begins.  This is known as the training set and goes through the ML model several times until high accuracy is observed and errors get minimised. 

Once this is done, the test data is fed to the model to check if it can predict values accurately and determine the effectiveness of the training. If errors are still observed, either the model must get retrained using additional data, or the model changes entirely. 

Q. How would you handle corrupted or missing data within a given dataset?

The easiest method of handling corrupted or missing data is eliminating the columns or rows completely and replacing them with a different value.

In Pandas, you can use two effective methods. 

  • IsNull() and dropna() help in locating rows or columns that have corrupted data and eliminating them.
  • Fillna() replaces the incorrect values with a placeholder. 

Q. How do you choose Classifiers based on the size of the training set data?

When you’re working with a small training set, an ML model with low variance and a right bias work better as it is less likely to see overfitting.

For instance, Naive Nayes will work best with a large training set. A model with high variance and low bias performs better when faced with complex relationships.

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Q. How would you explain the Confusion Matrix in the context of Machine Learning Algorithms?

An error matrix or confusion matrix refers to the table used for measuring an algorithm’s performance. A confusion matrix is generally used for supervised training. When used for unsupervised learning, it’s known as a matching matrix.

There are two parameters of the confusion matrix - Actual and Predicted. Both these dimensions also have identical sets of features.

Q. What are False Positives and False Negatives? Why do they matter?

A false positive case is one that should be classified as false but accidentally gets classified as true. Similarly, false negatives are the cases that deserve to be True but get classified as False. In the case of ‘False positives', positive is the ‘Yes’ row of the value predicted within the error matrix. It indicates the mistaken classification of the value of the case. 

Q. How many stages are there while building a Machine Learning model?

There are three stages of the process of building an ML model. These are:

  • Model Building: Choose the right algorithm for the ML model and train it as needed using training data.
  • Model Testing: Check your model’s accuracy using test data.
  • Deployment of the Model: After looking at the results of the test, make the required changes and then deploy it for use in real-time projects.

From time to time, the ML model must be checked regularly to ensure it works correctly. Every ML model should be updated from time to time for proper functioning.

Q. What is Deep Learning?

Deep Learning is one part of Machine Learning involving multiple systems that learn and think like human beings through artificial neural networks. It is called deep learning because the neural networks are several layers deep.

One fundamental difference between deep learning and machine learning is that machine learning models require manual feature engineering. Meanwhile, deep learning models use neural networks that automatically determine the necessary features to be added or eliminated.

Q. What are the differences between Deep Learning and Machine Learning?

Machine Learning 

Deep Learning

  • Machine learning allows machines to make decisions based on their previous data.
  • ML requires only a little data to train.
  • ML works perfectly using low-end systems, so large machines are not required.
  • Most ML features must be coded manually after being identified in advance.
  • Problems get divided into two parts, get solved in part and then get combined. 
  • Deep learning allows machines to make decisions using artificial neural networks.
  • Deep learning models need large volumes of data during the training phase.
  • It requires advanced machines as deep learning demands large volumes of computing power.
  • Machines learn which features are needed using the given data.
  • The problem gets solved using an end-to-end approach.

Q. What are the applications of supervised Machine Learning in Modern Businesses?

Supervised Machine learning has multiple applications including

  • Email spam detection: The ML model gets trained with historical data which consists of emails categorised as spam or general. The labelled data is fed to this model as input.
  • Healthcare diagnosis: ML models get images of specific diseases fed as input. They can then be used to identify the disease in future patients.
  • Sentiment analysis: In this application, algorithms are used for document mining and for determining if they are negative, positive or neutral. Sentiment analysis is 
  • Fraud detection: Fraud is a huge and increasing danger in the world today, especially in the IT and cybersecurity domains. ML models can be trained to identify fraudulent patterns and behaviours to identify any instances in future.

Q. What is classified as semi-supervised Machine Learning?

Supervised machine learning uses fully labelled data, while unsupervised machine learning doesn’t use training data at all. Meanwhile, semi-supervised machine learning refers to instances where the training data has a smaller percentage of labelled data and a larger volume of unlabelled data. 

Q. What is unsupervised Machine Learning?

When it comes to unsupervised machine learning, two techniques are the most dominant - clustering and association.

Clustering:

When problems are clustered, data gets divided into multiple subsets known as clusters. Every cluster contains data that is similar in nature. Each cluster reveals a different detail about each object, unlike regression or classification.

Association:

The association technique requires identifying association patterns that exist between different items and variables. For instance, e-commerce websites show customers suggestions for items they are interested in and also other complementary items based on their previous purchases and searches.

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Q. What is the difference between unsupervised and supervised Machine Learning?

In Supervised learning, the ML model learns using labelled data and predicts future input accordingly.

In Unsupervised learning, the ML model learns using unlabelled data. The model also enables the algorithm to act on the data and input without guidance.

Q. What is the difference between Inductive Machine Learning and Deductive Machine Learning?

Inductive Learning

Deductive Learning

  • Inductive machine learning observes an instance based on predefined principles before drawing a conclusion.
  • For example, it tells a child to stay away from fire using a video of fire damage.
  • Deductive machine learning draws inferences using experiences. 
  • For example, it lets the child touch the fire. Once the child gets injured, the model learns that playing with fire is dangerous and doesn’t repeat the mistake.

Q. What is the difference between K-means and KNN algorithms?

K-means

KNN

  • K-Means is an unsupervised clustering algorithm.
  • Every individual point in a cluster is similar to the other. But every cluster is different from the others.
  • KNN is categorised as a supervised classification algorithm.
  • It is an unlabelled observation, based on any number of its surrounding neighbours.

Q. What is ‘naive’ in the Naive Bayes Classifier?

This classifier is known as a ‘naive’ classifier as it makes assumptions that could easily be wrong. The Naive Bayes Classifier generally assumes that the presence of a feature in a class isn’t related to any other feature’s presence and assumes independence of features based on the variable of the class. For example, it considers a fruit a cherry simply because it is red and round. Several fruits match this description, which means the assumption is not entirely correct. 

Q. How can a machine play chess matches using Reinforcement Learning?

Reinforcement Learning always has an agent and environment. The agent will perform an action to achieve an objective. Each time it carries out a task taking it closer to its objective, it gets rewarded. Each time it performs a task that takes it away from its goal, it faces a penalty.

Older chess programs would determine which move to make after extensive research on several factors. A machine created specifically to plan and win such games will require extensive and specific rule implementation. 

By using reinforced learning, you don’t need to tackle this problem at all. The learning agent picks up by repeatedly playing chess. It makes a move and decision, takes feedback by checking if the move is correct and then learns the outcome before taking the next step. Reinforcement learning rewards every correct decision of the system and punishes every wrong decision.

Q: How will you decide which ML algorithm you should choose for a classification problem?

There are no fixed guidelines on how to choose an algorithm. But several developers follow a specific rule of thumb:

  • If you’re concerned about accuracy, test multiple algorithms while cross-validating them.
  • If you have a small training dataset, use a model and algorithm with high bias and low variance.
  • If you have a large training dataset, use an algorithm with low bias and high variance.

These are not all the questions you will face in the interview nor will you face all of them. But these interview questions on Machine Learning will give you an idea of the type of questions to expect and how you should frame the answers you know. Several recruiters have said that they have seen candidates get rejected even though they knew the answers, but didn’t know how to frame them. Clearing a Machine Learning job interview is the opening you need for a rewarding IT career.

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Armin Vans
Archer Charles has top education industry knowledge with 4 years of experience. Being a passionate blogger also does blogging on the technology niche.

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