Machine learning interview questions and answers for freshers in 2022

Machine learning interview questions and answers for freshers in 2022

The world is becoming more and more digital each day and there is a great increase in the use of Machine Learning in all areas of knowledge. If you’re a fresher who is just starting with Machine learning and interested in pursuing a career in Machine learning, then you would like to know the most frequently asked machine learning interview questions and answers for freshers in 2022. 

Machine learning is a technology that is poised to impact every industry including banking, retail, manufacturing, healthcare, education, and more. Top companies across the world including Google, Apple, Tesla, and Microsoft are actively seeking Machine learning engineers and freshers to join their team. According to a report by Forbes, machine learning jobs are projected to be worth almost $31 billion by 2024. So, needless to say, a machine learning engineer career can be a smart move. 

Read the article on: Skills required for data scientist

However, to be rightly prepared for the job, you need to be familiar with the most frequently asked machine learning interview questions. These machine learning interview questions are an integral part of the machine learning interview and the path to become a machine learning engineer, data scientist, or data engineer.  

Frequently asked machine learning interview questions for freshers

What is the difference between classification and regression?

In short, the most significant difference between classification and regression is that while classification predicts discrete class labels, regression helps predict a continuous quantity. Classification is used to classify data into some specific categories for example classifying emails into spam and non-spam categories. We use regression analysis when we are dealing with continuous data, for example predicting stock prices at a certain point in time.

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What is the difference between supervised and unsupervised learning?

Technically they both are a type of machine learning model. In a supervised learning model, the algorithm learns from labelled training data. It allows you to collect data or produce a data output from the previous experience. Whereas the unsupervised learning model mainly deals with unlabelled data and helps you to find all kinds of unknown patterns in data. Here inferences are drawn from datasets containing input data without labelled responses. 

What is cross-validation?

Cross-validation is essentially a technique used to assess how well a model performs on a new independent dataset. This is a resampling procedure that evaluates machine learning models on a limited data sample. 

How do you evaluate cross-validation?

  • Take the group as a holdout or test data set.
  • Take the remaining groups as a training data set.
  • Fit a model on the training set and evaluate it on the test set.
  • Retain the evaluation score and discard the model.

What do you understand by selection bias?

  • Selection bias is a statistical error that causes a bias in the sampling portion of an experiment.
  • This causes one sampling group to be selected more often than other groups included in the experiment leading to an error in results.

Which is a better model, random forest, or decision tree?

Random forest is the better model among these two because it is an ensemble method that takes many weak decision trees to make a strong learner. So, it is more accurate, more robust, and less prone to overfitting.

What is overfitting?

Overfitting is a modeling error that occurs when a model learns the training set too well, taking up random fluctuations in the training data as concepts. This results in failure to predict future observations effectively or fit additional data in the existing model.

How do you avoid overfitting?

  • Keep it simple: reduce variance by taking into account fewer variables and parameters, thereby removing some of the noise in the training data.
  • Use cross-validation techniques such as k-folds cross-validation.
  • Use regularization techniques such as LASSO that penalize certain model parameters if they’re likely to cause overfitting.

Name some Machine Learning Libraries and their benefits

  • Numpy: It is used for scientific computation.
  • Pandas: It is used for tubular data analysis.
  • Scikit learns: It is used for data modeling and pre-processing.
  • Tensorflow: It is used for the deep learning process.
  • Regular Expressions: It is used for text processing.
  • Pytorch: It is used for the deep learning process.
  • NLTK: It is used for text processing.
  • Statsmodels: It is used for time-series analysis.


Note: While answering this machine learning interview question, adding your experience in working with these libraries would be great. 

The growth of machine learning jobs has increased the demand for employees with this skillset and this trend will continue in the coming years. With machine learning job trends rising in areas such as deep learning and natural language processing, there is a place for you regardless of the speciality you choose to pursue. So, you need to be prepared when the opportunity knocks on your door. Learn these Machine learning interview questions and crack the machine learning interviews at major companies and start-ups.

As a premier business school in India, Praxis offers a 9-month full-time postgraduate program in Data Science. We have vast experience in business education, and we offer students both the time to understand the complex theory and practice of data science concepts and the guidance from knowledgeable faculty who are available on campus for mentoring. Our well-structured campus placement program ensures interview opportunities with the most significant companies in the Machine learning field.

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