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Machine Learning (Interview Questions)

Written by  Piyush Bhartiya, MBA

Published on Sat, February 15, 2020 6:27 AM   Updated on Mon, February 17, 2020 6:05 AM   4 mins read

Machine learning is one of the hottest things in the world right now because of its extensive use and applications in the modern-day world. Machine Learning is the branch of science in which computers can learn new things without being programmed over and over again for different tasks.

Machine Learning is a subset of Artificial Intelligence which enables computers to learn on their own from new resources and also from past experiences.

A career in Machine Learning is a very lucrative one as it’s one of the highest paying jobs in the world, and the average salary of a machine learning engineer in the US is about 110 Thousand Dollars.

A big pay scale like that means that Machine Learning is a tough nut to crack because it makes use of huge mathematical concepts along with different programming languages and is a combination of Algorithms, Programming skills, and knowledge of current machine learning projects and algorithms and concepts behind them.

Preparing for a machine learning interview can be a rigorous job because of the vast syllabus you have to cover. The following are some commonly asked Machine Learning Interview Questions, which you can expect in your upcoming interview.

Machine-Learning Interview Questions

Q1. What is Supervised and Unsupervised Machine Learning? Please point out the basic differences between them.

Answer– In supervised machine learning, we provide computers with example inputs and the desired outputs, and the objective is to learn a general rule that connects inputs to outputs. Some real-life uses are Image Processing and Market Predictions. In Unsupervised Machine Learning, no inputs and outputs are given to the computers, the computer itself has to find the hidden pattern in the inputs and produce the desired output. Population clustering is an example of Unsupervised Machine Learning.

Q2. What are KNN and K-Means clustering? Write the basic differences.

Answer– K-Nearest Neighbour or KNN is a supervised Machine Learning Algorithm, where we provide computers with the labeled data, and the algorithm then classifies the points based on the distance from the nearest points. Whereas K-Means Clustering is an unsupervised Machine Learning Program in which the algorithm classifies the points or clusters, and the principle behind this is the mean distance from different points.

Q3. What are the sections in which the data in Machine Learning is split?

Answer– In Machine Learning, data is split majorly into two sections. The Training Set and The Test Set. In the training set, data is used to train the model while the data in The Test Set is used to test the trained model.

Q4. What is Bayes Theorem, and how is it useful in Machine Learning?

Answer– Bayes Theorem is a mathematical theorem that helps us in predicting the outcome of an event based on the knowledge of conditions/factors associated with the current event. In Machine Learning, Bayes theorem is the principle behind a complete machine Learning Branch which deals with Naive Bayes classifier.

Q5. What are the Type-1 and Type-2 errors in Machine Learning?

Answer– In Machine Learning, Type-1 errors are False Positive statements i.e., claiming that something has occurred/happened when it has not, while Type-2 errors are False Negative statements i.e., claiming that nothing is happening. At the same time, something is happening or going on. For e.g., Telling a fit man he cannot see and is blind is a Type-1 error while telling a blind man nothing happened to him and can see is a Type-2 statement.

Q6. What is the exchange between Bias and variance?

Answer– Bias and Variance both are errors in Machine Learning.
Bias is the error due to the high simplistic assumptions used in the learning algorithms. In Bias, we are overly underfitting the data resulting in low accurate predictivity by the algorithm.
On the other hand, the variance is the error due to the overly complex assumptions and data used in the learning algorithm. It can cause the algorithm to overly fit the data, and your algorithm is very sensitive to the data.

About the Author & Expert

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Piyush Bhartiya

Author • MBA • 20 Years

Piyush values education and has studied from the top institutes of IIT Roorkee, IIM Bangalore, KTH Sweden and Tsinghua University in China. Post completing his MBA, he has worked with the world's # 1 consulting firm, The Boston Consulting Group and focused on building sales and marketing verticals for top MNCs and Indian business houses.

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