Top 100 interview questions on Data Science & Machine Learning

Carvia Tech | May 28, 2019 | 3 min read | 73 views

Questions could comprise of coding and theory questions both based on tech skills as : Language: R, Python Skills: Machine Learning, Statistics

Question bank on data science concepts

  • Why did you do masters in mathematics?

  • Rate yourself in statistics.

  • Rate yourself in Machine Learning.

  • You don’t know java. Why should we hire you?

  • Explain Logistic Regression

  • What is logit?

  • Rate yourself in R.

  • How will you extract data based on only two categories from column A and one category from Column B of a data frame in R?

  • Do you know how to pseudo code in python?

  • What is 99th percentile?

  • What are the types of sorting?

  • What is the probability that a ball chosen will not be green from a bag which contains 5 red ball, 7 green ball and 2 black balls?

  • what is regression?

  • what is correlation?

  • how will you calculate correlation?

  • what are the assumptions behind logistic regression?

  • Are you aware of SQL DB?

  • Have you worked on Linux?

  • What do you do if there is multi collinearity in dataset?

  • Can you give examples for normally distributed dataset?

  • Can you give examples for uniformly distributed dataset?

  • Explain SVM.

  • What is hyperplane?

  • Why didn’t you normalise dataset? (While i was explaining a project)

  • Explain chi-square distribution?

  • When will you use chi-square test?

  • What is the difference between decision trees and random forest?

  • How will you tune random forest model?

  • How to build a model on textual data?

  • How to convert text data to vector format?

  • What is tf-idf?

  • what is difference between Bag of words and tf-idf?

  • What is confusion matrix?

  • How will you evaluate a classification model?

  • What is variance and bias?

  • What is recall score?

  • What is precision score?

  • What is F-score?

  • We don’t know how data science works. We can just get you a project and get connected with client. You will have to solve the problem yourself. can you do that?

  • You haven’t worked in any production level project. Why?

  • You have mostly worked with AV’s and Kaggle’s datasets. Why not some real dataset?

  • How does correlation plays role in modelling?

  • What would you do if you have got imbalanced dataset to work upon?

  • Write SQL Query to get top 5 students w.r.t marks in mathematics if you have a table which contains data of mark sheet of students of a class.

  • Which algorithm was used in restaurant reviews classification project?

  • Explain Naive Bayes and its assumptions.

Machine Learning question bank for experienced

  • Explain this project and your role?

  • What problems you faced while working on A project?

  • How many frames were passed per second in openCV based project?

  • Why did you use regulariser?

  • Have you worked on pyspark, if yes then which one?(RDD or Dataframe)

  • Explain ReLu.

  • When will you use ReLu?

  • Difference between softmax and sigmoid function?

  • How does CNN works?

  • What is max pooling and how does it works?

  • What is recall score?

  • What is precision score?

  • What is F-score?

  • What was the data source used in A project?

  • Why have you mostly worked with keras and why not tensorflow?

  • Do you know data structures?

  • Can you write a custom function for deep learning model?

  • what is the role of loss function and optimiser in a deep learning model?

  • How will you use SVM for multi categorical classification(more than 2 categories?

  • How does strides work in CNN?

  • What are the libraries that you worked with in Python?

  • What are the libraries that you worked with in Python related to NLP?

  • Where have you used pandas in your projects?

  • What is tensor?

  • Define features you would need so as to tell if a person is diabetic or not?

  • Tell a project in which you had to create data from scratch and the problems you faced while working on it.

  • What base model you would suggest for Google’s smart reply?

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Machine Learning

Data science, machine learning, python, R, big data, spark, the Jupyter notebook, and much more

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