Some of the interview questions in ML interivews

interivew_questions.md

Interview

• Basic Interview questions

  • Introduce yourself
  • Where do you see yourself in 5 years
  • Your strength and weekness
  • Why should we hire you
  • What are your salary expectation
  • why did you choose this company
  • How do you deel with criticism
  • How do you handle your stress
  • What do you considered as your biggest achievement
  • Why you didn't goes to collage
  • What motivates you
  • Tell me about an experience when you faced difficulty at work while working on a project
  • Assume you are hired, then how long would you expect to work for us
  • What is success to you

• ML specific questions

  • What is your favorite algorithm
  • Explain the steps of each algorithm
  • How can you avoid overfitting
  • Your approach to EDA in tabular, image, audio, text
  • How do you select right futures
  • What is your understanding of back propogation
  • How to handle overfit and underfit in a network
  • Explain a computational graph
  • Explain you understanding of GAN
  • each activations
  • explain the adam optimizer in NN
  • How do you handle missing values
  • How do you pick a threshold in a classification problem
  • Difference between Univariate, Bivariate and Multivariate analysis
  • Why using loss
  • Explain prior probability, likelihood and marginal likelihood in context of naiveBayes algorithm
  • How to select k for k means
  • How to handle ourliears
  • Explain ROC curve
  • Why use use a dimentionality reduction
  • Why naive bayes classifier is 'naive'
  • What are the auto encoders
  • forward prop vs. backward prop
  • regularization methods in each algo and NN
  • Why and how to handle vanishing and exploading gradient, how to detect
  • What is Bltzmann Machine
  • When to use padding and stride in CNN
  • Describe a scenario where you use custom loss function
  • Types of auto encoders
  • explain cross validation and its need
  • when to use precision and recall
  • Loss fn vs. Cost fn
  • What is p value
  • How to prevent data leakage
  • What is central limit theorem
  • Explain SMOTE method for imbalance
  • explain XGB algo
  • What is the reason behind the curse of dimentionality
  • How do you check normality in a dataset
  • What is the purpose of A/B testing

• We can ask questions

  • What are your favourate aspect of your role as a data scientist
  • How long you are working here so far
  • What oppurtunities for growth. How company supports the growth.
  • What would my first 6 months looks like, some of the projects that I am working on like
  • A company specific question