This is a list of 18 questions which has been asked in several Microsoft data science / machine learning interviews. These questions have been compiled from Glassdoor and other sources. We shall be posting a series of related objective questions (capsule) quizzes in very near future.
- Can you explain the Naive Bayes fundamentals? How did you set the threshold?
- Can you explain SVM?
- How do you detect if a new observation is outlier? What is bias-variance trade off ?
- Basic statistical questions such as define variance, standard deviation etc
- Discuss how to randomly select a sample from a product user population.
- Describe how gradient boost works.
- What is L1 and L2 Norm? What is the difference between them?
- What is central limit theorem? How to determine is the distribution is normal?
- What algorithm can be used to summarise twitter feed?
- Simple probability questions that dealt with Bayesian equations
- What are some of the steps for data wrangling and cleaning before applying machine learning algorithms?
- How to deal with unbalanced binary classification?
- What is the difference between box plot and histogram?
- How do one go about solving the L2-regularised regression problem?
- Probability fundamentals
- Describe Markov chains?
- Describe different regularisation methods such as L1, L2 regularisation?
- Neural networks fundamentals
I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
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