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Machine learning interview questions

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Machine learning interview questions is the most searched topic on the internet. In 2020 the machine learning and artificial intelligence are becoming emerging trends because of its development. If you want to know why machine learning is important from designer`s point of view then check out the post on the machine learning icon. Everybody wants machine learning internships or machine learning jobs because of its popularity. Here we will discuss some questions which are very fruitful who wants any machine learning internship or machine learning jobs:

  • What have you been working on for the past few years?
  • Explain linear regression
  • What AI and machine learning tools are you familiar with, and how proficient are you in them?
  • What do you do to stay on top of changing technologies?
  • How do you handle missing or corrupt data in a dataset?

The following questions are asked if you have some experience in the field.

  • What kinds of machine learning problems have you tackled, and how did you tackle them?
  • What are the ethical implications of using machine learning?
  • How do you clean and prepare data to ensure quality and relevance?
  • What’s the most interesting project you’ve ever worked on?
  • What is data normalization and why do we need it?

You can find machine learning jobs or machine learning internship  by clicking on the link

Machine learning interview questions from dataset point of view

  • Explain dimensionality reduction, where it’s used, and its benefits?
  • How do you handle missing or corrupt data in a dataset?
  • Explain this clustering algorithm?
  • How would you go about doing an exploratory data analysis (eda)?
  • How do you know which machine learning model you should use?
  • Why do we use convolutions for images rather than just fc layers?
  • What makes cnns translation-invariant?
  • Why do we have max-pooling in classification cnns?
  • Why do segmentation cnns typically have an encoder-decoder style/structure?
  • What is the significance of residual networks?
  • What is batch normalization and why does it work?
  • How would you handle an imbalanced dataset?
  • Why would you use many small convolutional kernels such as 3×3 rather than a few large ones?
  • Do you have any other projects that would be related here?

Machine learning interview questions from the future point of view

  • Explain your current master’s research? What worked? What didn’t? Future directions?
  • Is rotation necessary in PCA? If yes, Why? What will happen if you don’t rotate the components?
  • Explain prior probability, likelihood, and marginal likelihood in the context of a naive Bayes algorithm?
  • To get help from top machine learning books we have the list of best machine learning books.

The following case study can be asked if the person taking the interview is from the food industry:

  • You are assigned a new project which involves helping a food delivery company save more money. The problem is, the company’s delivery team isn’t able to deliver food on time. As a result, their customers get unhappy. And, to keep them happy, they end up delivering food for free. Which machine learning algorithm can save them? You can learn about top machine learning hardware.

The above are some important interview questions.

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