Answer / Lavi Kumari

The objective of Machine Learning is to develop models that can learn patterns from data and make accurate predictions or decisions on new, unseen data. This involves training a model using labeled examples (Supervised Learning) or finding hidden structures in unlabeled data (Unsupervised Learning). The goal is to create intelligent systems capable of solving complex problems, recognizing speech, understanding images, recommending products, and much more.

What are two techniques of Machine Learning?

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How will you explain machine learning to a layperson in an easily comprehensible manner?

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Which library would you prefer for plotting in Python language: Seaborn or Matplotlib or Bokeh?

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Explain the difference between bias and variance?

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Why do we need to convert categorical variables into factor?

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What is a bayesian model?

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Tell me how is knn different from k-means clustering?

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Explain what are some methods of reducing dimensionality?

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Explain me what is bayes' theorem? How is it useful in a machine learning context?

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Assume that you are working on a data set, explain how would you select important variables?

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What is the “kernel trick” and how is it useful?

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What is candidate sampling in machine learning?

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