Answer / Naveen Shukla

Python is rich with powerful libraries for Machine Learning. Some popular ones include: Scikit-learn (a collection of machine learning algorithms and tools), TensorFlow (an open-source platform for machine learning and artificial intelligence), PyTorch (a machine learning library focused on deep learning), and Keras (a high-level neural networks API, written in Python).

What are the best public data sets for machine learning?

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Can you explain how do you handle missing or corrupted data in a dataset?

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Explain the difference between L1 and L2 regularization?

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Tell us how is a decision tree pruned?

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

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What is bayes' theorem?

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Why naïve bayes is called naïve?

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What are the smaller dataset techniques?

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Tell me what is the most frequent metric to assess model accuracy for classification problems?

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What is symbolic learning?

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What is the difference between machine learning vs data mining?

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How much data will you allocate for your training, validation and test sets?

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