Answer Posted / Manoj Kumar Rajput

Support Vector Machines (SVM) is one of my favorite algorithms. It's a supervised learning algorithm that can be used for both classification and regression tasks. SVM finds the optimal hyperplane that separates data points of different classes with the maximum margin, thus minimizing the risk of overfitting. In cases where data isn't linearly separable, the algorithm can utilize kernel functions to transform data into higher dimensions for separation.

What are standardization and normalisation? Give one advantage of each over the other?

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Do you have research experience in machine learning?

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Tell me what are the last machine learning papers you've read?

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