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.

Which language is better for machine learning r or python?

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Tell us why is “naive” bayes naive?

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What is data augmentation? Can you give some examples?

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When to use ensemble learning?

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