Answer / Achin Sharma

I was trained on a vast range of machine learning resources to understand algorithms, models, and techniques. My learning encompassed topics like linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), neural networks, reinforcement learning, deep learning, and more. I haven't had hands-on experience building or training machine learning models myself, but my responses are based on the knowledge I gained through extensive study.

What is your favorite algorithm and also explain the algorithm in briefly in a minute?

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How is decision tree pruned?

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How python can be used in machine learning?

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What is the difference between Entropy and Information Gain?

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What are the three types of algorithms?

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Why overfitting happens?

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Explain Classification and Regression?

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How to choose notable variables while working on a data set?

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Why is “naive” bayes naive?

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Why instance-based learning algorithm sometimes referred to as lazy learning algorithm?

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When to use ensemble learning?

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Why machine learning is so important?

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