Answer / Santosh Kumar Bhart

Machine Learning covers a wide range of topics, including Supervised Learning (learning from labeled data), Unsupervised Learning (learning from unlabeled data), Reinforcement Learning (learning through interaction with an environment), Deep Learning (using artificial neural networks with many layers to learn hierarchical representations), and Transfer Learning (reusing trained models on different but related tasks).

What are the three stages of building the hypotheses or model in machine learning?

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Name some feature extraction techniques used for dimensionality reduction?

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How can you avoid overfitting?

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What is sequence learning?

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Why is Python better for machine learning?

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When to use ensemble learning?

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Tell us what's the f1 score? How would you use it?

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Mention any one of the data visualization tools that you are familiar with?

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Why does overfitting happen?

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What are some common unsupervised tasks other than clustering?

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Is macbook good for machine learning?

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what is the function of ‘supervised learning’?

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