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Artificial Intelligence (AI) Interview Questions and Answers

Ques 6. What is natural language processing (NLP)?

Natural Language Processing is a field of AI that focuses on the interaction between computers and humans using natural language, enabling machines to understand, interpret, and generate human-like text.

Example:

Chatbots and language translation applications use NLP to understand and generate human language.

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Ques 7. How does overfitting occur in machine learning, and how can it be prevented?

Overfitting occurs when a model learns the training data too well, including noise and irrelevant details, leading to poor performance on new data. It can be prevented by using techniques like cross-validation, regularization, and having a sufficiently large and diverse dataset.

Example:

A model that perfectly memorizes a small dataset but fails on new examples is overfit.

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Ques 8. What is the difference between machine learning and deep learning?

Machine learning is a broader concept that involves the development of algorithms to enable machines to learn from data. Deep learning is a subset of machine learning that specifically uses neural networks with multiple layers (deep neural networks) to learn and make decisions.

Example:

Linear regression is a machine learning algorithm, while a deep neural network is an example of deep learning.

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Ques 9. Explain the concept of bias in machine learning.

Bias in machine learning refers to the presence of systematic errors in a model's predictions, usually stemming from biased training data. It can lead to unfair or discriminatory outcomes.

Example:

A facial recognition system trained predominantly on one ethnicity may exhibit bias against other ethnicities.

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Ques 10. What is transfer learning in the context of machine learning?

Transfer learning involves leveraging knowledge gained from one task to improve the performance of a model on a different but related task. It allows the reuse of pre-trained models for new tasks, saving time and resources.

Example:

Using a pre-trained image classification model for a similar but distinct classification task.

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