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.
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.
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.
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.
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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