Explore AI and Data Science

What does "NLP" stand for in AI?

• Natural Language Processing

• Natural Linguistic Processing

• Neural Language Processing

• Natural Language Programming

Which AI technology powers virtual assistants like Alexa?

• Natural Language Processing (NLP)

• Computer Vision (CV)

• Machine Learning (ML)

• Speech Recognition (SR)

Which field combines computer science and statistics to analyze data?

• Data Science

• Information Technology

• Machine Learning

• Artificial Intelligence

What is the main role of algorithms in AI?

• Algorithms are only relevant in computer programming.

• Algorithms primarily focus on hardware design.

• Algorithms are used to store data permanently.

• The main role of algorithms in AI is to enable machines to process data, learn from it, and make decisions.

What is supervised learning in AI?

• Supervised learning is a method that requires no data for training.

• Unsupervised learning involves training models without labeled data.

• Supervised learning is only applicable to image recognition tasks.

• Supervised learning is a machine learning approach where a model is trained on labeled data to make predictions.

What is the purpose of a neural network?

• To store data in a structured format.

• To replace traditional programming languages entirely.

• To create simple linear equations.

• The purpose of a neural network is to model complex relationships in data for tasks such as classification, regression, and pattern recognition.

Which programming language is commonly used for data science?

• HTML

• SQL

• Python

• Java

What is the term for data that is used to train a model?

• validation data

• test data

• input data

• training data

What does 'big data' refer to?

• Big data only applies to social media data.

• Big data is a term used for traditional data storage methods.

• Big data refers to large and complex datasets that require advanced tools and techniques for processing and analysis.

• Big data refers to small datasets that are easy to analyze.

What is the goal of unsupervised learning?

• To discover hidden patterns or intrinsic structures in data.

• To predict future outcomes based on labeled data.

• To enhance the accuracy of supervised learning models.

• To classify data into predefined categories.