Modeling

What is the primary difference between Machine Learning (ML) and Deep Learning (DL)?

• ML uses smaller datasets, while DL uses very large datasets

• ML is a type of AI, but DL is unrelated to AI

• DL involves explicit programming of rules, while ML does not

• ML is only used in gaming, and DL is used in robotics

What is the purpose of a testing dataset in machine learning?

• To teach the model how to recognize patterns

• To evaluate how well the model performs on unseen data

• To label the data for future predictions

• To store data for future training

Which of the following is an example of labeled data?

• A file of images with no tags or descriptions

• A table of vehicle data with the "Type of Vehicle" column filled in

• A list of songs without any genre or artist information

• A collection of random sensor data from a factory

Which of the following is an example of a supervised learning task?

• Clustering customer data

• Detecting spam emails

• Finding product recommendations

• Grouping similar articles

In Reinforcement Learning, what is the role of the "agent"?

• It provides labeled training data

• It makes decisions and learns from rewards or penalties

• It groups data into clusters

• It builds association rules between different variables

Which of the following is an example of unsupervised learning?

• Predicting house prices based on features

• Classifying emails as spam or not spam

• Segmenting customers based on purchasing behavior

• Training a robot using rewards and penalties

Supervised learning consists of two types: __________ and __________.

In Reinforcement Learning, the __________ receives feedback from the environment in the form of rewards or penalties.

In which type of machine learning is the data labeled with the desired output?

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

• Deep Learning

An email spam filter that learns to identify spam emails based on labeled examples is an application of:

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

• Transfer Learning