You work for a large credit card processing company that wants to create targeted promotions for its customers. The data science team created a machine learning system that groups together customers who made similar purchases, and divides those customers based on customer loyalty. How would you describe this machine learning approach?

You are using K-nearest neighbor and you have a K of 1. What are you likely to see when you train the model?

Are data model bias and variance a challenge with unsupervised learning?

Which choice is best for binary classification?

With traditional programming, the programmer typically inputs commands. With machine learning, the programmer inputs

Why is it important for machine learning algorithms to have access to high-quality data?

In the knowledge discovery process, which of the following steps is involved in retrieving data, relevant to the analysis task, from the database?

In relation to K-Nearest Neighbors (K-NN) algorithm, what effect does the small k value (neighborhood size) have on bias and variance?

Which of the following types Of data analysis models is/are used to conclude continuous valued functions?

Which of the following machine learning frameworks works at the higher level of abstraction?

Which of the following machine learning tools provides API for the neural networks?