What are two methods used to detect outliers in structured data? (Choose two.)
With only limited labeled data available how might a neural network use case be realized?
Considering one ML application is deployed using Kubernetes, its output depends on the data which is constantly stored in the model, if needing to scale the system based on available CPUs, what feature should be enabled?
Given the following sentence:
The dog jumps over a fence.
What would a vectorized version after common English stopword removal look like?
What is a class of machine learning problems where the algorithm is given feedback in the form of positive or negative reward in a dynamic environment?
Which is an example of a nominal scale data?
What is used to scale large positive values during data cleaning?
In which example would recall be preferred over precision?
