Respuesta :

stigawithfun

Answer:

4. Supervised learning.

Explanation:

Supervised and Unsupervised learning are both learning approaches in machine learning. In other words, they are sub-branches in machine learning.

In supervised learning, an algorithm(a function) is used to map input(s) to output(s). The aim of supervised learning is to predict output variables for given input data using a mapping function. When an input is given, predictions can be made to get the output.

Unsupervised learning on the other hand is suitable when no output variables are needed. The only data needed are the inputs. In this type of learning, the system just keeps learning more about the inputs.

Special applications of supervised learning are in image recognition, speech recognition, financial analysis, neural networking, forecasting and a whole lot more.

Application of unsupervised learning is in pre-processing of data during exploratory analysis.

Hope this helps!

Both assistants use artificial intelligence technologies and use algorithms to reach a conclusion.

  • They have been made and programmed to answer human-like questions and they do this by surfing the web.
  • They learn from the process of supervised learning. This is the task of learning that function which maps the inputs. The training data is used for an unseen situation.

Hence the option 4 is correct.

Learn more about  how to recognize yours.

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