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.
<em>Hope this helps!</em>
Well a viral meme would spread very fast, it's a VIRAL meme which means it would spread faster than a normal meme. Even if it is contained in the 24 hour time period it would still become rather popular.
The PDU that is processed when a host computer is de-encapsulating a message at the transport layer of the tcp/ip model is segment.
<h3>What is this PDU about?</h3>
Note that in the transport layer, a host computer is said to often de-encapsulate what we call a segment so that they can be able to put back data to an acceptable or given format through the use of the application layer protocol that belongs to the TCP/IP model.
Therefore, The PDU that is processed when a host computer is de-encapsulating a message at the transport layer of the tcp/ip model is segment as it is the right thing to do.
Learn more about host computer from
brainly.com/question/553980
