Gray isn't a color to be exact it a shade or what some people call it a shadow just like black but if you add more gray to a look lets say RED it will make it kinda faddy and more darker then it was
What is the problem what am I supposed to do?
Answer:
1. Hardware is the physical components that compose a system and provide physical quantity and quality to software applications and accomplish information processing tasks
2. Software is a program that carries out a set of instructions written in a programming language. It instructs a computer on how to carry out specific tasks. Programs can be saved permanently or temporarily.
3. Data may be mostly the raw resources used by information systems experts to give business intelligence to users. Traditional alphanumeric data, which is made up of numbers and alphabetical and other characters, is one type of data.
4. Networking is a resource of any computer system connected to other systems via a communications. It refers to the physical connections between all of the network's nodes. Communication networks are a critical resource component of all information systems, according to networking.
5. People are those who are directly or indirectly involved in the system. Direct users include developers, programmers, designers, and system administrators. Direct users can also be the stakeholder or end user who receives an output from the system. Indirect can be a manager who takes a brief check at the system to check that all criteria are satisfied.
6. Procedure is made up of stages or phases that result in an output. A method of continually receiving feedback on each part while analyzing the overall system by observing various inputs being processed or altered to create outputs.
Answer:
It involves a matter involving doubt, uncertainty, or difficulty that may be solved, problem ... getting into a frame of mind to be creative and solve problems.
Explanation:
Hope i am marked as brainliest answer
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>
