Math.random() is a built-in function in JavaScript that generates a random number between 0 and 1.
<h3>What is Math.random()?</h3>
Math.random() is a useful and versatile function that can add a element of randomness to your JavaScript programs.
This function can be used in a variety of ways in JavaScript programs, such as:
- Generating random numbers for games or simulations.
- Creating random samples for statistical analysis.
- Shuffling elements in an array for a random order.
- Selecting random items from a list for a quiz or survey.
- Creating unique IDs or keys for objects in a database.
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It does look like a model
Hope it helps I mean just my opinion.
Answer:
Quick analysis tool.
Explanation:
Excel is a spreadsheet application package found in the Microsoft office suite. it's environment on the display screen is called a worksheet and a collection of the worksheets is called a workbook. The excel packet is used for data analysis and interpretation and presentation.
When a group of cells in a worksheet is selected, a small tool kit appears that the lower right corner, it is known as a quick analysis tool. It is use for easy and fast analysis and formatting of that selected group.
Answer:
result = 0
for i in range(99, 0, -1):
result += i
print(result)
Explanation:
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>
