Answer:
import random
randomlist = []
for i in range(0,20):
n = random.randint(-29,30)
if n < 0 :
n = 100
randomlist.append(n)
print(randomlist)
Explanation:
The random module is first imported as it takes care of random. Number generation.
An empty list called randomliay is created to hold the generated random integers.
Using a for loop, we specify the range of random numbers we want.
Inside the for loop ; we attach our generated random integer which will be in the range (-29 to 30) in a variable n
For each n value generated, if the value is less than 0( it is negative, since all the values are integers), replace the value with 100.
Answer:
Please find the answer below
Explanation:
// Online C compiler to run C program online
#include <stdio.h>
int main() {
// Write C code here
//printf("Hello world");
int userNum;
int i;
int j;
scanf("%d", &userNum);
/* Your solution goes here */
for(i = 0; i<=userNum; i++){
for(j = 0; j <= i; j++){
printf(" ");
}
printf("%d\n", i);
}
return 0;
}
HTML5
HTML5 has fewer plug-ins like the ability to standardize how audio and video are presented on a Web page. It introduces the <video> element designed to remove the need to install 3rd party add-ons and plug-ins like adobe flash player. It also adds the <audio> element too that allows pages to smoothly add audio files.
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>
