Answer:
Webcasts
Explanation:
The rest of the options don't need to be streamed as there isn't a continuous flow of information.
Answer:
harris_poll_ranking = int(input("Enter team's Harris Poll ranking [1 - 2,850]: "))
coaches_poll_ranking = int(input("Enter team's Coaches Poll ranking [1 - 1,475]: "))
computer_ranking = float(input("Enter team's computer ranking [0 - 1]: "))
harris_poll_score = harris_poll_ranking / 2850
coaches_poll_score = coaches_poll_ranking / 1475
bcs_score = harris_poll_score / 3 + coaches_poll_score / 3 + computer_ranking / 3
print(bcs_score)
Explanation:
*The code is in Python.
Ask the user to enter the harris_poll_ranking as int, coaches_poll_ranking as int and computer_ranking as float
Calculate the harris_poll_score, divide the harris_poll_ranking by 2850
Calculate the coaches_poll_score, divide the coaches_poll_ranking by 1475
Calculate the bcs_score, harris_poll_score, coaches_poll_score and computer_ranking by 3 and sum them
Print the bcs_score
Answer:
A) Maintaining the shared connections between offices
Explanation:
Plz mark brainliest
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>
