Answer:
The third point i.e " Use an alternating least squares (ALS) algorithm to create a collaborative filtering solution" is the correct answer .
Explanation:
The Alternating Less Squares is the different approach that main objective to enhancing the loss function.The Alternating Less Squares process divided the matrix into the two factors for optimizing the loss .The divided of two factor matrix is known as item matrix or the user matrix.
- As we have to build the machine learning model which proposes restaurants to restaurants that are based on the customer information and the prior restaurant reviews the alternating least squares is the best model to implement this .
- All the other options are not the correct model also they are not related to given scenario that's why they are incorrect options.
Answer:
B. Process, group of techniques
Explanation:
Data warehousing simply means storing large amounts of data from multiple sources to a central "warehouse" to be analyzed or simply stored for later. Data mining on the other hand involves discovering patterns in sets of data to make better decisions. Analyzing data with a group of techniques like statistics and machine learning.
Answer:
Data is a piece of an Information or a raw form of information while an Information is a processed Data.
Explanation:
An example of Data would be Alphabets of someone's name while the name would be an example of an Information.
On it's own, data makes no sense and needs to be processed to become Information which makes sense.
Answer:
The second one:
num = int (input("Enter a number between 1 and 100: "))
c = num
while (c <= 100):
print (c)
c = c + 1
Explanation:
First of all, you don't know Python... (LEARN IT)
Second of all, The first loop doesn't make sense? num 100
And the second one works, you can try compiling it (lazy to explain...).
Answer:
red
Explanation:
color is set to red in the last line of being defined.
