Coursera - Machine
Learning test
questions and
answers 2025
Supervised Learning
Produce more "right answers" using the data.
In supervised learning, we are given a data set and already know what our
correct output should look like, having the idea that there is a relationship
between the input and the output.
Supervised learning problems are categorized into "regression" and
"classification" problems. In a regression problem, we are trying to predict
results within a continuous output, meaning that we are trying to map
input variables to some continuous function. In a classification problem,
we are instead trying to predict results in a discrete output. In other
words, we are trying to map input variables into discrete categories.
Regression Problem
Predict continuous valued output (price).
Classification Problem
Predicting a discrete value output, 0 or 1, cancerous or not cancerous.
Problem 1: You have a large inventory of identical items. You want to
predict how many of these items will sell over the next 3 months.
Problem 2: You'd like software to examine individual customer accounts,
and for each account decide if it has been hacked/compromised.
Should you treat these as classification or as regression problems?
A) Treat both as classification problems.
B) Treat problem 1 as a classification problem, problem 2 as a regression
problem.
, C) Treat problem 1 as a regression problem, problem 2 as a classification
problem.
D) Treat both as regression problems.
C)
Problem 1 is probably a
regression problem.
Problem 2 is definitely a classification problem.
Unsupervised Learning
You don't know beforehand what the right answer is.
Can you find structure in the data?
Clustering algorithms, as an example.
Unsupervised learning allows us to approach problems with little or no
idea what our results should look like. We can derive structure from data
where we don't necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships
among the variables in the data.
With unsupervised learning there is no feedback based on the prediction
results.
Of the following examples, which would you address using an
unsupervised learning algorithm? (Check all that apply.)
A) Given email labeled as spam/not spam, learn a spam filter.
B) Given a set of news articles found on the web, group them into sets of
articles about the same stories.
C) Given a database of customer data, automatically discover market
segments and group customers into different market segments.
D) Given a dataset of patients diagnosed as either having diabetes or not,
learn to classify new patients as having diabetes or not.
B and C
Training Set
Our job is to learn from the data to predict the price of the houses.
Learning test
questions and
answers 2025
Supervised Learning
Produce more "right answers" using the data.
In supervised learning, we are given a data set and already know what our
correct output should look like, having the idea that there is a relationship
between the input and the output.
Supervised learning problems are categorized into "regression" and
"classification" problems. In a regression problem, we are trying to predict
results within a continuous output, meaning that we are trying to map
input variables to some continuous function. In a classification problem,
we are instead trying to predict results in a discrete output. In other
words, we are trying to map input variables into discrete categories.
Regression Problem
Predict continuous valued output (price).
Classification Problem
Predicting a discrete value output, 0 or 1, cancerous or not cancerous.
Problem 1: You have a large inventory of identical items. You want to
predict how many of these items will sell over the next 3 months.
Problem 2: You'd like software to examine individual customer accounts,
and for each account decide if it has been hacked/compromised.
Should you treat these as classification or as regression problems?
A) Treat both as classification problems.
B) Treat problem 1 as a classification problem, problem 2 as a regression
problem.
, C) Treat problem 1 as a regression problem, problem 2 as a classification
problem.
D) Treat both as regression problems.
C)
Problem 1 is probably a
regression problem.
Problem 2 is definitely a classification problem.
Unsupervised Learning
You don't know beforehand what the right answer is.
Can you find structure in the data?
Clustering algorithms, as an example.
Unsupervised learning allows us to approach problems with little or no
idea what our results should look like. We can derive structure from data
where we don't necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships
among the variables in the data.
With unsupervised learning there is no feedback based on the prediction
results.
Of the following examples, which would you address using an
unsupervised learning algorithm? (Check all that apply.)
A) Given email labeled as spam/not spam, learn a spam filter.
B) Given a set of news articles found on the web, group them into sets of
articles about the same stories.
C) Given a database of customer data, automatically discover market
segments and group customers into different market segments.
D) Given a dataset of patients diagnosed as either having diabetes or not,
learn to classify new patients as having diabetes or not.
B and C
Training Set
Our job is to learn from the data to predict the price of the houses.
