YouTube Transcript: Supervised Machine Learning explained with Examples | 3 Examples of Supervised Machine Learning💡🌐 | YouTubeToText
YouTube Transcript: Supervised Machine Learning explained with Examples | 3 Examples of Supervised Machine Learning💡🌐
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examples of supervised machine learning
supervised learning is one of the most
common types of machine learning where
the algorithm is trained on a labeled
data set in a labeled data set the
output variable is already known for
each input variable and the algorithm
learns to map inputs to outputs based on
this training data supervised learning
is used because it relies on labeled
training data where each image has a
known category the model learns to
associate features in the images with
these predefined
categories let's consider a supervised
learning example where we predict
whether an email is Spam or not spam in
this case we'll use features associated
with emails to classify them into these
two categories features inputs the
features of the email can include
various characteristics like the
presence of certain keywords for example
when free money the senders email
address the number of exclamation marks
in the email the length of the email
number of words or
characters whether it contains specific
patterns like deer name or click here
label output the label indicates whether
the email is Spam or not spam or ham
this is a binary classification problem
let's illustrate this with a simplified
diagram on the left side we have two
examples of email features in in puts
the first email has certain keywords an
unknown sender multiple exclamation
marks a longer length and a specific
pattern the second email has different
characteristics in the middle we have
the model our supervised learning
algorithm it takes these features as
input and learns to make predictions on
the right side we have the output which
is the label or prediction for the first
email the model predicts spam and for
the second email mail it predicts not
spam or ham supervised learning in this
case helps us build a model that learns
the relationship between the email
features and the labels spam or not spam
once drained the model can automatically
classify new unseen emails as spam or
not spam based on their
characteristics let's explore another
supervised learning example where we
predict whether a student will pass or
fail an exam based on two features
features inputs one the number of hours
a student studied two the number of
hours a student slept label output
whether the student passed or failed the
exam let's create a diagram to
illustrate this on the left side we have
two examples of student features inputs
the first student studied for 4 hours
and slept for 7 hours the second student
studied for 2 hours and slept for 5
hours hours in the middle we have the
model our supervised learning algorithm
it takes these features as input and
learns to predict whether a student will
pass or fail the exam on the right side
we have the output which is the label or
prediction for the first student the
model predicts parus and for the second
student it predicts fail supervised
learning in this example helps us build
a model that understands the
relationship between the hours studied
hour slept and the outcome pars or fail
once drained the model can predict
whether a new student will pars or fail
the exam based on their study and sleep
patterns another example of supervised
learning problem predicting whether a
credit card transaction is fraudulent or
legitimate based on transaction details
features inputs one transaction amount
the amount of money involved in the
transaction two merchant category
the type of merchant where the
transaction occurred for example grocery
store online retailer free time of day
the time when the transaction took place
for example morning afternoon evening
four location the location where the
transaction occurred for example City
Country label output whether the
transaction is classified as fraudulent
fraud or legitimate legit here's a
diagram to illustrate this on the left
side we have two examples of transaction
features inputs the first transaction
has a relatively small amount is from a
grocery store occurred in the morning
and took place in New York USA the
second transaction has a larger amount
is from an online retailer occurred in
the evening and the location is unknown
in the middle we have the model our
supervised learning algorithm it takes
these transaction details as input and
learns to predict whether the
transaction is classified as fraudulent
fraud or legitimate legit based on the
transaction features on the right side
we have the output which is the label or
prediction for the first transaction the
model predicts legit and for the second
transaction it predicts fraud in this
example supervised learning helps us
build a model that understands the
relationship between transaction details
and the likelihood of a transaction
being fraudulent the model can then
classify in new transactions as
fraudulent or legitimate based on their
features thanks for watching if you like
the tutorial please give it a thumbs up
and subscribe to our YouTube channel for
future tutorials
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