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welcome to predictive modeling and
machine learning with matlab
in this course you'll learn how to build
and evaluate supervised machine learning
models
machine learning is used in a wide range
of applications
from identifying risk factors for
disease
to designing advanced safety systems for
automobiles
let's take a look at the types of
machine learning covered in the
specialization
in course 2 you performed unsupervised
machine learning by clustering data
you use unsupervised machine learning
when you're looking to uncover groups or
patterns
there is no correct answer which is why
it's called unsupervised learning
in contrast the goal of supervised
machine learning is to build a
predictive model
the data must contain both a set of
features and the known response for each
observation
if that response is a continuous numeric
variable then you have a regression
problem if the response is one of a
discrete set of values or categories
it's called classification
for example predicting the power
generated from solar panels based on
weather conditions and time of year is a
regression problem while predicting if a
patient is at risk for heart disease is
a classification problem
the models and metrics used to evaluate
regression and classification models are
very different however the same workflow
applies
understanding this workflow is key to
successfully applying supervised machine
learning
the first step is to import and explore
the data
what does it contain
is there potentially enough information
to answer your question
before you start building a machine
learning model you need to understand
the data
you gained these skills in course one of
the specialization
the goal is to make predictions on new
observations
to evaluate how well your model will do
with new data it's important to split
your data set
one split is the data set you will use
to train your model
this data set contains training and
validation data as you'll see shortly
the other is referred to as the test
data
test data is used to simulate new
observations that your final model has
never seen
set the test data set aside and forget
about it until you have your final model
next you pre-process the combined
training and validation data
this includes tasks like cleaning and
creating new features
skills you learned in course two of this
specialization
once you've prepared your data
it's time to start the process of
supervised machine learning the focus of
this course
when trying to find the best model
you'll need to try a variety of
approaches
which type of model should you use
what features are important
how do you find the optimal model
parameters
you'll need to iterate many times to
find the best fit for your data
notice that you split the data again for
this step
you want a model that captures the
underlying trends without fitting the
noise in the data
fitting the noise is called overfitting
using validation data during the
training process helps prevent
overfitting and you'll learn different
approaches to using validation data in
this course
when you've found the best model options
you train a final model
using the combined training and
validation data
once you have your final model it's time
to apply it to new observations
this is where you use the test data that
you set aside earlier
after applying the same pre-processing
steps use your model to predict outcomes
on the test data
this is how well you can expect your
model to perform on new observations
now you can see how each course in the
specialization
fits into the supervised machine
learning workflow
so let's see how this course is
organized
in modules one and two you'll learn how
to train common regression and
classification models
you'll use apps to quickly try different
approaches and identify the models that
are most promising for your application
you'll also learn metrics and techniques
for evaluating regression and
classification models
once you know how to train and evaluate
models you're ready to start applying
the workflow
in module 3 you'll use validation data
to help prevent overfitting
and help you choose the best model
you'll perform feature selection
and optimize model parameters to improve
accuracy
and you'll end the module by applying
your final model to test data to assess
the model's performance
in module 4 you'll learn strategies for
addressing potential issues
for example what if you have a class
imbalance meaning most of your
observations belong to the same class
at the end of this course you'll have
developed the skills needed to apply
supervised machine learning techniques
on your data
so let's get started
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you