Hello!
In this video, we'll provide some basics on supervised and unsupervised learning.
An easy way to begin grasping the concept of supervised learning is by looking directly
at the words that make it up.
Supervise means to observe and direct the execution of (a task, project, or activity)
Obviously, we aren't going to be supervising a person?
Instead, we'll be supervising a machine learning model that might be able to produce classification
regions like we see here.
So how do we supervise a machine learning model?
We do this by 'teaching' the model.
That is, we load the model with knowledge so that we can have it predict future instances.
But this leads to the next question, which is, how exactly do we teach a model?
We teach the model by training it with some data from a labeled dataset.
It's important to note that the data is labeled.
And what does a labeled dataset look like?
Well, it can look something like this.
This example is just taken from the Iris dataset, which is a famous dataset used for machine
learning.
Let's start by classifying some components of this table.
The names up here, which are called Sepal length, Sepal width, Petal length, Petal width,
and Species are called the Attributes.
a) The columns are called Features, which include the data.
b) If you look at a single data point on a plot, it'll have all of these attributes.
That would make a row on this chart, or an observation.
c) Looking directly at the value of data, you can have 2 kinds.
d) The first is numerical.
When dealing with machine learning, the most commonly used data is numeric.
e) The second is categorical? that is, it's non-numeric, because it contains characters
rather than numbers.
In this case, it is categorical because this dataset is made for Classification
f) Usually, a dataset like this will be put into a .csv file, or comma-separated value
file.
This file separates Observations by new lines, and attributes by commas (hence comma-separated)
There are two types of supervised learning, classification and regression.
Since we know the meaning of supervised learning, what do you think unsupervised learning means?
Unsupervised Learning is exactly as it sounds.
We do not supervise the model, but we let the model work on its own to discover information
that may not be visible to the human eye.
Unsupervised learning uses machine learning algorithms that draw conclusions on UNLABELED
data.
Unsupervised learning has more difficult algorithms than supervised learning, since we know little
to no information about the data, or the outcomes that are to be expected.
With unsupervised learning, we're looking to find things such as
groups/clusters, perform density estimation, and
dimensionality reduction.
In supervised learning, however, we know what kind of data we're dealing with, since it
is labelled data.
In comparison to supervised learning, unsupervised learning has:
fewer tests and Fewer models that can be used in order to
ensure the outcome of the model is accurate.
As such, unsupervised learning creates a less controllable environment, as the machine is
creating outcomes for us.
Now, let's investigate a machine learning algorithm.
Here we can see the output of an algorithm applied to examining poisonous mushrooms.
As you can see, it tells us if a mushroom is poisonous or edible, depending on its features.
So, without looking at the data itself, do you think this a supervised or unsupervised
machine learning problem?
The answer is supervised Machine Learning, as this is an example of classification.
That is, it classifies mushrooms into two different labels: poisonous or edible.
Specifically, it does so using a classification tree algorithm.
The biggest difference between Supervised and Unsupervised Learning is that supervised
learning deals with labeled data while Unsupervised Learning deals with unlabeled data.
In supervised learning, we have machine learning algorithms for classification, and Regression.
Classification is the organization of labeled data.
Regression is the prediction of trends in labeled data to determine future outcomes.
While it's possible to classify data using regression, covering that now is out of scope
for this course.
In unsupervised learning, we have clustering.
Clustering is the analysis of patterns and groupings of unlabeled data.
Thanks for watching!