We talked about supervised learning.
Using this method, we must provide labeled data, also
called examples, as part of the training phase.
If I'm building an image classifier that should identify the
type of animal on a given image, then I need
a large number of examples images that are labeled with
the type of animal in each image.
This information is used during the training phase.
Unfortunately, the vast majority of available
data in many application in many
industry use cases is usually unlabeled.
We know the input feature x, but we don't
have the labels y to train our model.
If we still want to use supervised learning, then
we can consider several options like searching available label
data from other sources that can be free in
the internet, or maybe purchase a label dataset
from a third party company.
The next option will be to label
the data somehow as a manual process.
Let a group of people, expert, go over some
portion of the dataset and label it. Okay.
This can be an expensive and very slow process, and
in some cases the amount of manually labeled data will
not be good enough to train a good model.
Still, it is a practical
option for using supervised learning.
As you may guess, the next option to consider
is to use unsupervised learning, which is not as
widespread and frequently used as supervised learning.
Unsupervised learning is learning without a
teacher supervising the learning process.
The goal is to identify automatically
meaningful patterns in unlabeled data.
We don't need to provide the algorithm a
labeled dataset, which makes it a very
attractive option to some use cases.
Unsupervised learning is used for
two main fundamental tasks.
The first one is called clustering and
the second one is called dimension reduction.
Clustering is about summarizing and grouping
similar instances together into clusters.
It is helping to find a small number of attributes
that will represent the patterns in the data and by
doing that, uncover the underlying structure of the dataset.
Clustering as a method is widely used
for search engines, customer segmentations method, image
segmentation, simple data analysis, and more.
We'll talk about it in the next lecture.
The second type of task is called
dimension reduction, which is about reducing the
complexity of the input data.
This method, under unsupervised learning, is sometimes used
to preprocess the input data and compress it
before feeding into a supervised learning algorithm.
Okay, the idea will be to compress the
data while maintaining its structure and usefulness.
Let's review each one of them.
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