Let's learn about the third type of machine learning, which is unsupervised learning.
Unsupervised learning is a type of machine learning that analyzes and models data without labeled responses
or predefined categories.
Unlike supervised learning, where the algorithm learns from input output pairs, the unsupervised learning
algorithms work solely with input data and aim to discover hidden patterns, structures, or relationships
within the data set independently, without any human intervention or prior knowledge of that data.
Meaning, it's very clear here.
We have training data set.
We have training data set with no labels passed by the algorithm creating the model.
And then we get the prediction under supervised learning for the clustering dimension reduction and
association rule mining.
Clustering A clustering technique involves grouping similar data based on defined criteria.
It's useful for segmenting data and finding patterns in each group.
Dimension reduction.
In order to find the exact information, dimension reduction reduces the number of variables considered
and the association rule.
Mining discovering relationships between seemingly independent databases or other data repositories
through association rules.
I prepared a very good example about unsupervised learning, and this image shows sets of animals like
elephants, camels and cows that represent raw data that that the unsupervised learning algorithm will
process.
Here we have the input raw data.
The interpretation stage signifies that the algorithm doesn't have predefined labels or categories for
that data.
It needs to figure out how to group or organize the data based on the inheritance pattern.
An algorithm represents an algorithm represents unsupervised learning process, which can be clustering,
dimensionality reduction, or anomaly detection to identify patterns in the data.
So based on this algorithm, the model will be trained.
The process stage shows the algorithm working on the data.
The output shows the result of unsupervised learning process.
In this case, the algorithm might have grouped the animals into clusters based on their species, for
example elephants, camels, cows, and the output.
Here, for example, you can count the number of legs and number of eyes that all of of necks.
So those are some variables that may be used for processing and may be used by the algorithm in order
to process the data.
The working of unsupervised machine learning can be explained in many steps.
The first step is collecting the data like the input raw data.
Gather data set without predefined labels or categories without any tags exactly like this image.
Go and get those images.
We don't tell the machine that those four cows or camels or elephant.
Select the algorithm.
Choose a suitable supervised unsupervised algorithm such as clustering.
Like k means.
This is a very popular algorithm.
K mean association rule learning like dimensionality reduction like PCA based on the goal.
Also we have PCA train the model on raw data, feed the unlabeled data set to the algorithm.
We get the data and all those data to the algorithm.
The algorithm looks for similarities, relationships or hidden structure within the data, then grouping
or transform data.
The algorithm organizes data into groups and clusters, analyzing that discovered groups, rules or
features to gain insight or use them for further tasks like visualization, anomaly detection, or as
an input for other models.