Another type of machine learning is semi-supervised machine learning.
It's similar to supervised machine learning system, but it uses both labeled and unlabeled data in
addition to supervised data.
The term labeled data refers to information that has a meaningful tag that allows the algorithm to understand
the data, whereas unlabeled data does not have such a tag, which means that machine learning algorithm
can be taught to label data that has not been seen labeled.
Several unlabeled data sets are provided to the classifier after it has been trained on the labeled
data.
Upon classification and classifying the unlabeled data, the model is further retrained using the originally
available labeled data to increase the accuracy of the model.
The goal is to learn a function that accurately predicts outputs based on inputs similar to supervised
learning, but with much less labeled data.
Let's take an example.
Here we have an input of animals elephant, cow, and camel.
And those are the pictures of those animals.
But the problem here, we don't have all the labels.
We have partial labels like camel and cow that we don't have the label for the elephant in semi-supervised
learning here we are training the model and and here it uses a small amount of labeled data combined
with large amount of unlabeled data to train models.
The goal is to learn a function that accurately predicts output based on inputs.
So here we are predicting that this animal would be an elephant.
So the prediction it's an elephant.
This is the goal of using this type of machine learning a mix where the teacher provides some concepts
in a class, and the students and the student practices with homework assignments based on those concepts.
So this is like a similar to a student being taught some concepts in class and trying to figure out
the and solve the homework assignment based on those concepts.
Okay, so we are not training all the data.
We don't have all the input data.
We don't have all the labels.
We have partial labels.