Machine Learning Models

Make sure you have gone through the first post on Machine Learning. In that post, I have covered the basics like, What is Machine Learning, Why we use Machine Learning, How it works, and its application.

In this post, I will cover the following topics:-

1. Machine Learning Models
2. Labelled Data
3. Unlabelled Data
4. Supervised Learning
5. Unsupervised Learning

Machine Learning Models

Commonly used Machine Learning Models are:-

• Supervised Learning
• Unsupervised Learning

Before going into briefly about What is Supervised Learning and What is Unsupervised Learning. Firstly we will discuss Labelled Data and Unlabelled Data.

Labelled Data:-

• The data which contains a target variable or an output variable that answers a question of interest is called labelled data.

For example:-

• Patient was completely cured or not after the treatment?
• Is the employee still working with them?

The above two questions will have answers in the form of yes or no.

In simple term, we can say

Labelled data:-
Data that comes with a label. The data which contains a target or a dependent variable answering a question of interest is called labelled data.

Unlabelled data:-
Data that comes without a label. Unlabelled data is one that contains information about something but does not have a predefined target variable.

Difference between Labelled Data and Unlabelled Data:-

Labelled Data:-

• Used in supervised machine learning
• Needs human to label
• Expensive, hard and time-consuming to get and store
• Used for complex predicting tasks

Unlabelled Data:-

• Used in unsupervised machine learning
• Obtained by observing and collecting
• Comparatively easy to get and store
• Often used to preprocess sets of data

Now let's discuss Supervised Learning and Unsupervised Learning.

SUPERVISED LEARNING

Supervised Learning is used whenever we want to predict a certain outcome from a given input, and we have examples of input and output pairs. We build a Machine Learning model from these input pairs, which comprise our training set. Our goal is to make accurate predictions for new, never-before-seen data. Supervised Learning often requires human effort to build a training set, but afterwards automates and often speeds up other impossible tasks.

In short

• Supervised Learning model is a model which learns under supervision and this supervision is provided by labelled data.
• In this model, we are more interested in the target value.

Unsupervised Learning

In this type of learning, there is no known Output, no teacher to instruct the learning algorithm. In Unsupervised learning, the learning algorithm is just shown the input data and asked to extract knowledge from this data.

In short

• Complement of Supervised Learning
• There is no target variable involved
• Works on only unlabelled data
• Identifies if some pattern exists in the data

The next post will be completely based on Supervised and Unsupervised learning. Types of supervised and unsupervised learning. What is the target variable of both these learning?

If you have any question regarding this post ask me in the comment section.