DEV Community: Ekta Chaudhary

Supervised and Unsupervised Learning

Ekta Chaudhary — Tue, 23 Feb 2021 10:08:10 +0000

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.*

Supervised Learning handles mainly two types of Machine Learning Problems:-

Classification Model
Regression Model

Classification Model:-

In this, the goal is to predict a class label, which is a choice from a predefined list of possibilities. Here, the target variable is a category variable.
Example:- Classifying emails as email spam or not spam.

Regression Model:-

In this, the goal is to predict a continuous number or floating-point number in programming term. In this, the target variable is a continuous numerical variable
Example:- Predicting a person's annual income from their education, their age, and where they live as the amount can be any number in a given range.

An easy way to distinguish between classification and a regression model is to ask whether there is some kind of continuity in the output. If there is continuity between possible outcomes, then the problem is a regression problem.

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.

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

Unsupervised Learning is further divided into two types:-

Transformations of the dataset
Clustering

Transformation of the dataset

Unsupervised transformation of a dataset are algorithms that create a new representation of the data which might be easier for human beings or other machine learning algorithm to understand compared to the original representation of the data. A common application of unsupervised transformation is dimensionality reduction, which takes a high-dimension representation of data consisting of many features, and finds a new way to represent this data that summarizes the essential characteristics with fewer features.
Example:- Reduction to 2_d for visualization purposes.

Clustering Algorithms

Partitioning data into distinct groups of similar items.
Consider the example of uploading photos to a social media site. To allow you to organize your pictures, the site might want to group them together with the same person. The site doesn't know which pictures to show whom. A sensible approach would be to extract all faces and divide them into the group of faces that look similar. Hopefully, the image can be grouped together for you.
The best example of this is Google Photos or Facebook Photos.

Challenges in Unsupervised Learning

A major challenge in unsupervised learning is evaluating whether the algorithm learned something useful. Unsupervised learning algorithms are applied to data that doesn't contain any label information, so we don't know that the output is right or not.

Machine Learning Models

Ekta Chaudhary — Sun, 21 Feb 2021 07:01:54 +0000

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:-

Machine Learning Models
Labelled Data
Unlabelled Data
Supervised Learning
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

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 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.

Brief Introduction to Machine Learning for Beginners

Ekta Chaudhary — Fri, 19 Feb 2021 15:24:39 +0000

Machine Learning

Machine Learning is the ability of Machines, that is, computers learn and improve from their past experiences or data without being explicitly programmed.

PAST EXPERIENCES----->PATTERN RECOGNITION----->PREDICTIONS

Applications of Machine Learning:-

Facebook NewsFeed
Facebook Photo Auto-tagging feature
Product recommendations by shopping portals
Automatic recommendation of movies to watch on various platforms like Netflix, Amazon Prime, Hotstar, etc.

Some of the Advanced Applications of Machine Learning:-

Identifying Frauds in Banking
Sentiment Analysis
Amazon Go
Chatbots
Self Driven Cars

Apart from Commercial Applications, Machine Learning has a tremendous influence on the way data-driven research is done today.

Finding distant Planets.
Discovering New Particles.
Analyzing DNA Sequences.
Personalized Cancer treatment, etc.

Why Machine Learning?

In the early days of intelligent applications used hand-coded rules of if and else decisions to process data or adjust to user input.

Think of a spam filter whose job is to move the appropriate incoming email messages to a spam folder. We could make a blacklist of words that would result in an email being marked as spam. This would be an example of using an expert-designed rule system to design an "intelligent" application.

How Machine Learning Works?

Machine Learning is the ability of machines to learn from past experiences to make predictions or recommendations.
Data is at the heart of Machine Learning.

In Tradition, We give Input to the Program and get Output as the result.

Input------>Program------>Output

But, In Machine Learning Model We give Data to the Machine Learning Model and get Intelligence(Algorithms, Rules) as a result.

Data------>ML Model------>Intelligence

Knowing Your Task & Knowing Your Data

Quite possibly the most important part of the Machine Learning process is understanding the data you are working with and how it relates to the task you want to solve. It won't be effective to randomly choose an algorithm and throw your data at it.

It is necessary to understand what is going on in your data set before you begin building a Model.

Note:-

Hey, I'm a beginner too. I'm sharing my journey. Hoping that someone might get benefit from this.
If you have any suggestions or questions for me ask me in the comment section.