INTRODUCING MACHINE LEARNING – Variables

The main aim of my blog is to explain the basic and advanced concepts visually using simple hand-written pictures.

In addition some pictures and charts are used to explain concepts effectively.

Let us start this using a fundamental concept Variable.

VARIABLES?

Variables are any characteristics, number, or quantity that can be measured or counted.

A variable may also be called a Datapoint.

Variables are classified into two types, they are as follows.

Variables can be two types, either Categorical or Numerical.

Categorical variables are qualitative in nature and Numerical variables are quantitative in nature.

These can be further explained using following diagram.

Categorical Variables

Numerical Variables

The next important concept to understand is regarding Supervised learning and Unsupervised learning.

• Supervised Learning

Supervised learning is generally used for prediction purpose.

Supervised learning has a target column (also known as Class labels).

• Unsupervised Learning

In general, Unsupervised learning is used to do Analysis of a dataset.

Unsupervised learning do not have Target column(class labels).

• Categorising Variable type with respective kind of problem.

There are various Machine learning Algorithms/ Models are used to process the datapoints and various predictions and analyses are made.

Some of important models used for Supervised and Unsupervised learning will be discussed in upcoming blogs.

This post is just an introduction to Machine learning and Variables.

In the upcoming blogs the Models will be explained in detail.

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