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Basics and Introduction to Machine Learning

These days everyone is talking about Machine Learning. But should we go for it, or it is just a bubble, that will burst soon. From my point of view, You can not ignore Machine Learning. As in the future, everything should be automated, and without the help of Machine Learning we can not cope up with that situation.

Almost each and every field will be dependent on Machine Learning in the future. But there are few of the fields where Machine Learning is very much capable of.

Let’s talk about the basics of Machine Learning.

Machine learning is an application or subfield of artificial intelligence (AI). It provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The main aim of Machine Learning is to allow the computers to learn itself without human intervention or assistance and adjust actions accordingly. Machine Learning doesn’t work on traditional computing. Machine learning algorithms instead use statistical analysis in order to output values that fall within a specific range.

In this blog, we will learn about common machine learning methods or types of Machine Learning, viz. supervised, unsupervised, and reinforcement learning.

Supervised Learning

In supervised learning, the computer is provided with example inputs that are labeled with their desired outputs. The purpose of this method is to “learn” by comparing its actual output with the “taught” outputs to find errors and modify the model accordingly.

A common use case of supervised learning is to use in the stock market to anticipate upcoming fluctuations.

Unsupervised Learning

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.

The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

Reinforcement Learning

This type of machine learning requires the use of a reward/penalty system. The goal is to reward the machine when it learns correctly and to penalize the machine when it learns incorrectly.

The most common use case of this type of learning are Chess game or self-driving cars.

 

This is pretty much about the basics of Machine Learning. In our next blog, we will get some hands-on with some code and get to know how we can train models and utilize them.

 

thank-you

 

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