Page Nav

HIDE

Grid

GRID_STYLE

Classic Header

{fbt_classic_header}

Header Ads

Trending News

latest

What is Machine Learning ? | About its classification and why it is important

Welcome as a Guest an d Leave as a  Friend Hello, TechTrending readers welcome to our website hope you guys enjoying Our  Xiaomi Mi Mi...

Welcome as a Guest and Leave as a Friend

Hello, TechTrending readers welcome to our website hope you guys enjoying Our Xiaomi Mi Mix 4 Full specifications, features and price in India today our topic is about MACHINE LEARNING So let's move into the topic post by Bhanu Keerthi



Machine Learning is the study of computers that have the capability to learn without being explicitly programmed. It is also the category of algorithm that allows the software application to be more accurate in predicting outcomes without being programmed.
Machine Learning is a personalize online ad delivery in almost real-time and which is actively being used today.

Introduction : Machine Learning (ML)
The term Machine Learning was created by Arthur Samuel that was in the year 1959, excellent in the field of computer gaming and artificial intelligence.
He stated that "it gives the computer the ability to learn without being explicitly programmed" . Within the field of data analysis, machine learning is used to devise complex models and algorithms that are known as predictive analytics. These analytical models allow researchers, data scientists, engineers and analysts to produce reliable, repeatable decisions.

Classification of machine learning


1. Supervised learning: 

In this, we train the machine using data which is well labeled. It states that some data is tagged with the right answer. A supervised machine algorithm learns from labeled training data. The advantage is it allows you to collect data or produce a data output from the previous experience.       

2. Unsupervised learning:

 It shows the methods for human use to figure out that certain objects or events are from the same class 
    

3. Reinforcement learning: 

If you present the algorithm without the examples that don't have labels, as in unsupervised learning then you can accompany an example with positive or negative feedback according to the solution the algorithm proposes comes under the reinforcement learning.

4. Semi-supervised learning: 

In the algorithm, we have a combined combination of labeled and unlabeled data. It has a very small amount of labeled data and a large amount of unlabeled data.


Applications of Machine Learning:

Web search engine: 

people use it for research, shopping, information or entertainment. Most of them look for answers or for the data with which they can take a decision.

Photo tagging application: 

In most of the social media photo tagging in done. It is done because of a face recognition algorithm that runs in the application.

Government: 

Government agencies have multiple data sources. The machine learning helps to identify thefts and detect fraud.

Oil and Gas: 

The application of machine learning is helpful to the industry from the starting to the analyzing of underground minerals.

No comments

thanks for messaging