WHAT IS MACHINE LEARNING ?
Machine can learn ?
Yes, machine also can learn.Would you like to understand, let's know how !
Machine learning is the process of data learning by the machine and take decision with very less human brain.It allows you to give customized service to each person with new analytical capabilities using big data resources based intelligence for machine learning.Officially it can be defined by the scientific study with statistical machine model and algorithm made for computer system.Using this system machine can take self decision with the help of minimum human intervention.Whatever data it take or consume for machine learning is known as training data.This data will send command to the operating system and machine will take an action as per the received command.
Machine learning is working on the concept of algorithm instead of advance programming.Machine learning algorithm will timely improve getting more feedback from it's work.Machine Learning is sub branch of artificial intelligence and interrelated with IoT technology.Because it all are dependent on each other.Combining all of them we can achieve smart technology.
What is the difference between them ;
They can be differentiated by the intelligence level used in it.We can arrange them in stages as in below image. AI is early concept to give an information to machine to understand the visual aspects.machine learning is an one step further more powerful to use data in form of algorithm.It helps to improve the capability of device to faster faster response.Deep learning is the drive related advance technology which allows you to control and operate the complete machine devices.But it is necessary to know it's use and application areas depending on them will decide method.
Categories of Machine Learning Algorithms,
In machine learning input data provided is in the form of algorithm.Mainly there are three types of machine learning algorithms which are commonly used in the industry can be explained as follows,
(1) Supervised Learning :
Supervised machine learning is the set of data in mathematical models called lables.It contains desired input and output one or more for completion of learning process.Examples of algorithm used in supervised machine learning image classification, regression, prediction and active learning. Example of classification type are diagnostic, customer retention, fraud detection and image classification.Example of regression type are weather forecasting,(2) Unsupervised Learning :
In unsupervised learning system there is only input and undefined output.Which can be obtained by the grouped or clustering data points.In these method input data source is not labeled and also not trained from available data.In these clustering data is work as a different mathematical function.This function may be based on probability and assumptions.Another method is dimensionality reduction in which some key actions are big data visualization, meaningful compression, structure discovery and feature elicitation.
(3) Reinforcement Learning :
This method is different than others due to it's working.It is work based on three distinct parameter that can agent, Environment and action.This parameter has different work.Agent is decision maker and data learner, environment is that with agent interact and action is result of it.Agent get reward from correct task and penalties for the incorrect task perform by the machine or gadget without any human intervention and intelligence.Then it will decide itself what to do next time and increase in the learning capability.This learning is in form of dynamic algorithm.
Let's understand more from videos :
https://www.youtube.com/watch?reload=9&v=VbBSEFkj5EkWho are the users ?
In today's world most of the services are using data resources for the better customer experience and feedback.Due to hardening level now a days Artificial Intelligence (AI) is the first choice to make it possible.There is widespread application of machine learning and will discussed on wikipedia.Some of them are discussed in detail as given below,
Government : Government use it for safety and utility purpose.Sometimes it is used for fraud detection.
Finance : In finance fields it is help investors to find proper users and more from cybersurveillance and market safety.
Retails : In retail it help to analyse buying history and customer shopping experience, personalization, insights, supply planning etc.
Transport : This is new field which using machine learning bye the help of tracking report and information exchange, delivery report and time schedule.
Health care : Health care unit widely spread over world so it is the big platform for machine learning based instruments, apps and devices which observe health and fitness, checkup and treatment.It is work on smart monitoring of patient.
Government : Government use it for safety and utility purpose.Sometimes it is used for fraud detection.
Finance : In finance fields it is help investors to find proper users and more from cybersurveillance and market safety.
Retails : In retail it help to analyse buying history and customer shopping experience, personalization, insights, supply planning etc.
Transport : This is new field which using machine learning bye the help of tracking report and information exchange, delivery report and time schedule.
Health care : Health care unit widely spread over world so it is the big platform for machine learning based instruments, apps and devices which observe health and fitness, checkup and treatment.It is work on smart monitoring of patient.
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