Machine Learning

Abstract

Machine Learning (M.L) is a scientific study of statical modals and algorithms that computer used to perform a certain task. Learning algorithms used in many applications that our used in daily life. Image recognition is a well known for identify an object as a digital image, one of the reason it work so well is because a learning algorithm that has based on the intensity of the pixels black & white images and color images. These algorithms are used for various purposes like predictive analytics, virtual assistants etc. The main advantage of using machine learning is that, once an algorithm grasp what to perform with particular data, then it works automatically.

Introduction

I. INTRODUCTION

Machine Learning has become a very important feature of everyday life. It is a method of analyzing algorithm and designing to learn automatically, which allows computer to analyze a large amount of data, find out the hidden laws for prediction or classification based on characteristics of data. It is a subset of AI in which algorithmic models are trained to perform specific tasks by recognizing and learning pattern.

Machine Learning (M.L) is a branch of Artificial Intelligence (AI) that enables computer to self learn from training data and improve over time without being explicitly programmed. Machine Learning algorithms are able to detect patterns in data and learn from them, in order to make there own predictions. Machine Learning can be put to works on massive accounts of data and can perform much more accurately than humans. It helps you to save time and money on tasks and analyses , like solving customer pain points to improve customer satisfaction, and data mining from internal sources.

II. RESEARCH ON MACHINE LEARNING    DEVELOPMENT

A. Theoretical System Continues to Mature

In the future development process, the mechanical theory system will also be further optimized, and its content branches and coverage will also be expanded. In the initial formulation process of machine learning content, its content is mainly applicable to some automation industries and the content of the entire theoretical system has not been completely sound. In practical application the content of the entire theoretical system is not application in some field in response to such situations, the next stage machine learning theory will be continuously strengthened, and the degree of refinement of the content will also be strengthened, which provide convenient conditions for the subsequent promotion of machine learning.

B. Integration of Multiple Digital Technologies

At this stage, relying on internet technology  has produced many branch technologies such as internet of things technology, digital technology, cloud computing technology. These technology can provide many convenient conditions in the process of data calculation. Although these digital technologies are still in initial stage of integration, with the rapid development of technology, the integration of digital technology is also constantly improving. Besides, in the future development process, these technology will be combined with algorithms to form a new technology application system, there by lying a foundation for the further improvement of data analysis speed.

Conclusion

Machine Learning is still in its infancy, and it mainly relies on supervised learning and does not fully overcome weak artificial intelligence. Relevant personal need to constantly improve the theoretical foundation and practice of machine learning. In the corresponding scientific field and the development of computer technology, we should provide a good environment for machine learning and the development prospect of machine learning is very board. In addition it is also necessary to actively learn from the experiences and lessons from developed country, set up machine algorithms situation for the development of domestic enterprises, and provide technical support for the economic development of the industry.

Copyright

Copyright © 2022 Ravit Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.