Support Vector Machine (SVM) Introduction — Machine Learning

SVM stands for support vector machine, and although it can solve both classification and regression problems, it is mainly used for classification problems in machine learning (ML) . SVM models help us classify new data points based on previously classified similar data, making it is a supervised machine learning technique. The companion resources to this article can be found either on Google Colab  or Github .

#data-science #support-vector-machine #machine-learning #artificial-intelligence #education

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Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

• ML and AI can offer high security in the transportation industry.
• It offers high reliability of their services or vehicles.
• The adoption of this technology in the transportation industry can increase the efficiency of the service.
• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

• Identifying Diseases and Diagnosis
• Drug Discovery and Manufacturing
• Medical Imaging Diagnosis
• Personalized Medicine
• Machine Learning-based Behavioral Modification
• Smart Health Records
• Clinical Trial and Research
• Better Radiotherapy
• Crowdsourced Data Collection
• Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

• Fraud prevention
• Risk management
• Investment predictions
• Customer service
• Digital assistants
• Marketing
• Network security
• Loan underwriting
• Algorithmic trading
• Process automation
• Document interpretation
• Content creation
• Trade settlements
• Money-laundering prevention
• Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

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Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

• Improved Unsupervised Algorithms
• Increased Adoption of Quantum Computing
• Enhanced Personalization
• Improved Cognitive Services
• Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Hire Machine Learning Developer | Hire ML Experts in India

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

• ML and AI can offer high security in the transportation industry.
• It offers high reliability of their services or vehicles.
• The adoption of this technology in the transportation industry can increase the efficiency of the service.
• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

• Identifying Diseases and Diagnosis
• Drug Discovery and Manufacturing
• Medical Imaging Diagnosis
• Personalized Medicine
• Machine Learning-based Behavioral Modification
• Smart Health Records
• Clinical Trial and Research
• Better Radiotherapy
• Crowdsourced Data Collection
• Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

• Fraud prevention
• Risk management
• Investment predictions
• Customer service
• Digital assistants
• Marketing
• Network security
• Loan underwriting
• Algorithmic trading
• Process automation
• Document interpretation
• Content creation
• Trade settlements
• Money-laundering prevention
• Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

• Improved Unsupervised Algorithms
• Increased Adoption of Quantum Computing
• Enhanced Personalization
• Improved Cognitive Services
• Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Support Vector Machines (SVM)

History

This article is about an introduction to SVMs, understanding the mathematical intuition, Regularization, implementing the concept in code, and then knowing the fields of its applications. Right then tighten your seat belt and let’s enter this wonderland of beautiful concept Support Vector Machines (SVM). Vladimir Vapnik one of the pioneers in the Machine Learning world had discovered the SVMs in the early 1960s. But he was unable to prove the magic of this Algorithm computationally as he was lacking the needed resources back then in the 1960s. It was in the early 1990s that the Bell Laboratory invited him to come forward to the US related to the research work. Back then in 1993, there was quite a fast track research going on in the character digit recognition area. Vapnik bet that his work of SVMs could do much better than the neural nets in digit recognition. So one of my colleagues tried to apply the SVM and it worked wonders to his surprise. So finally the work which was discovered in the 1960s got recognition in the 1990s when it was actually implemented to give better computational results.

Introduction

Support Vector Machines is a supervised machine learning algorithm used in Classification and Regression modeling. The purpose of SVMs is to identify a sample in space and segregate it based on the classes. For example, if I give you images of fruits as a list then the algorithm classifies the input images that are recognized as Apple or Strawberry. Remember in the above example the classification was based on the assumption that the classes are easily separable. What if the images were distributed in such a way that its difficult to split the classes by drawing a straight line? Before we analyze in that direction we need to familiarize a few basics.

#machine-learning #supervised-learning #support-vector-machine #svm