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Machine Learning

Machine Learning - WebCreatify

Machine learning is the branch of science where computers act without being explicitly programmed. It is a concept which you use every day without even knowing about it. It is a subset of Artificial Intelligence (AI) in which machine is trained to perform several operations. Machine Learning was introduced to solve complex data, analyze big tasks, deliver accurate outputs, improves performance with fast speed on a large scale. It reduces risks.

Machine Learning is an expanding technology that has enabled computers to learn automatically from the previous operations that were performed on the computer. It uses various algorithms to implement models and make predictions from the previous data. Machine Learning is used for image recognition, speech recognition, and many more. You might know about tagging your friends on Facebook that is done using machine learning. There are various techniques of ML such as Supervised, Unsupervised, and Reinforcement learning.

 

fig.no.1.Machine Learning(technology.com)

Machine learning combines data with mathematical tools to predict an output. This output is then used by corporate to makes actionable perception. ML is also related to data mining. The machine receives input data, by using algorithms it produces the output. The core objective of machine learning is learning and reasoning.

ML works on the recommendations. If you use Hotstar application, then the recommendations are based on your history like which type of movies, serials you have watched before. Then, it provides recommendations that will help you to get your favorite movies. ML is used for fraud detection, automate tasks, prediction maintenance, etc.  Machine learning brings computer science and statistics together for making predictive models. It constructs algorithms that learn from historical data. The more we will provide the information, the higher will be the performance. Machine Learning uses statistical algorithms and neural networks to provide solutions. It has changed the way of thinking about the problem.

 

fig.no.2.Introduction to machine learning(staticjavatpoint.com)

History of ML

Machine Learning was introduced by Arthur Samuel in the year 1959. He designed a first program that could learn from the previous data. In the 21st century, many businesses started working with machine learning to increase calculation potential, in order to stay ahead in the competition.

How does Machine Learning work

The accuracy of output which is predicted depends totally upon the amount of data, as the huge amount of data helps predicts the output more accurately. 

Machine learning works two main techniques:

Supervised learning : 

It allows us to collect data from a previous ML deployment. Supervised learning works the same as humans actually learn. In this technique, training sets are provided to the computer through which the system can learn the relationship between given inputs and outputs. Supervised learning can be used to predict new data when the output data is known. It is based on supervision. 

Unsupervised learning : 

It helps to find all kinds of unknown patterns in data. In unsupervised learning,  the explicit output variable is not provided. It can be used to classify data. Two common unsupervised learning tasks are clustering and dimensionality reduction.  In clustering, data points are grouped into clusters such that elements are similar to each other in a particular group. It is mainly done for market segmentation.

 

fig.no.3.Working of machine learning(javatpoint.com)

Applications of  Machine Learning 

1. Automation.

Machine learning works autonomously in any field without human beings. For example, robots perform protect workers from repeated and dangerous tasks in manufacturing plants.

2. Finance Industry.

In the finance industry, banks are mainly using ML to find patterns inside the data, to prevent fraudulent transactions, to handle huge data with faster speed and accurate results

3. Government organization.

The government makes use of ML to manage public safety. Aadhar card data is huge data to handle and very complex. ML helps to solve this complex data in a well-organized way.

4. Healthcare. 

Machine learning was first used in the healthcare sector for image detection. ML can convert human data into clinical perception which will help doctors to get better outcomes and reduce costs. 

5. Business.

Machine learning in business enhances scalability and improves business operations. ML predicts maintenance and reduces risks, eliminates manual work of data entry, detects spam, analyze financial issues, provide better recommendations,  increases customer satisfaction

6. Marketing.

Earlier there were mathematical tools to estimate the customer value analysis which was very complex. ML manages the customer relationship and marketing campaign.

7. Supply Chain.

ML manages to schedule in SCM, identifies product damage, improves the delivery process, demand forecasting, and boosts end to end visibility.

Conclusion:

Machine learning has the ability to detect what the human eye couldn’t. It catches complex patterns. The machine can think like a human and this ML is used in every sector. ML provides a very efficient and fast output which saves time. Nowadays, ML plays a very important role to solve real-world problems, make decisions. ML helps industries to bring smarter and innovative products. This ML technology will improve lives in numerous ways and solve each and every problem.


Sneha Paherwar

Author

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