Applications of machine learning.
In this blog, we will know some important applications of machine learning. We are already using machine learning but a lot of people are not aware of it.
Machine learning has a wide variety of applications, it is versatile, it can be used in many fields.
In the last blog we have discussed how Google uses machine learning in a lot of its applications.If you want to know about machine learning applications at Google then click here.
Table of contents.
machine learning in speech recognition.
a. history of speech recognition.
b. meaning of speech recognition.
c. facts of voice assistants.
How does machine learning work in insurance?
How does machine learning is used in telecommunication?
How does machine learning is used in traffic prediction?
Use of machine learning in email spam.
1. Machine learning in speech recognition.
a. History of speech recognition.
The first speech recognition system was designed by Bell laboratories in 1952, this speech recognition system was focused on numbers not on words.
Ten years later, shoebox was introduced by the international business machines corporation (IBM).
b. What is mean by speech recognition
It is a process in which a computer identifies the sound produced by human speech.
Examples : Microsoft's cortana, Google assistant, Amazon's alexa, apple's Siri and many more.
c. Facts.
siri was introduced in the year 2010 by Apple.
Microsoft Cortana was introduced in December 2015.
Amazon alexa was introduced in November 2014.
The human voice vocal folds or vocal cords are capable of producing different vibratory patterns that's why machine learning algorithms recognise the patterns.
Although, recognising the human voice is not an easy task every human has a different type of voice and the way of talking is different.
Everyone has a different style of talking. Some people used to talk slowly, some people talk fast, so it's a difficult task for machine learning algorithms.
2. Machine learning in insurance.
How does machine learning is used in insurance?. Nowadays a lot of insurance companies are using machine learning.
First use of machine learning in insurance is AI assistant/chat bots which provide the guidance for business protocols to internal agents if they have any problem.
Now, no need to call the insurance customer care for any solution because chatbots help you to find the solution very easily. If you have any inquiries you can directly chat with chatbots.
Machine learning algorithms help to claim insurance for car accidents in real time.
These machine learning algorithms were trained to identify the amount damage has been done to the insurance client car depending on the amount of damage the customer can claim the insurance in real time.
They just need a smartphone to capture the image of a car accident and in real time they can get an amount of damaged vehicles.
Machine learning reduces a lot of the workload of the management as well as clients, because clients can claim the insurance in no time.
3. Machine learning in telecommunication.
How does machine learning is used in telecommunication? Why is machine learning used in telecommunication?. You will get all these answers right here.
Like insurance companies, telecommunication companies are also using chatbots to help their customers.
In the past, if you had any problem you needed to call the customer care, you needed to wait in the queue for around 10 minutes and after waiting for a long time sometimes the call connected and sometimes it didn't and even after the call connected sometimes you will not get the answer you need.
It's the most frustrating thing and this frustration leads to changing the operator. A lot of telecom operators faced the same issue, they were not able to satisfy the customer needs. So, that's why now they started adopting chat bots.
These chatbots are available 24/7, so they can help anytime. Now, no need to wait in the queue for a long time. You can easily get the solution you need within a minute.
Chatbots are reducing a lot of workload for the management and it is also helpful for customers.
4. Machine learning in traffic prediction.
How does machine learning is used in traffic prediction? According to the World Health Organization (WHO), around 1.35 million people die every year in road traffic accidents and 3700 people everyday lose their lives due to heavy traffic.
Most of the time 5-29 age group people are involved in these road accidents, because some children's go to their schools and adults go to their office.
All these accidents happen because of human errors, because many people drive when they are drunk and some of them use their mobile phone while driving and a lot of people love listening to podcasts while driving.
All these distractions lead to accidents. Here, machine learning helps to avoid some of the accidents.
Global positioning system (GPS) is a satellite based navigation which helps to determine the location and velocity. This GPS system works 24/7. There are many algorithms which are used for tackling the GPS data.
5. Machine learning in E-mail spam.
At present email is one of the important tools for communication, you will get all the information to your mailbox whether it's about business, education and even if you purchase any product on e-commerce and many more.
That's why detecting email spam is one of the important and necessary things. Email spam can be detected by supervised learning.
Supervised learning uses classification systems for prediction of email spam.
Summary.
In this blog, we learnt the applications of machine learning. We have studied the history of speech recognition and the meaning of speech recognition.
Now, We know how machine learning is effectively used in insurance. We have studied the use of chatbots in insurance as well as telecommunications.
Now, we have information about how many accidents are caused due to heavy traffic and how machine learning is used in traffic prediction.
Lastly, we have studied the importance of email and how machine learning is used for detecting email spam and which algorithm is used for spam detection.
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