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Machine learning is an application of AI where the systems can automatically learn and improve from experience. The machines are not being explicitly programmed. The development of computer programs that can access data and use it to learn for themselves is the ultimate aim of machine learning.

 Learning begins with data observations. Direct experience of data is necessary to make better decisions in the future based. Computers must learn automatically without human intervention. This is the primary aim of Machine Learning.

Artificial Intelligence (AI) is everywhere. One way or the other, and you don't even know about the fact that you are using it in your day-to-day life. One of the popular applications of Artificial Intelligence is Machine Learning (ML). Herein, we share a few examples of machine learning that we use every day.

1. Virtual Personal Assistants: 

Siri, Alexa, Google Now are some of the famous examples of virtual personal assistants. They help you in finding useful information when asked over voice. You need to activate them and ask, "Alexa! Can you sing a song for me?" "What is the time now? Similar questions. The personal assistant looks out for the information, recalls your related queries, and collects data from the device you are using. You can also instruct assistants for specific tasks like an Alarm setting, Business Schedule, lunch date, and so on.

 2. Search Engine:

The reason why search engines like Google, Bing, Yahoo, etc. work so well is that the system has learned how to rank pages through a sophisticated learning algorithm.

 3. Tagging Applications:

Who doesn't like to tag their friends on Facebook and Instagram? It is possible because of a face and feature recognition algorithm that runs behind the application.

 4. Spam-Mails:

Millions of spam emails are received each day to each mail account. How does the Gmail or Hotmail classify the emails that are spam and put them in a spam folder? This is again achieved by a spam classifier running at the back end of a mail application.

 5. Traffic Predictions via GPS:

We all have been using GPS navigation services. While doing that, our current locations are being saved at a central server for managing traffic. This data is then used to build a map of the traffic. While this helps in preventing the traffic and does congestion analysis.

 6. Surveillance Camera:

The video surveillance system nowadays is powered by Machine Learning, which makes it possible to detect crime before-hand. They track the unusual behavior of people like standing motionless for a long time, stumbling, walking here and there lost or napping on benches, etc. The system gives an alert to human attendants, which can ultimately help to avoid mishaps.

 7. Similar Pins:

The core element of Computer Vision in Machine Learning, which extracts useful information from images and videos. Pinterest uses this technique to identify (or pin) the pictures or videos and recommend similar pins accordingly.

8.People You May Know:

Machine learning works on a simple concept of understanding with experiences. The friends you connect with, the profiles that you visit very often, your interests, workplace, or a group that you share with someone, suggested friends, etc. are continuously monitored by the Facebook team. How does this happen? Machine Learning is the answer.

9. Online Customer Support:

Whenever you want to talk to a customer care agent, you are most of the time directed to a chatbot. You talk to a chatbot. These bots tend to extract information from the customers and provide a meaningful solution. They understand the user queries better and serve them with better answers, which is possible due to its machine learning algorithms.

10. Product Recommendations:

You shopped for a product online a few days back, and then you keep receiving ads for shopping suggestions. You might have noticed that the shopping website or the app recommends some items that somehow match your taste. Who would have thought that it is machine learning doing the magic for you? Based on your history with the website/app, past purchases, items liked or added to cart, brand preferences, etc., the product recommendations are made.

11. Online Fraud Detection:

Cyberspace is made a secure place, and tracking monetary frauds online is one of its examples. For example, Paypal is using the Machine Learning technique for protection against money laundering. The company uses a set of tools that helps them to compare millions of transactions taking place in one day. They can also distinguish between legitimate or illegitimate transactions taking place between the buyers and sellers.

12.Medical Diagnosis:

ML provides methods, techniques, and tools that can help in solving diagnostic and prognostic problems in a variety of medical domains. It is being used for the analysis of the importance of clinical parameters and their combinations for prognosis, for example: whether breast cancer is benign or malignant.

13.Financial Services:

Key insights in financial data, as well as occurrences of financial fraud, are detected by the companies, with the help of machine learning technology. Opportunities for investments and trade increase these days. The usage of cyber-surveillance identifies those individuals or institutions which are prone to financial risk and take necessary actions in time to prevent fraud.

14.Marketing and Sales:

A price optimization strategy is used by product selling companies. The prime used in Amazon, the sort feature in selling apps, is all done using Machine Learning.

15.Government:

Government agencies like public safety have a specific need for ML, as they have multiple data sets, which can be mined for identifying useful patterns and insights. The public can interact and give feedback to the government on the use of AI and ML. It can pay the way for a better government as well.

16.Transportation:

Based on the travel history, ML can help in predicting potential problems that could arise on specific routes and accordingly suggest a different direction. The rate of your experience of a place is also ML.

17.Oil and Gas:

From Analyzing underground minerals to finding new energy sources, ML has expanded its roots. Example: enhanced solutions for Reservoir Modelling, Optimizing drilling operations, etc.

18.Emotion Classification:

It is the process of identifying human emotion. The face locks used in today's phone are because of an algorithm running behind it. The use of technology like Machine Learning to help people with emotion recognition is a relatively nascent research area.

19.News Classification:

News classification is another important application of a machine learning approach. Why or How? As a matter of fact that now the volume of information has grown tremendously on the web, picking appropriate and useful information becomes the users from the ocean of this web.

20. Age/Gender Identification:

The recently forensic related task has become an important research issue in the world of research. Age or gender identification is an important task for many cases in identifying a person. Age or gender identification can be made using a Neural Networks algorithm.

Teacherswala (P) Ltd. Team. Gurugram | New Delhi | Ghaziabad | Noida | Greater Noida | Faridabad | IMT Manesar Call: +91-9354746231 & WhatsApp : +91-9354746231 Visit Us: www.teacherswala.com, www.stcinstitute.in

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