APPLICATIONS OF MACHINE LEARNING

Introduction Machine Learning- Applications:-

Machine learning is simply how computer "think" through and execute a task without being programmed to. It is a subset of Artificial Intelligence that involves algorithm and models that can automatically analyse and learn data to make inferences without human intervention.

Machine learning is an application of Artificial Intelligence that provides a system the ability to automatically learn and improve from the experience without being explicitly programmed. It mainly focus on the development of computer logical programs that can access any data and use it learn for themselves.

APPLICATION OF MACHINE LEARNING:-

• Search engines:- Search engines are using machine learning for pattern detection that help identify spam or duplicate content. Search engines can understand our queries in various ways:-
• Understanding user queries :-Whenever we write our question in a search engine, the most important thing for the search engine becomes to understand what we are trying to ask.
• Spelling suggestion/correction:- User can make spelling errors while typing their queries in search engine. If we write a wrong spelling in a search engine, it shows us the correct spelling of the same word.
• Photo tagging applications:- Machine learning is certainly the talk of the town and being applied by many giant business companies like facebook, apple, google,etc. Facebook is currently having one of the best machine learning algorithm in place. Suppose if we post a photo with our friends, Facebook will automatically suggest us tag if we want to tag our friend in the same.
• Spam detector:- Spam email is usually consist of words like sale, discount, prize ,etc. Let call them SPAM- WORDS and let the other words be NON- SPAM words. A Machine learning algorithm will take in a set of labelled messages as the input and will sort out the spam emails away from inbox.

EXAMPLES OF MACHINE LEARNING :-

1. Database mining for growth of automation: - Typical application include web click data for better UX (User Xperienced) , medical records for better automation in healthcare. Technologies in the healthcare industry have led to many medical achievements from AI based software for the management of medical records to diagnosing and recognising conditions.
2. Application that cannot be programmed:- There are some tasks that cannot be programmed us the computers we use are not modelled that way. Example: autonomous driving. Autonomous driving will change the way we travel. Reduced traffic congestion,lower travel cost, will make on daily commute quicker, less stressful and more affordable.
3. Understanding Human learning:- This is the closest we have understood and mimicked the human brain. It is the new revolution, The real AI. Humans have the ability to learn , however, machine learning has become a resource which can even replace human learning.
4. Arthur Samuel:- In 1959, Arthur Samuel defined Machine learning as a " field of study that gives computer the ability to learn without being explicitly programmed." Samuel was a true pioneer program at first could be easily won , but overtime it learnt all the broad position that would eventually lead him to victory or loss and thus became a better chess player than Samuel itself.
5. Tom Mitchell:- In 1997, Tom Mitchell gave a well posed definition that has proven more useful to engineering types:- " A computer program is said to learn from experience E with respect to some task T and some performance P, if it's performance on T , as measured by P, improves with experience E." So if we want our program to predict , we can run it through a Machine learning algorithm.

Machine learning is a sub area of Artificial Intelligence, thereby the term refers to the ability of IT systems to independently find solutions to perform by recognising pattern in database.

Written by: Ashish Sharma, Babu Banarasi Das National Institute of Technology and Management