Machine Learning- Applications
Machine learning is one of the most exciting latest technologies in the field of computer science, data science. The name itself signifies: Machine learning is the process of learning that the human being does in the real life from the environment. Either knowingly or unknowingly we are using machine learning dozen of times. Here are the applications of Machine Learning:
Web Search Engine:Search engines like google, Bing ,Yandex etc use most advance machine learning algorithm to rank the pages according to different factors. It also detects the choice of users, their geographical location and many more.
Photo tagging Applications:The most common application is facebook or any other photo tagging application.
Do you know how it happens?
Well the answer is very simple. It all about machine learning algorithm that is running in the backend. Face recognition algorithm that runs behind the application.
Spam Detector:You might have seen spam folder in your Gmail. Big companies like google, yahoo, Hotmail are doing development in their technology in order to remove this spam to the spam folder and make the model more reliable and stronger. This is again achieved by machine learning techniques by a spam classifier running in the back end of mail application.
Today, Machine Learning can be used in different companies to improve business decisions, increase productivity, detect disease, forecast weather, and do many more things. With the exponential growth of technology, we not only need the better tool to understand the present data but also we have to be prepared about the future data. So to achieve this goal we need to build intelligent machines that has ability to predict the future data for the growth of company. This intelligent system has ability to learn things themselves. If the machine learn themselves from the input things then it can does hard things for us.The play of Machine Learning comes in action at this point. Below are some examples of machine learning are:
Database Mining for automation growth:In database mining typical applications of machine learning include Web-click data for better UX, Medical records for better automation in the sector of healthcare, biological data,business and other many more.
Applications that cannot be programmed:There are many tasks that cannot be programmed as we does in computers. For examples Self Driving Car, Recognition tasks from unordered data ,Face Recognition, Handwriting Recognition, Natural language Processing, computer Vision etc.
Understanding Human Learning Process:The todays technology is so close that we can mimicked the psychology of human brain.
It is the start of a new revolution and real AI.
Now, After this short insight lets come to a more formal definition of Machine Learning given by
“Machine Learning is a field of study that commands computers, the ability to learn without explicitly being programmed”.
Samuel coded a Checker playing program which could study over time and at first it could be easily won. But over time, it learnt all the board situation that would eventually lead him to success or loss and thus became a better chess player than Samuel itself. This was one of the most early efforts of defining Machine Learning and is somewhat less formal.
“A computer program is said to learn from E as Experience w.r.t some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”
This is a more proper and mathematical explanation. Lets clarify the previous Chess program
- E is number of the games.
- T is playing the chess against computer.
- P is win/loss by the computer.
In the Next tutorial we shall discuss about the different types of Machine Learning problems .