 Good evening, everybody. I would like to share something about machine learning and bird's eye view. Machine learning is a buzzword today. Everybody in the industry talks about machine learning. The objective of this presentation is to know about what motivated to have created this innovative applications in various domains, almost from science, engineering to commerce and literature. I can show two examples. One is the Amazon Automated Warehouse, where the entire process is being done by machine. And also another one, an Amazon Prime Air, where the products are delivered by the road. These two are classic examples of machine learning, where machine learning could be involved in day-to-day life. So it is a new area in the information technology industry. And it is possible to create innovative applications. Although the concept started from the 20th century, based on Alan Terry, who is called as the father of artificial intelligence, he thought that computers can think like human beings. And a lot of movies were also there, like Star Wars, where they predicted that the machines could also behave like human beings. And the Amazon CEO Jeff Bezos told one that we are now solving problems with machine learning and artificial intelligence, which are in the fictions in the earlier decades. The definition for machine learning is ability to think. That is the machines we want to give you a power of thinking. That is the definition by Arthur Samuel. And Tom Mitchell gives an explanation that if we have learned something from the task that we have done and we have made some performance, that experience is called as an intelligent thing. So the machine learning depends upon how we are learning with the help of experience. These are the two examples of machine learning problems. One is email, which is already we know that. A lot of spam emails are coming in our folder. And the machine learning algorithms can classify whether the particular mail is spam or not. Similarly, we can also predict whether the rain is whether we will have rain today or not. These are the two classic examples of machine learning. The machine learning involves a lot of subjects. For example, it involves statistics and machine learning, databases, knowledge discovery, data mining, neuro computing, pattern recognition, et cetera. This is a mixed subject. From all these areas, we are combined and we are doing some kind of algorithms which will make the computers to think. We can model the machine learning as we get the data. And we create a model that is called predictive model. We take some training set of data. And using this data, we create predictions. What will happen? And if it is true, then we apply the existing data to prove that whether we are getting that incident or not. So that is called machine learning models. So here in the email, data is a sample emails. And model is training the email and classifying it spam or not. And action is applied to all the incoming emails to categorize whether it is spam or not. That's better to interrupt the last 30 seconds. OK. There are a lot of tools available for machine learning. For example, C is C++ used for intensive computation. But Python is the most preferred language for its simplicity. It has a lot of packages like NumPy, SkyPy, Skykit Learn, and TensorFlow, et cetera, which are more popular. And we can write a lot of algorithms in machine learning. So go for machine learning and get through all the packages available in Python. Let us create a lot of innovative applications. Thank you very much.