Machine learning is one of the most exciting recent technologies.
So, What is Machine Learning?
Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed"
Machine Learning examples in our life
You probably use it dozens of times a day without event knowing it. Each time you do a web search on Bing or Google, that work so well because their machine learning software has figured out how to rank what pages.
When Facebook photo application recognizes your friends in your pictures, that's also machine learning.
Each time you read your email and a spam filter saves you from having to wade through tons of spam, again, that's because your email client software has learned to distinguish spam from non-spam email.
So, that's machine learning. There's a science of getting computers to learn without being explicitly programmed.
What's you learn here?
We're a long way away from the goal, but many AI scientists think the best way to make progress on this is through learning algorithms called Neural Networks, which mimic how the human brain works. Here you'll learn about that, too.
In this, you'll learn about the state of the art and also gain practice implementing and deploying these algorithms yourself. But it's turn out just knowing the algorithms and knowing the math isn't that much good if you don't also know how to actually get this stuff to work on problem that you care about.
So, we've also spent lot of time developing exercise for you to implement each of these algorithms and see how the work for yourself.