Introducing Machine Learning.

...provides computers with the ability to learn without being explicitly programmed...

Step on Machine Learning

Machine learning is an attempt to get machines (i.e., computers) to learn how to do stuff in a similar way to how people learn.

This differs from how computers have traditionally been endowed with capabilities. That is, historically, computers have accomplished things by following instructions provided by software engineers who translate a task (like computations) into a program that can perform it. A program is a recipe of sorts – a list of commands that can be performed by a computer to achieve a goal. Machine learning is an alternative to having an engineer program a computer by allowing the computer to program itself.

The motivation for machine learning is simple: there are many tasks that we want computers to perform that have proven very difficult for computers to actually do.

Some of the more surprising examples are tasks that people are able to do with thoughtless ease. For example, while it is not too difficult to program a computer to solve college level calculus problems or search for a term among a billion words in seconds, it is agonizingly hard to program a computer to do tasks like identify an object in a picture, turn hand-written text into typed text, recognize a person’s face, estimate gender and age or have a natural (not even intelligent) conversation, or walk.

Solving Complex Problems

Machine learning techniques generally require a large amount of computing power and data. Therefore, in the early years of computing, many of the useful problems we wished to solve with machine learning required more data and processing power than was available at the time.

Recently we are now at a point where machine learning is being successfully applied to all sorts of useful problems. This growth is being compounded by the aggregation of data inherent in web and mobile-based applications and commerce.

Take Netflix as an example. Because Netflix provides a service to users through a web-based application, the company can analyze the movie preferences of millions of people to help predict which movies a new user might like. This wouldn’t be possible if user data wasn’t aggregated. Every day we are all experiencing more and more of the benefits of machine learning in our lives.