Machine learning approaches are being deployed to solve all sorts of problems almost everywhere.

From Google Gmail’s continuous battle against spam to PayPal’s fraud detection practice through deep learning(a subset of machine learning). it’s hard to imagine a scenario where machine learning methods couldn’t be put to good use.

the best way to understand machine learning is to look at some example problems.

Spam Detection: Given email in an inbox, identify those email messages that are spam and those that are not. Having a model of this problem would allow a program to leave non-spam emails in the inbox and move spam emails to a spam folder. We should all be familiar with this example. Google spam filter is an example.

Speech Understanding: Given an utterance from a user, identify the specific request made by the user. A model of this problem would allow a program to understand and make an attempt to fulfill that request. The iPhone with Siri has this capability.

Product Recommendation: Given a purchase history for a customer and a large inventory of products, identify those products in which that customer will be interested and likely to purchase. A model of this decision process would allow a program to make recommendations to a customer and motivate product purchases. Amazon has this capability. Also think of Facebook, GooglePlus and Facebook that recommend users to connect with you after you sign-up.

Stock Trading: Given the current and past price movements for a stock, determine whether the stock should be bought, held or sold. A model of this decision problem could provide decision support to financial analysts.

In the next post, we approach the next phase… stay posted.