Machine Learning: Stanford (coursera)

                            Epod 

              Educate People on Demand

               Summary:Week 1

        Machine learning is divided into two groups.  

    1. Supervised learning  

    2.Unsupervised learning.

    This article introduces the first. The second will follow in the next post.This course on Machine Learning is taught at Stanford,but anyone can attend online,for free, through Coursera. Google it.

   

        Supervised learning is the type of learning that takes place when the training instances are labelled with the correct result, which gives feedback about how learning is progressing. This is akin to having a supervisor who can tell the agent whether or not it was correct.Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created.

      Supervised methods are methods that attempt to discover the relationship between input attributes (sometimes called independent variables) and a target attribute (sometimes referred to as a dependent variable). The relationship discovered is represented in a structure referred to as a model.

      Usually models can be used for predicting the value of the target attribute knowing the values of input attributes. The supervised methods can be implemented in a variety of domains such as marketing, finance and manufacturing.

               Supervised learning splits into two broad categories: 
1. Classification for responses that can have just a few known values, such as 'true' or 'false'. Classification algorithms apply to nominal, not ordinal response values.  2. Regression for responses that are a real number, such as miles per gallon for a particular car.