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Showing posts from December, 2015

Types of Machine Learning

Using computers to analyze data and find patterns in large datasets is achieved with Machine Learning. However, there are different ways that data can be observed.
Classification - Looking at elements in a dataset and determining what category the element belongs to, based on preset parameters.      Imagine that I worked in a post office and I had to sort mail into different categories, Packages and letters. The only information that I am given is the weight of the mail. Based on the weight of the incoming mail, a model can be built so that mail can be classified as a package or a letter.
Clustering - Grouping elements in a dataset into categories, based on attributes of the elements. An example of this would be if I had a data set of what students used to read textbooks. The program could first look at the given data set and separate the electronic methods from the non-electronic. Then we would have 2 groups which we could label as: Physical Books and Electronic devices. We would have…

So what is Machine Learning?

It’s mentioned all the time in the news and companies are constantly touting how enhanced their applications are with learning machines. But what does that really mean?
To put it simply, its teaching a machine how to do something. There’s more than one way to do this but most models follow the same method of “Practice makes perfect”.
For example, if I wanted to learn how to draw, I would start with the basics and do simple things. I would practice the motions and gestures that I wanted to get the desired effect. The more I practice, the better (in most cases) I would get. My skill would improve over time. However I could be learning the wrong way and actually be making my drawing ability worse.
Teaching machines is the same way. A machine is given a data set (ex. a series of images it wants to learn how to draw) and it runs a loop that practices its skill at finding patterns amongst the data set (ex. a flower petal is more curves than straight lines, while a sidewalk is mostly straig…