Machine Learning is Fun!

 Machine learning is based on the premise that generic algorithms can tell you something interesting about a piece of data without requiring you to build specialized code tailored to the situation.

You give data to the generic algorithm instead of writing code, and it creates its logic depending on the data.

A classification algorithm is an example of an algorithm. It can categorize data into many groupings.

Without modifying a line of code, the same classification system that recognizes handwritten numerals could be used to classify emails into spam and not spam. It’s the same algorithm, but it’s fed different training data; therefore, the categorization logic is different.

Machine Learning Algorithms are divided into two categories.

Machine learning algorithms may be divided into supervised learning and unsupervised learning. The distinction is subtle but critical.

Assume you work as a real estate agent. Because your company is expanding, you recruit a group of fresh trainee agents to assist you. But there’s a catch: you can look at a property and have a pretty decent notion of what it’s worth, but your trainees don’t have your expertise, so they have no clue how to price their homes.

You decide to build a little software that can assess the worth of a property in your region based on its size neighborhood what similar houses have sold for to aid your trainees (and perhaps free yourself up for a trip).

Is machine learning magic?

When you see how readily machine learning techniques can be applied to seemingly tricky issues (such as handwriting recognition), you get the impression that you could use machine learning to tackle any problem and receive an answer as long as you have enough data. Enter the data, and the computer will magically calculate the equation that best matches the data!

It’s vital to note, though, that machine learning only works if the problem can be solved with the data available.

Building a model that forecasts housing values based on the sort of potted plants in each house, for example, would never work.

There is no correlation between the sale price and the number of potted plants in residence. As a result, no matter how hard it tries, the computer will never establish a link between the two.

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