Machine Learning Explained – Part 1.1

You can see why I started this series here.

What is machine learning ?

  • My definition:
    • When you tell a machine to learn from experience rather then, explicitly giving it a bunch of instructions.
  • Course Definitions:
    • “the field of study that gives computers the ability to learn without being explicitly programmed.” – Arthur Samuel
    • “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” – Tom Mitchell

Supervised Learning:

You can think of this like teaching a small child how to do something you already know. Such as counting objects, or throwing a ball. Another way of thinking about it is that you give the machine the data, knowing that there is some relationship there. Then having the machine find it by its self.

This type of learning comes in two different flavours:

Regression

Given a bunch of data and asked to predict what will happen next. An example will could be: “given all the historical data about housing prices, what will be the price of a house in 2020 ?” We are mapping input data to a continuous function to.

Classification

Take the input data, and give me discrete outputs (classifications) . For example if you were to take data on students and predict which students would become engineers. Here we still know what factors really influence the result, which still makes it supervised learning.

Unsupervised Learning:

We have mountains of data that we think is random and has no structure. We have no idea what the relationships are between the variables. So we let our machine loose on the data to discover the relationships between the different variables. And it starts to cluster the data into different piles.

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