Fishing in the Ocean of Artificial Intelligence 🎣
By: Asish Binu Mathew
Tags: Machine Learning, Introduction, Algorithms, Supervised Learning, Unsupervised Learning

Content

What is ML?

So how does all this work behind the scenes?

The three musketeers in AI 😁

Terminologies JUST IN CASE 👀

What is ML?

Teaching computers to learn by itself without specifically programming them.

We use ML for either prediction, a program telling whether a customer will buy a certain product or not, or inference, a company identifying certain trends in consumer purchases.

So how does all this work behind the scenes?

ML requires data, the more the better. Given below is a portion of the iris dataset. Iris is a flower with three sub species. Each row of the table represents an iris flower, including its species and dimensions of its botanical parts, sepal and petal, in centimeters.

The inputs are sepal_length,sepal_width,petal_length,petal_width.

The output or prediction is the species.

IRIS Dataset

We start by collecting data The collected data might not be in a format required by the computer, so we clean the data or convert it to the right format We choose an appropriate algorithm, we call these algorithms as models. Consider a model to be black box for now, we will learn more about it in the future. These models give outputs for every inputs we give. These outputs are predictions. We feed the data to the model/black box/algorithm, this stage is called training. The model looks at this data in different ways and tries to find relationships between the inputs and outputs. Now we have what we call as a trained model, we measure the accuracy or correctness of the model by certain methods.

The three musketeers in AI 😁

How you teach your computer about something depends on the data you have and the situation you are going to use it

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Supervised Learning

When you have data which are labelled, you have an output for every corresponding input, is called supervised learning. You are kind of telling the machine that for some particular set of inputs this will be the output. In the case of iris dataset the features of the flower ( length of sepal and petal width of petal and sepal ) are the inputs and the sub species of the flower is the output or prediction.

Unsupervised Learning

The data has no labels. Sometimes its difficult to put a label to the data. In these situations the algorithm tries to find patterns between the data. Suppose the model is given an image having both dogs and cats which have not seen ever .Thus the model has no idea about the features of dogs and cat so it can’t categorize it as dogs or cats. But it can categorize them according to their similarities, patterns, and differences.

Reinforcement Learning

A reinforcement algorithm learns by trial and error method. Consider training a dog, rewards are given to encourage the dog to perform certain tasks and punishments are given to indicate what not to do. Similarly rewards or points are given to the algorithm for doing a sequence of tasks. These tasks should make the algorithm get closer to its goal. Points are reduced when it makes a mistake.

Terminologies