A Perfect Intro To Supervised Learning
By: Nikhilesh Garnepudy
Tags: Artificial Intelligence, Introduction To Ml, Supervised Learning, Machine Learning, Introduction To Ai

Photo by Kevin Ku

We’re taught to ride the bicycle, a few lanes around the house. We’re taught to solve a few examples of the math. The next similar numerical you encounter is something you learn training yourself over the previous ones. Hello?

I believe nothing is complex. I’ve often been intimidated by these buzzwords. Supervised Learning, data pre-processing, blah blah, what not? Through this article, I hope to establish a clear picture of one such buzzword — Supervised Learning. Trust me on this. This article shall be short, apt, and hit you with just the right info, all needed to give you the perfect headstart. Let’s move?

A Few Basics

Machine Learning

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In the simplest terms, Machine Learning is the study of algorithms that teach computers to learn from experience. Just like humans learn to ride a bicycle better or read faster through practice, a machine trains itself on previous data that we input. We call that data commonly training data. The data on which we apply it is called as testing data.

Artificial Intelligence

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The science that aims at mimicking the human brain. And here, machine learning is the subset of artificial intelligence that helps train the model better with task experience.

One thing to put in mind is that not all AI is Machine Learning. Here’s an easier way to put it down.

AI -> ML-> DL

So to be on the same page. Deep Learning is a subset of Machine Learning. Machine Learning is a subset of Artificial Intelligence. But this does not imply that everything under artificial intelligence is Machine Learning.

Machine learning can broadly be divided in to three categories.

Supervised Learning Unsupervised Learning Reinforcement Learning

What is Supervised Learning?

Everything under Machine Learning sums up to data and datasets. Datasets are huge tables of information with labeled columns and organized values. Under supervised learning, the data is trained based on the labeled values in the datasets.

Each table contains a humungous number of input values. These input values are known as training data. There is one column called the target column. In Supervised Learning, the input is mapped to the target column based on the examples of input-output pairs.

The main goal of Supervised Learning is to see to it that the data examples are trained for logic to help predict future values with the nearest approximation possible.

The logic that machine develops is through an algorithm which the human writes. These algorithms form the basis and the final step of any machine learning project. Algorithms under supervised Learning are divided into two kinds.

Regression Classification

Regression

Regression algorithms refer to those algorithms where the output that has to predict has several possibilities. An example could be to predict the size of a shirt. Prediction of weather, favorite movie, possible box office collection, and a few others are examples where regression algorithms are used.

Common regression algorithms are linear regression and logistic regression. A detailed explanation of the algorithms would be explained in further articles.

Classification

Classification algorithms refer to those algorithms where the output is binary or is within a definite set of values. The most common example would be to predict whether it would rain tomorrow or not. The possibility of flunking a test or prediction of cancer are a few other examples of classification.

Common classification algorithms include Random forest Classifiers, support vector machines, etc.

This article was to give you guys a brief of one of the most common machine learning categories. A detailed explanation would be given in the upcoming articles.

Nikhilesh Garnepudy