Supervised Learning Algorithms

Supervised Machine learning, is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately.
As input data is fed into the model, the model adjusts its weights until it has been fitted appropriately.
In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly.

One practical example of supervised learning problems is predicting house prices. How is this achieved?
First, we need data about the houses: square footage, number of rooms, features, whether a house has a garden or not, and so on.
We then need to know the prices of these houses, i.e. the corresponding labels.
By leveraging data coming from thousands of houses, their features, and prices, we can now train a supervised machine learning model to predict a new house’s price based on the examples observed by the model.

Image classification is a popular problem in the computer vision field. Here, the goal is to predict what class an image belongs to.
In this set of problems, we are interested in finding the class label of an image. More precisely: is the image of a car or a plane? A cat or a dog?

One particularly interesting problem which requires considering a lot of different parameters is predicting weather conditions in a particular location.
To make correct predictions for the weather, we need to take into account various parameters, including historical temperature data, precipitation, wind, humidity, and so on.
This particularly interesting and challenging problem may require developing complex supervised models that include multiple tasks.
Predicting today’s temperature is a regression problem, where the output labels are continuous variables.
By contrast, predicting whether it is going to snow or not tomorrow is a binary classification problem.

Another great example of supervised learning is text classification problems.
In this set of problems, the goal is to predict the class label of a given piece of text.
One particularly popular topic in text classification is to predict the sentiment of a piece of text, like a tweet or a product review.
This is widely used in the e-commerce industry to help companies to determine negative comments made by customers.

Regression algorithms are used if there is a relationship between the input variable and the output variable.
It is used for the prediction of continuous variables, such as Weather forecasting, Market Trends, etc.
Below are some popular Regression algorithms which come under supervised learning:

Classification algorithms are used when the output variable is categorical, which means there are two classes such as Yes-No, Male-Female, True-false, etc.

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