ML SERIES (1) : A simple guide to Supervised and Unsupervised Learning
Machine learning is a part of artificial intelligence, that provides system ability to learn and improve without being explicitly programmed. differnt techniques.
Supervised ans Unsupervised Learning are techniques of solving machine learning problem. Lets dive to know more, what are they and how they work.
Training the machine using “labelled data” is known as supervised learning. So, What the hack is this labelled data — Well, when the input value is associated with a output then it is a labelled data.
Supervised learning is categorized into “regression” and “classification” problems. In a regression, we calculate the results within a continuous output, it means that input variables is mapped with continuous function (for ex — a straight line).
While, In a classification problem, data is mapped with some discrete output value. Output categories are pre-defined. For example —
Is there any chances of rain today ? [ Yes / NO]
Which fruit is this? [ Mango / Orange ]
Example 1 :
Here is the labelled data for Iris flower classification, in which we will train algorithm to classify any flower using its sepal and petal length and width.
Now, machine will be able to identify any unknown flower sample.
An approach to machine learning where the software learns from data without being given any correct answers (like we were providing labelled data in supervised learning ),
One approach to solve this problem is Clustering.
Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.
Non-clustering: The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).
ML Series (2) : Support Vector Machine —> https://bit.ly/2Laqkn6