Introduction to Machine Learning
So let’s move forward with what machine learning is all about!! simply we can say machine learning is all about making predictions based on the past experiences for example :-
Google’s Map: Using the location data from smartphones, Google Maps can inspect the agility of shifting traffic at any time, moreover map can organize user-reported traffic like construction, traffic, and accidents. By accessing relevant data and appropriate fed algorithms, Google Maps can reduce commuting time by indicating the fastest route.
Spam Filters: Some rules-based filters aren’t served actively in an email inbox such as when, for example, a message comes with the words “online consultancy”, “ online pharmacy”, or from “unknown address”.
Email Classification: Gmail categories emails into groups Primary, Promotions, Social, and Update and label the email as important.
Types of Learning :-
1: Supervised learning
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
learning problems are categorized into “regression” and “classification” problems. In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.
Example 1:
Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.
We could turn this example into a classification problem by instead making our output about whether the house “sells for more or less than the asking price.” Here we are classifying the houses based on price into two discrete categories.
Example 2:
(a) Regression — Given a picture of a person, we have to predict their age on the basis of the given picture
(b) Classification — Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
2 : Unsupervised Learning
Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships among the variables in the data.
With unsupervised learning there is no feedback based on the prediction results.
Example:
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.
