What is Machine Learning ?
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
And, Arthur Samuel describe it as: “the field of study that gives computers the ability to learn without being explicitly program.” This is an older, informal definition.
Now everything in this world works mutually with machine learning knowingly or unknowingly. The recommendation that shows you while you are surfing the browsers and filtering your mails as spam or non-spam , the machine translation, what not everything can be achieved by Machine learning and Deep learning.
Types of ML
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
1. Supervised Learning:
In supervised learning, we are given a labeled data set and already know what our correct output ought to appear as if, having the concept that there’s a relationship between the input and therefore the output.
Supervised learning problems are categorize 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.
though 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. Here is a description on Math is Fun on Continuous and Discrete Data.
Scenario-1:
So, let me illustrate my very own personal project “BURN OUT RATE”. So we are provide with a set of features or resources to determine the burnout rate, where burnout is characterize by emotional exhaustion, cynicism, and ineffectiveness in the workplace, and by chronic negative responses to stressful workplace conditions. While not considered a mental illness, burnout can be considered a mental health issue. so this is a regression problem.
So, we can even turnout this problem into a classification problem like whether a person has gone through the burnout rate or not. you can go through my codes regarding this burn out rate issue and the reference link for that burn out rate is as following: https://github.com/Naveen-Reddy55/Burn-out-Rate
Scenario-2:
(a) Regression – Given a picture of Male/Female, We have to predict his/her age on the basis of given picture.
(b) Classification – Given a picture of Male/Female, We have to predict Whether He/She is of High school, College, Graduate age. Another Example for Classification – Banks have to decide whether or not to give a loan to someone on the basis of his credit history.
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. Like you will not have any labelled dataset , where we need to figure it out using unsupervised machine learning algorithms.
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, i.e., there is no teacher to correct you.
Example:
Clustering: Take a collection of 1000 essays written on the US Economy, and find a way to automatically group. these essays into a small number that are somehow similar or related by different variables.
such as word frequency, sentence length, page count, and so on.
Non-clustering: The “Cocktail Party Algorithm”, which can find structure in messy data (such as the identification of individual voices and music from a mesh of sounds at a cocktail party (https://en.wikipedia.org/wiki/Cocktail_party_effect) ). Here is an answer on Quora to enhance your understanding. : https://www.quora.com/What-is-the-difference-between-supervised-and-unsupervised-learning-algorithms ?
3. Reinforcement learning:
Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward.
however, Reinforcement learning is one of three basic mL paradigms, alongside the other two.
Summary and farewell:
So in this article, we have seen, what exactly is mL, types of mL, and a detailed study of types.
So we can bring a conclusion that in upcoming years. even now our life associates with machine learning make an impact in upcoming years more effectively.
written by: Naveen Reddy
reviewed by: Krishna Heroor
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