Do you want to learn more about Machine Learning (ML)? What is Machine Learning? What are the different types of Machine Learning algorithms?
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Machine Learning has been a new hype in the field of computer science for a year along with other trending topics such as the Internet of Things, Artificial Intelligence, Big Data…
But what is Machine Learning for beginners?
Machine Learning is developing a computer program that improves automatically with experience.
This is a general definition according to me so that it is easily understood by freshers.
By the field of usage and kind of data we are using as input, we can modify this definition accordingly.
For example, Genetic programming is the field of Machine Learning where you essentially evolve a program to complete a task while Neural networks modify their parameters automatically in response to prepared stimuli and expected a response.
Machine Learning can be divided into two following categories based on the type of data we are using as input:
There are two main types of machine learning algorithms.
What is a Supervised Machine Learning Algorithm?
It is a task of inferring a function from labeled training data.
What is an Unsupervised Machine Learning Algorithm?
It is the task of inferring from a data set having input data without labeled response.
Both algorithms are further classified into subtypes.
Supervised learning is where you use an algorithm to learn a mapping function from input to output.
Y = f(x) where, x – input variable Y – output variable
The goal is to approximate the mapping function so well that when you have new input data that you can predict the output variable for that data.
Supervised learning problems can be further divided into regression and classification problems.
Classification is when we have discrete-valued output.
A regression is when we have continuous-valued output.
Some example of supervised learning algorithms are:
Unsupervised learning is where you only have input data and there is no corresponding output.
The goal of unsupervised learning is to recognize structure in the data in order to learn more about data.
Unsupervised learning problems can be further divided into clustering and association problems.
A clustering problem is where you want to recognize the inherent grouping in the data.
An association rule learning problem is where you want to discover rules that describe your data.
Some example of unsupervised learning algorithms are:
This is all about different types of machine learning algorithms. We will cover more insight into each topic and algorithm in upcoming articles.
If you have any questions, write in the comment.