4 Types of Machine Learning (Supervised, Unsupervised, Semi-supervised & Reinforcement)

Machine learning is a subfield of Artificial Intelligence. The concept of machine learning originally started in 1959 by an American Arthur Samuel. He was an expert in the field of computer gaming and intelligent machines.

In this post, we are going to discuss the types of machine learning. There are four major types of machine learning. Let’s start with the introduction.

Introduction

We are living in a global world. Today, vast progress has been made in every walk of life.

So far, various tools and techniques are being used to increase the comforts of humans. Nowadays, Numbers of machines have been working to boost up the speed of human work and tasks.

There are some machines that are artificial intelligent in their behavior. In traditional computer programming, the computer or machines get input and processes it and then make an output but contrary to this machine learning the machine has an artificial intelligence system with past experiences and algorithms to develop proper solutions.

Definition

Machine learning is a subset of artificial intelligence. An artificial intelligence system is programmatically organized with algorithms and this system develops the optimum and best solutions.

It focuses mainly on designing the systems, allowing them to learn and make a prediction on some past experiences. It is based on the idea we should give the machine access to the data to learn themselves.

Types of machine learning

These are the four types of machine learning.

  1. Supervised Machine Learning
  2. Unsupervised Machine Learning
  3. Semi-supervised Machine Learning
  4. Reinforcement Machine Learning

1 – Supervised Machine Learning:

In Supervised machine learning, the machine mainly focuses on regression and classification types of problems.

We know the correct output and relationship with input and output in this phase. It deals with labeled datasets and algorithms.  

In supervised learning, the machine gets the last calculated data on the machine, also be called “target data”. It includes the data and the result.

The models are stored in the machines to make the prediction. There are two major processes 

  1. Classification
  2. Regression

Classification is the process in which the input data is labeled on the basis of past data experiences. Machines are also trained with algorithms about the data format.

The algorithms specify the format to recognize by the machine. It identifies a unique class whether it has discrete or boolean value. The example of classification is weather forecasting, and specify tomorrow will be hot or cold.

Regression is the process to identify the labeled data and calculate the results on the basis of prediction. The machine has the ability to learn the data and display real-valued results. These results are based on independent values

Example of Regression: A human picture is given to a common man to identify the gender of the person in the picture. Another example, prediction of temperature on tomorrow on the basis of past data.

2 – Unsupervised Machine Learning

The unsupervised machine learning is totally opposite to supervised machine learning.

In this type of learning, the results are unknown and to be defined. It uses unlabeled data for machine learning. We have no idea which types of results are expected.

In this model, the machine observes the algorithms and finds the structure of data. It has less computational complexity and uses real-time analysis of data through this model.

The results are very reliable when compared to supervised learning. For example, we present images of fruits to this model; this model makes clusters and separates them on the basis of a given pattern and relationships.

There are two types

  1. Clustering
  2. Dimensionality Reduction

Clustering

In clustering, data is found in segments and meaningful groups. It is based in small groups. These groups have their own patterns through which data is arranged and segmented.

Dimensionality Reduction

The unnecessary data is removed in this phase. In unsupervised-machine, algorithms remove unnecessary data to summaries the distribution of data in groups.

3 – Semi-Supervised Machine Learning

Semi-supervised machine learning is also known as hybrid learning and it lies between supervised and unsupervised learning. This model has the combination of labeled and unlabeled data. The data has fewer shares of labeled data and more shares of unlabeled data in this learning.

The labeled-data is very cheap in contrary to the unlabeled data. The procedure is that the algorithm firstly uses unsupervised learning algorithms to cluster the labeled data and then uses the supervised learning algorithm.

4 – Reinforcement Machine Learning

There are no training data sets. The machine has a special software. It works as an agent with the environment to get feedback.

The environment means there are no training data sets. The work of an agent is to achieve the target and get the required feedback.

An example of a reinforcement learning problem is playing game. In which an agent has a set of goals to get high score and feedback in terms of punishment and rewards while playing.

Read also: Introduction to Data Science (Beginner’s Guide)

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