Machine Learning allows systems to learn without any human intervention and improve their performance. Machine Learning has been helpful to identify patterns in a given data, build self-explanatory models using the available data, and make predictions.

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Machine learning has contributed towards fast-track digital transformation and has been adopted by companies all across the globe. Enterprises extensively use machine learning models to improve the performance of their software products and apps, and this is why it becomes important to understand which type of machine learning technique should you apply to get maximized performance. This article discusses the three main categories of machine learning.


types of machine learning


To learn about machine learning, read our blog on – What is machine learning

Supervised Learning 

Supervised learning is a methodology that involves machine learning using a set of previously labeled data. Here, the supervised learning algorithm is told what we want to learn. A set of examples with previously known results are included in the study data, in which the learning model includes the parameters of the sample to progressively adapt and incorporate the new data and classify them correctly. 

Supervised learning can prove to be very valuable when it comes to eliminating manual classification tasks and making valuable data predictions. Interestingly, supervised learning allows making predictions based on data that has not yet been entered or processed. Its main usage includes image detection, object recognition, predictive analytics, customer sentiment analysis, spam detection, to name a few.

There are two types of models in supervised learning – 

  1. Classification – Classification supervised learning model is a predictive analysis model that aims to estimate the categorical classes of a data set based on a binary or multi-class pattern. It uses algorithms to assign test data into specific categories. 
  • Regression – Regression supervised learning model works towards assigning categories to unlabeled data. This may have several predictive variables of explanatory order and a response variable, the function of the model would be to determine if there is any relationship between these variables.

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Some examples of supervised learning algorithms are –

  • Neural networks
  • Naïve Bayes
  • Linear regression
  • Logistic regression
  • Support vector machines (SVM)
  • Decision trees
  • k-nearest neighbor
  • Random forest

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Unsupervised Learning 

Unsupervised learning is another form of machine learning which uses machine learning algorithms to analyze and cluster unlabeled datasets for analysis. The machine learning algorithms help to find out the hidden patterns in the data. With unsupervised learning, vital information can be obtained without having any reference to the output variables. In this case, unlike supervised learning, here learning is achieved through the analysis of data that does not yet have results. Unsupervised learning is helpful in exploratory data analysis, pattern recognition, image recognition, customer segmentation, among others. Unsupervised learning is majorly based on the data and the outcomes are controlled by it.

Types of Unsupervised learning

  • Clustering – As the name suggests, clustering involves grouping objects into clusters based on their similarities. The most similar groups are clustered in one while the objects with less or no similarities end up in a separate group. The basis of clustering is finding patterns and commonalities.  
  • Association – An association rule helps to find the relationships between variables in the large database. It checks the data dependency of the datasets and maps the most profitable output.

Some of the commonly used unsupervised learning algorithms include –

  • The k-means Clustering
  • DBSCAN Clustering
  • Principal Component Analysis
  • Association Rules
  • Gaussian Mixture Models
  • Singular value decomposition

Reinforcement Learning 

Reinforcement learning is a deep learning methodology that involves taking appropriate action to maximize reward in a given situation. This type of learning is based on improving the response of the model using a feedback process. The algorithm learns by observing and learns by trial and error to produce a solution.

It is not a type of supervised learning, because it is not strictly based on a set of labeled data, but on the monitoring of the response to the actions taken. Nor is it unsupervised learning, since when we model our “learner” we know in advance, what the expected reward is. Reinforcement learning can be used in robotic control, aircraft control, industrial automation, strategy planning, developing business models, data processing, etc.

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Types of Reinforcement Learning 

There are two types of reinforcement learning:

  • Positive Reinforcement – It is the event occurring because of a particular behavior of the data set, it contributes towards increasing the strength and the frequency of the behavior. Positive reinforcement helps to maximize the performance and extend the change for a longer time. 
  • Negative Reinforcement – Negative Reinforcement is also a process of strengthening a behavior but the underlying condition is negative and that needs to be stopped or avoided.

Some of the commonly used reinforcement learning algorithms include –

  • Q-learning 
  • SARSA (State-Action-Reward-State-Action)
  • Deep Q-Networks(DQNs) 
  • Deep Deterministic Policy Gradient(DDPG)

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Machine learning is a very powerful tool that turns data into information and facilitates decision-making. The key is to concisely define the learning objectives, depending on the characteristics of the data set we have. Select the most suitable type of learning that best fits to provide a solution that responds to the needs.


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