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Machine Learning: Types of Machine Learning

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In this article discover the three main types of machine learning – supervised learning, unsupervised learning, and reinforcement learning. Understand the strengths and weaknesses of each approach with the examples. Gain a comprehensive understanding of the different types of machine learning and how they are transforming the way we approach artificial intelligence.

Also, let’s explore Introduction to Machine Learning

Table of Contents

Introduction – Machine Learning?

Machine Learning teaches the machine to perform real-life human activities by providing vast data. In this Learning, the machine learns from the data and past analyzed experiences. 

In ML language, Machine Learning is programming a machine or computer without being explicitly programmed. Machines can adapt the new technology to learn instructions and draw patterns from the data.

In simple words, machines are taught by provided data to learn and understand the pattern and draw inferences (graphs, simplified data) to return the desired output.

The efficiency of ML algorithms improves adaptively by increasing the quantity of data available during the “learning” process. For instance, deep Learning is a subset of machine learning which helps computers mimic human behavior, such as learning from cases. It provides superior performance over conventional ML algorithms.

In the present, thanks to the growth of large data sets, IoT technology, as well as the widespread use of computing machine learning, has been indispensable for solving problems in many areas, including

  • Finance using computation (credit scoring algorithmic trading)
  • Computer vision (facial recognition, motion tracking, object detection)
  • Computer-based biology (DNA sequencing and detection of brain tumors, drug discovery)
  • Aerospace, automotive manufacturing, and aerospace (predictive maintenance)
  • Natural processing of languages (voice recognition)

Machine learning and its applications

Machine learning is already being used daily, and you may not know how it affects your daily life. Here are some ways it’s utilized that you must be aware of:

Features of social media: Social media platforms incorporate machine-learning algorithms to deliver more customized experiences to users. Facebook tracks your actions, including your comments, likes and comments, and time spent on various types of content. The algorithm can learn from your activities and creates pages and friend suggestions specifically tailored to your needs.

Virtual assistants: Apple’s Siri, Amazon’s Alexa, and Google Now are all popular choices if you’re searching for a virtual personal assistant. These voice-activated devices can perform everything from searching for flights to reviewing your schedule to creating alarms. Machine learning is an essential element of these intelligent audio devices and speakers. They collect data and refine the information each when you use them. The machine will then utilize this information to present results that closely match your preferences.

Product suggestions: Popular among e-commerce websites and product recommendations are a standard machine-learning program. These sites can monitor your behavior according to your past searches or purchases and the history of your shopping cart to provide suggestions and suggestions about items you find interesting.

Recognition of images: The technology’s complexity appears in many areas. In the course of your daily life, you’ve likely encountered this when you upload a picture on your social media platform. The platform will recognize the individual when you tag someone on an image. It could also be transformative for identifying criminals or threats, unlocking mobile phones and other devices, and locating missing individuals.

Types of ML

Supervised Learning

In Supervised Learning, the model is provided with the labelled data that data contains input label and corresponding output label. The task of the Supervised Learning is to understand relationship or pattern between the respective data and map the input to its corresponding output data.

Example: The machine is initially trained to predict the desired outcomes, including cats and dogs, based on some features such as eye size, hair color, nose structure, mouth dimension, etc.; now, an input picture of a cat and dog is provided, and the machine is expected to identify; based on some features such as eye size, hair color, nose structure, mouth dimension, etc., to make the final prediction. So, this is the process of object identification in Supervised Learning. 

The main goal of the supervised learning method is to understand the pattern of input and output data to map variables correctly so that model can predict the accurate result.

The supervised machine learning technique is further divided broadly into two categories:

Classification: These are algorithms to solve classification issues in which the output variable is categorical. For example, whether it is true or false, female or male or female, etc. Real-world applications of this type can be seen in spam detection and filtering emails.

A few well-known classification algorithms are some of the most famous classification algorithms are the Random Forest Algorithm, Decision Tree Algorithm, Logistic Regression Algorithm as well as Support Vector Machine Algorithm.

Regression: These algorithms solve problems with regression when the input and output variables share a linear relationship. They can be used for their ability to forecast continuous output parameters. Examples include weather predictions and market trend analysis, and so on.

The most popular regression algorithms use the Simple Linear Regression Algorithm, Multivariate Regression Algorithm, Decision Tree Algorithm, and Lasso Regression.

Unsupervised Learning 

Unsupervised Learning – Unsupervised Learning is the second type in ML, in which the machine/model is trained with unlabeled data. Unlabeled data includes input variables and corresponding output variables but is not mapped. The machine has to understand the data and draw the pattern based on similarities and differences to simplify the data and predict the desired outcomes. Hence, the machine is ready to predict the output using the test dataset in subsequent phases.

Example: The machine is provided with an input of lots of fruit and vegetable images, which need to be known to the machine/model. When an input dataset is provided to the machine, it has the task of identifying the pattern of objects, such as color, shape, or differences seen in the input images, and categorizing them. Upon categorization, the machine predicts the output as it gets tested with a test dataset.

Unsupervised machine learning is further classified into two types:

Clustering: The term “clustering” is grouping objects into groups based on specific criteria like the similarities or differences between objects and, for instance, dividing customers according to the products they buy.

The most well-known clustering algorithms include The K-Means Clustering Algorithm, Mean-Shift Algorithm, and DBSCAN Algorithm. Principal Component Analysis and Independent Component Analysis.

Association Learning: This identifies the typical relationships between variables from large data sets. It determines the dependence of various data items and then maps the variables that are associated with them. The most common applications are the analysis of market data.

The most popular algorithms that adhere to the guidelines of associations include Apriori Algorithm, Eclat Algorithm, and FP-Growth Algorithm.

Semi-supervised Learning 

Semi-supervised Learning – Semi-supervised Learning is the combination of datasets that includes both labeled and unlabeled data. Therefore, it contains characteristics of both supervised and unsupervised machine learning.

Example: Behind the scene, an actor has been helped by the movie set’s workers about how to act in various situations such as comedy, thriller, emotions, etc., which is termed supervised Learning. But in unsupervised Learning, on the set, while recording, the actor performs various roles automatically without anybody’s help. Meanwhile, an actor revising the concept after learning under the direction of a movie set’s workers on the set is a semi-supervised form of Learning. 

Reinforcement Learning

It is a feedback-based method. The AI component can automatically take stock of its surroundings through the hit-and-trial method and learn from experience, increasing its efficiency. The AI component is rewarded for every excellent move and penalized for every wrong move. The reinforcement-learning component is designed to maximize rewards for good behavior.

Contrary to supervised Learning, reinforcement learning does not have labeled information, and agents learn through experiences only. Consider video games. In this case, the game defines the setting, and every action the agent uses to reinforce determines its condition. The agent can be rewarded with punishments and rewards, which affects the game’s score overall. The goal of the agent is to obtain the highest score.

Reinforcement learning can be applied to fields like information theory, game theory, and multi-agent systems. Reinforcement learning is further subdivided into two kinds of techniques or algorithms:

Positive reinforcement: Positive reinforcement provides a reaffirming stimulus after the agent’s action. This increases the likelihood that the behavior could occur again later, e.g., adding the reward following a specific behavior.

Negative reinforcement:  Negative reinforcement is the practice of strengthening a particular habit that helps avoid negative consequences.


In conclusion, machine learning is a powerful tool that enables computers to learn from data and improve performance over time without being explicitly programmed. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model using labeled data, while unsupervised learning involves finding patterns and structure in unlabeled data. Reinforcement learning involves training a model to make decisions based on rewards or punishments received for specific actions.

Each type of machine learning has its own strengths and weaknesses, and choosing the right approach depends on the specific problem and data available. Machine learning is increasingly being used in a wide range of applications, including image and speech recognition, natural language processing, and autonomous vehicles. As the field of machine learning continues to evolve, we can expect to see even more exciting applications that improve our lives and businesses.

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