Short overview of Machine Learning techniques

In this article we cover four major types of machine learning techniques, we explain their applications and help you differentiate them

Machine Learning can be performed using multiple approaches. Even if you are not planning to become a machine learning developer, knowing these techniques can enable you how your current or future AI project will be built.

The four basic techniques of ML are: supervised , unsupervised, semi-supervised ,and reinforcement learning. There are other Machine Learning techniques, so the below list is not exhaustive.

Supervised machine learning

This technique uses historical data (i.e. data that was generated beforehand), to understand patterns and then infer values from new observations. Supervised learning parameters are crucial in mapping the input-output pair. It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process.

Some use cases that are based on supervised machine learning are predicting customer churn, personalized recommendations, fraud detection, etc.

Unsupervised machine learning

Unsupervised learning is a machine learning technique, in which there is no need to supervise the model. Instead, the model works on its own to discover information. For some use cases, unsupervised learning can be used to help a supervised model at learning. This type of ML algorithm does not use any classified or labeled data points, it mainly deals with unlabelled data.

Some applications of unsupervised machine learning techniques include: clustering (grouping coherent segments of customers, or grouping logistic pick-up points), anomaly detection (detecting issues in operational or industrial processes), association mining (customer analytics), and latent variable models (factor analysis).

Semi-supervised machine learning

As the name may suggest, this model combines elements of supervised and unsupervised learning. It works by using both labeled and unlabeled data to improve the system's learning abilities.

A semi-supervised machine learning algorithm is often used to label a dataset, and retrain the model with the newly labeled dataset. Some of the well-known examples of semi-supervised learning are speech analysis, protein sequence classification, and web content classification.

Reinforcement machine learning

A reinforcement learning algorithm, or agent, learns by interacting with its environment. In reinforcement learning, there is no answer in the data but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, the algorithm is bound to learn from its experience and the process of win and error can finally lead to long-term rewards. Popularly being used for text mining, robotics, and trade executions, reinforcement learning also has multiple applications in another popular field - healthcare. This machine learning technique can be used for medication dosing optimization, treatment policies optimization, and clinical trials.

If you are curious to find out how your organization can leverage these Machine Learning techniques to optimize the decision-making process, leave us a message, we will answer within one hour to propose a slot for a call to get to know each other.

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