Types of Machine Learning Algorithms with Use Cases Examples

All the innovative perks that you enjoy today – from intelligent AI assistants and Recommendation Engines to the sophisticated IoT devices are the fruits of Data Science, or more specifically, Machine Learning.

The applications of Machine Learning have permeated into almost every aspect of our daily lives, without us even realizing this. Today ML algorithms have become an integral part of various industries, including business, finance, and healthcare. While you may have heard about the term “ML algorithms” more times than you can count, do you know what they are?

In essence, Machine Learning algorithms are advanced self-learning programs – they can not only learn from data but can also improve from experience. Here “learning” denotes that with time, these algorithms keep changing the ways they process data, without being explicitly programmed for it.

Learning may include understanding a specific function that maps the input to the output, or uncovering and understanding the hidden patterns of raw data. Another way ML algorithms learn is through ‘instance-based learning’ or memory-based learning, but more on that some other time.

Today, our focus will be on understanding the different kinds of Machine Learning algorithms and their specific purpose.

  1. Supervised Learning

As the name suggests, in the supervised learning approach, algorithms are trained explicitly through direct human supervision. So, the developer selects the kind of information output to feed into an algorithm and also determines the kind of results desired. The process starts somewhat like this – the algorithm receives both the input and output data. The algorithm then begins to create rules mapping the input to the output. This training process continues until the highest level of performance is reached. So, in the end, the developer can choose from the model that best predicts the desired output. The aim here is to train an algorithm to assign or predict output objects with which it has not interacted during the training process.

The primary goal here is to scale the scope of data and to make predictions about future outcomes by processing and analyzing the labeled sample data.

The most common use cases of supervised learning are predicting future trends in price, sales, and stock trading. Examples of supervised algorithms include Linear Regression, Logistical Regression, Neural Networks, Decision Trees, Random Forest, Support Vector Machines (SVM), and Naive Bayes.

There are two kinds of supervised learning techniques:

Regression – This technique first identifies the patterns in the sample data and then calculates or reproduces the predictions of continuous outcomes. To do that, it has to understand the numbers, their values, their correlations or groupings, and so on. Regression can be used for pride prediction of products and stocks.

Classification – In this technique, the input data is labeled in accordance with the historical data samples and is then manually trained to identify particular types of objects. Once it learns to recognize desired objects, it then learns to categorize them appropriately. To do this, it has to know how to differentiate between the acquired information and recognize optical characters/images/binary inputs. Classification is used to make weather forecasts, identify objects in a picture, determine if a mail is spam or not, etc.


  1. Unsupervised Learning

Unlike supervised learning approach that uses labeled data to make output predictions, unsupervised learning feeds and trains algorithms exclusively on unlabeled data. The unsupervised learning approach is used to explore the internal structure of data and extract valuable insights from it. By detecting the hidden patterns in unlabeled data, this technique aims to uncover such insights that can lead to better outputs. It may be used as a preliminary step for supervised learning.

Unsupervised learning is used by businesses to extract meaningful insights from raw data to improve operational efficiency and other business metrics. It is commonly used in the fields of Digital Marketing and Advertising. Some of the most popular unsupervised algorithms are K-means Clustering, Association Rule, t-SNE (t-Distributed Stochastic Neighbor Embedding), and PCA (Principal Component Analysis).

There are two unsupervised learning techniques:

Clustering – Clustering is an exploration technique used to categorize data into meaningful groups or “clusters” without any prior information about the cluster credentials (so, it is solely based on their internal patterns). The cluster credentials are determined by similarities of individual data objects and their differences from the rest of the objects. Clustering is used to group tweets featuring similar content, segregate the different types of news segments, etc.

Dimensionality Reduction – Dimensionality Reduction is used to find a better and possibly simpler representation of the input data. Through this method, the input data is cleansed of the redundant information (or at least minimize the unnecessary information) while retaining all the essential bits. This way, it allows for data compression, thereby reducing the storage space requirements of the data. One most common use case of Dimensionality Reduction is segregation and identification of mail as spam or important mail.

  1. Semi-supervised Learning

Semi-supervised learning borders between supervised and unsupervised learning. It juxtaposes the best of both worlds to create a unique set of algorithms. In semi-supervised learning, a limited set of labeled sample data is used to train the algorithms to produce the desired results. Since it uses only a limited set of labeled data, it creates a partially trained model that assigns labels to the unlabeled data set. So, the ultimate result is a unique algorithm – an amalgamation of labeled data sets and pseudo-labeled data sets. The algorithm is a blend of both the descriptive and predictive attributes of supervised and unsupervised learning.

Semi-supervised learning algorithms are widely used in Legal and Healthcare industries, image and speech analysis, and web content classification, to name a few. Semi-supervised learning has become increasingly popular in recent years owing to the rapidly growing quantity of unlabeled and unstructured data and the wide variety of industry-specific problems.

  1. Reinforcement Learning

Reinforcement learning seeks to develop self-sustained and self-learning algorithms that can improve themselves through a continuous cycle of trials and errors based on the combination and interactions between the labeled data and incoming data. Reinforcement learning uses the exploration and exploitation method in which an action occurs; the consequences of the action are observed and based on those consequences, the next action follows – all the while trying to better the outcome.

During the training process, once the algorithm can perform a specific/desired task, reward signals are triggered. These reward signals act like navigation tools for the reinforcement algorithms, denoting the accomplishment of particular outcomes and determining the next course of action. Naturally, there are two reward signals:

Positive – It triggers when a specific sequence of action is to be continued.

Negative – This signal penalizes for performing certain activities and demands the correction of the algorithm before moving forward.


Reinforcement learning is best suited for situations in which only limited or inconsistent information is available. It is most commonly used in video games, modern NPCs, self-driving cars, and even in Ad Tech operations. Examples of reinforcement learning algorithms are Q-Learning, Deep Adversarial Networks, Monte-Carlo Tree Search (MCTS), Temporal Difference (TD), and Asynchronous Actor-Critic Agents (A3C).

So, what do we then infer from all this?

Machine Learning algorithms are used to reveal and identify the patterns hidden within massive data sets. These insights are then used to positively influence business decisions and find solutions to a wide range of real-world issues. Thanks to the advanced in Data Science and Machine Learning, we now have ML algorithms tailor-made to address specific issues and problems. ML algorithms have transformed healthcare applications, and processes and also the way businesses are conducted today.

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