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Types Of Machine Learning Algorithms And Their Applications

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What is Machine Learning?

Machine Learning is a branch of Artificial Intelligence and Computer Science that uses algorithms and data to learn and make predictions. It gives computers the ability to learn and take decisions like humans without being explicitly programmed. In today's world, it is used in almost every field. Some of the most popular applications of Machine Learning include virtual assistants, self-driving cars, social media recommendations, product recommendations and email filtering, etc.

Machine Learning is of different types based on the type of data that it uses for the learning and the type of predictions that are to be made from that data. In this article, we will be learning about the four different types of Machine Learning algorithms and their real-world applications.

The different types of Machine Learning algorithms are as follows:

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

Let’s check the next section to learn more about them and their applications in the real world.

1. Supervised Learning:

It is a type of Machine Learning that uses structured and labelled datasets for training the models and predicting the outcomes accurately. In this type, the user already knows the type of outcome that is needed from the data. It contains the independent variables that are used for the training of the model and the dependent variable is the outcome that is to be predicted from the data. The Machine Learning model learns from the independent variables and based on those the future prediction is done.

Supervised learning is classified into the following two categories:

  • Regression: It is used when the output is a continuous or a real value such as a price of a car or the weight of a person, etc.
  • Classification: Classification is used when the dependent variable is a discrete value such as “spam” or “not spam” and “male” or “female”, etc.

The different supervised algorithms are linear regression, logistic regression, K-Nearest Neighbours, Decision Trees and SVM ( Support Vector Machines), etc.

The real-world applications of Supervised Learning are as follows:

  • Price Prediction
  • Spam Filtering
  • Face Detection
  • Speech Recognition
  • Image Classification
  • Language Translation, etc.

2. Unsupervised Learning:

It is a type of Machine Learning that uses unstructured and unlabelled datasets. It is used to discover hidden patterns in the data without human supervision. In this, the dependent or the target variable is unknown which means that the result from the data is not known at the initial phase. The machine itself discovers the patterns and gives the output accordingly.

Unsupervised learning has three common approaches that are as follows:

  • Clustering: It is a technique that groups or forms clusters of unlabelled data according to the similarities or differences in the properties of the data.
  • Association Rules: It is a rule-based technique that finds the relationships between the different variables of the dataset. It is generally used in market basket analysis.
  • Dimensionality Reduction: It is a technique that is used to reduce the number of dimensions or features in a dataset so that it does not result in overfitting.

The different unsupervised learning algorithms are K-Means Clustering, K-Nearest Neighbours, Hierarchical Clustering, Principle Component Analysis and Anomaly Detection, etc.

The real-world applications of unsupervised learning are as follows:

  • Recommender Systems
  • Product Segmentation
  • News Sections
  • Computer Vision
  • Customer Segmentation, etc.

3. Semi-Supervised Learning:

It is a machine learning technique that falls in between both supervised and unsupervised learning. It uses both labelled and unlabelled data for training the algorithm. It contains a small amount of labelled data and a large amount of unlabelled data. In this, unsupervised learning is used to make the clusters of data then the labelled data is used to label that unlabelled data iteratively. It saves time and money because the unlabelled data can be labelled using the labelled data.

The different methods of semi-supervised learning are as follows:

  • Generative Models
  • Low-Density Separation
  • Laplacian Regularization
  • Heuristic Approaches

The real-world applications of semi-supervised learning are as follows:

  • Speech Detection
  • Determining the 3-D structure of a protein
  • Determining the presence of oil at a certain location
  • Web content classification, etc.

4. Reinforcement Learning:

It is a reward-based machine learning technique. In this algorithm, the reinforcement agent learns from the experience based on which it makes the sequence of decisions. The main goal of the agent is to take action by making decisions such that the reward is maximized in a particular situation. There is no dependent variable or a pre-defined solution to a problem, the machine itself finds the solution by using the hit and trial approach.

The different types of Reinforcement Learning are as follows:

  • Positive Reinforcement Learning
  • Negative Reinforcement Learning

There are various reinforcement learning algorithms such as Q-Learning, Deep Q Network, SARSA and DDPG, etc.

The real-world applications of reinforcement learning are as follows:

  • Image Processing
  • Gaming
  • Robotics
  • Navigation System, etc.


In this article, we have learned about the four types of Machine Learning algorithms that are supervised, unsupervised, semi-supervised and reinforcement learning with their real-world applications. I would also be explaining each of them in detail separately in the upcoming articles.

For more such upcoming content related to Python, Machine Learning, Data Science and Front-end development follow Chirag Rathi!

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