A Guide to Machine Learning Algorithms

Artificial intelligence’s dynamic arm, machine learning, is revolutionizing industries because of its capacity to learn from data and predict or decide without being programmed explicitly. This blog is about three basic machine-learning algorithms.

Supervised Learning: Learn with a Teacher

Like a child who learns to differentiate animals under the guidance of his/her parents, supervised learning algorithms are trained using labeled data where both input and desired output are available. The model is thus able to associate inputs with appropriate outputs.

Common algorithms in supervised learning

Linear regression is used to predict numbers. House price prediction and product price prediction are two major examples of linear regression.

Logistic regression is used to predict categories like spam or not spam.

Decision trees are used to build tree-like models for decision-making, such as loan approval.

Spam filtering, image recognition, and fraud detection are real-world examples of this algorithm's application.

Unsupervised Learning: Learn on Your Own

Unlike supervised learning, unsupervised algorithms do not have predetermined labels in their quest for new patterns and structures within data. ** Common algorithms in unsupervised learning**

Clustering algorithms are used to put similar points together. Customer segmentation is a perfect example of this algorithm used in unsupervised learning.

Dimensionality reduction is used to simplify data by removing some features like image compression.

Association Rule Learning is an unsupervised learning algorithm used to determine associations between items. Market basket analysis is a perfect example of this Machine learning algorithm.

Following are some real-life examples of unsupervised learning. Put customers into different groups based on their buying behavior and paying behavior in shopping malls and e-commerce companies. Build a recommendation system that suggests a particular email is spam or not.

Reinforcement Learning: Learn Through Rewards

This type of learning is based on the way children learn how to ride a bicycle through trial and error. An agent learns how to make decisions by interacting with the environment in reinforcement learning. It then receives rewards or punishments based on what it does.

Common algorithms in reinforcement learning

Q-learning is used to learn the best action in a given state.

Deep Q-Networks is used to merge deep learning and Q-learning.

Self-driving cars, and games like alphaGo are perfect examples of reinforcement learning.

Which algorithm should I use?

The type of algorithm that you will choose depends on the kind of problem that you need to solve and the nature of the data that you are using.

When there is a clear relationship between inputs and outputs, supervised learning is most appropriate.

Unsupervised learning can be used to discover hidden patterns in data and for exploratory purposes.

Reinforcement learning is applicable where one can learn through experimentation.

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