3 Types of Machine Learning Techniques Everyone need to know

Adithya Thatipalli
3 min readMay 24, 2023

Machine Learning (ML) is an exciting field during any hype cycle and its a part of AI Umbrella that allows computers to learn and make predictions without explicit instructions. Key to this technology are different learning techniques that help machines find patterns and insights in data.

Machine learning is a vast domain and its not simple to confined itself to single article or single umbrella. With the advancement of data and technology new methods are being used and developed to create new effective models

In this article, we’ll explore three essential types of machine learning techniques in simple terms, providing easy-to-understand examples for each.

  1. Supervised Learning:

Supervised learning is a common and widely used technique. It involves training a model using labeled data, where the expected output or “label” is already known. The aim is to teach the model the relationship between input data and the corresponding output labels, enabling it to make accurate predictions when faced with new, unseen data.

Example: Email Spam Classification

Suppose you want to create an email spam detector. You collect a dataset of emails, with each one marked as “spam” or “not spam.” The dataset includes different email attributes like the subject, sender, and content. Using supervised learning, you can train a model with this labeled data. The model will learn patterns and be able to predict whether an incoming email is spam or not based on what it has learned.

2. Unsupervised Learning:

Unsupervised learning involves working with unlabeled data. Unlike supervised learning, there are no predefined output labels or correct answers to guide the learning process. Instead, the model’s goal is to uncover hidden patterns or structures within the data.

Example: Customer Segmentation

Imagine you have a large dataset with customer information like age, gender, location, and buying habits. Using unsupervised learning, you can train a model to group similar customers together based on these attributes. The model might identify clusters of customers who share similar preferences or behaviors, helping businesses tailor their marketing strategies for different customer segments.

3. Reinforcement Learning:

Reinforcement learning draws inspiration from how humans and animals learn from rewards and punishments. In this approach, an agent learns to make sequential decisions in an environment to maximize a cumulative reward.

Example: Autonomous Driving

Consider a scenario of autonomous driving. The reinforcement learning agent interacts with a simulated environment, receiving observations like images from the car’s cameras and taking actions like accelerating, braking, or steering. The agent’s objective is to learn a policy that enables safe and efficient driving. It receives positive rewards for following traffic rules and reaching the destination, while negative rewards are given for collisions or traffic violations. Over time, the agent learns to navigate the environment effectively by trying out different actions and maximizing the cumulative reward.

Machine learning encompasses different techniques, each serving a specific purpose. Supervised learning uses labeled data for predictions, unsupervised learning uncovers hidden patterns in unlabeled data, and reinforcement learning enables agents to learn through trial and error. Understanding these fundamental learning techniques helps us appreciate the power and versatility of machine learning algorithms in solving real-world problems across various domains.

Thanks for reading :)

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Adithya Thatipalli

Security Engineer by Day, Cloud and Blockchain Learner during Night