Machine Learning (ML), a subset of Artificial Intelligence (AI), is the scientific study of algorithms and statistical methods that enable machines to perform tasks without being explicitly programmed for them. In other words, ML is a type of programming technique where a machine is not limited to performing only a specific task. Instead, it uses algorithmic and mathematical approaches to learn from sample data, also known as a Training Dataset, and make predictions or decisions based on that data without explicit programming.
The term “Machine Learning” was coined in 1959 by Arthur Samuel, an American pioneer in the field of computer gaming and AI. ML tasks can be classified into several broad categories, which are outlined below.
Supervised Learning
Supervised Learning is a task-driven technique in which a machine predicts the next value based on labelled data. In this method, the machine is trained on a dataset containing both input and output values. The machine then learns the relationship between these inputs and outputs to make predictions.
Supervised learning can be further divided into the following two types:
- Classification (Defined Labels): In this technique, the output has defined labels. The goal is to categorize input data into predefined classes.
- Regression (No Labels Defined): This technique deals with continuous values, where the output is not predefined but rather a continuous quantity.
Examples of Supervised Learning:
- Linear Regression
- Nearest Neighbor
- Gaussian Naive Bayes
- Decision Trees
- Support Vector Machine (SVM)
- Random Forest
Unsupervised Learning
Unsupervised Learning is a data-driven technique in which the machine identifies clusters or hidden patterns in the data without any labelled outputs. The algorithm seeks to find underlying structures or relationships in the data based on its own experiences.
Examples of Unsupervised Learning:
- K-Means Clustering
- Apriori Algorithm (for Association Rule Learning)
Semi-supervised Learning
Semi-supervised Learning combines elements of both supervised and unsupervised learning. In this method, an algorithm learns from both labelled and unlabelled data. Typically, the dataset contains a large amount of unlabelled data with a small amount of labelled data, allowing the algorithm to bridge the gap between the two learning methods.
Examples of Semi-supervised Learning:
- Internet Content Classification
- Speech Analysis
- Protein Content Classification
Reinforcement Learning
Reinforcement Learning (RL) involves machines improving themselves based on past experiences. The system interacts with a dynamic environment to perform certain goals, receiving feedback in the form of rewards or penalties. This feedback helps the machine learn how to perform tasks more effectively over time.
Examples of Reinforcement Learning:
- Real-Time Decision Making
- Robot/Automobile Navigation
- Game AI
- Learning Tasks
- Skill Acquisition
Machine Learning is continuously evolving and is a powerful tool used in a variety of domains, including autonomous systems, predictive analytics, and natural language processing. By leveraging these different learning techniques, machines can improve their performance and adapt to complex environments, making them increasingly capable of handling real-world challenges.