Before learning about the types, first, understand what is machine learning. Machine learning is a subset of AI that allows machines to learn from data, improve performance based on previous experiences, and make predictions. Machine learning is a collection of algorithms that work with massive amounts of data. Data is fed into these algorithms to train them, the model is built, and a specific task is performed based on the training.
Based on the methods and ways of learning, machine learning is divided into mainly four types, which are:
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi-Supervised Machine Learning
- Reinforcement Learning
Supervised Machine Learning
Supervised machine learning is based on supervision. We train the machines using the “labelled” dataset, and the device predicts the output based on the training. The labelled data indicates that some inputs have been mapped to the production. Put another way, we train the machine with the information and corresponding output and ask the device to predict the outcome.
The supervised learning technique’s primary goal is to map the input variable (x) to the output variable (y). Risk assessment, fraud detection, spam filtering, and other real-world supervised learning applications are examples.
2. Unsupervised Machine Learning
Unsupervised learning differs from supervised learning in that there is no need for supervision, as the name implies. In unsupervised machine learning, the machine is trained using an unlabeled dataset and predicts the output without charge.
The unsupervised learning algorithm’s primary goal is to group or categorise the unsorted dataset based on similarities, patterns, and differences. Machines are told to find hidden ways in the input dataset.
3. Semi-Supervised Learning
Semi-supervised learning is a machine learning algorithm that falls somewhere between supervised and unsupervised learning. It bridges the gap between Supervised (with labelled training data) and Unsupervised (with no labelled training data) learning algorithms by combining labelled and unlabeled datasets during the training period.
The concept of semi-supervised learning is introduced to address the shortcomings of both supervised and unsupervised learning algorithms. The main goal of semi-supervised learning is to effectively use all available data rather than just labelled data as supervised learning. Initially, similar information is clustered using an unsupervised learning algorithm, and it then aids in marking the unlabeled data. It is because labelled data is more expensive to acquire than unlabeled data.
4. Reinforcement Learning
Reinforcement learning is a feedback-based process in which an AI agent (a software component) explores its surroundings automatically by hitting and trailing, taking action, learning from experiences, and improving its performance. Because the agent is rewarded for every good act and punished for every wrong move, the goal of the reinforcement learning agent is to maximise the rewards.
There is no labelled data in reinforcement learning, as there is in supervised learning, and agents learn solely from their experiences. Because of how it works, reinforcement learning is used in various fields, including game theory, operations research, information theory, and multi-agent systems.
The Markov Decision Process can be used to formalise a reinforcement learning problem (MDP). The agent in MDP constantly interacts with the environment and performs actions; with each movement, the atmosphere responds and generates a new state. Another topic you may like to read is FEA Analysis.