Machine Learning

Fri, 08 Mar 2024 07:41:13 GMT — Properties

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Supervised

Supervised learning is defined as when a model gets trained on a “labelled dataset”. Labelled datasets have both input and output parameters. In supervised learning algorithms learn to map points between inputs and correct outputs. It has both training and validation datasets labelled.

Unsupervised

Unsupervised learning is a type of machine learning technique in which an algorithm discovers patterns and relationships using unlabeled data. Unlike supervised learning, unsupervised learning doesn’t involve providing the algorithm with labelled target outputs. The primary goal of unsupervised learning is often to discover hidden patterns, similarities, or clusters within the data, which can then be used for various purposes, such as data exploration, visualisation, dimensionality reduction, and more.

Reinforcement Learning

Reinforcement machine learning algorithm is a learning method that interacts with the environment by producing actions and discovering errors. Trial, error, and delay are the most relevant characteristics of reinforcement learning. In this technique, the model keeps on increasing its performance using reward feedback to learn the behaviour or pattern. These algorithms are specific to a particular problem, for example, Google's self-driving car, AlphaGo, where a bot competes with humans and even itself to get better and better performers in the game of Go. Each time we feed in data, they learn and add the data to their knowledge which is training data. So, the more it learns the better it gets trained and hence experienced.

Deep Learning and Deep Neural Networks

Deep learning is a subset of machine learning that uses multi-layered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain.

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