Machine Learning lies under the umbrella of popular technology, Artificial Intelligence (AI). This technology aims to help systems gain the ability to learn automatically from past experience and use their knowledge to improve on their own without being explicitly programmed.
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Interesting or not, some of the most significant topics in Machine Learning are as follows:
Supervised Learning: Supervised learning is a technique in Machine Learning that allows the systems to learn mapping functions that help in mapping input-output pairs. An algorithm of supervised learning analyzes the labeled training data to develop the required function that can be useful in mapping new data.
Unsupervised Learning: Unsupervised learning is another Machine Learning technique that extracts inference from datasets that consist of unlabelled input data. It aims to model the distribution and underlying structure of the data to gain more knowledge from it.
Neural Network: Neural network programming paradigm is a biologically inspired technique that allows systems to learn themselves using observational data.
Deep Learning or Deep Neural Network (DNN): Deep Learning is a part of Machine Learning that is inspired by artificial neural networks (ANN). In this, several neural networking layers are stacked together to build a large network that helps in mapping input to output. This technology assists the network in extracting various necessary features until it lands on a familiar one.
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