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Difference Between Machine Learning and Neural Network

Machine Learning

Machine Learning falls under the larger canvas of Artificial Intelligence. Machine Learning seeks to build intelligent systems or machines that can automatically learn and train themselves through experience, without being explicitly programmed or requiring any human intervention.

Neural Network

The structure of the human brain inspires a Neural Network. It is essentially a Machine Learning model (more precisely, Deep Learning) that is used in unsupervised learning. A Neural Network is a web of interconnected entities known as nodes wherein each node is responsible for a simple computation. In this way, a Neural Network functions similarly to the neurons in the human brain.

Below us the difference between Machine Learning and Neural Network

Machine Learning

Neural Network

Machine Learning uses advanced algorithms that parse data, learns from it, and use those learnings to discover meaningful patterns of interest.

Neural Network consists of an assortment of algorithms used in Machine Learning for data modelling using graphs of neurons.

Machine Learning model makes decisions according to what it has learned from the data

Neural Network arranges algorithms in a fashion that it can make accurate decisions by itself

Machine learning models can be categorized under two types – supervised and unsupervised learning models.

Neural Networks can be classified into feed-forward, recurrent, convolutional, and modular Neural Networks.

Skills required for Machine Learning include programming, probability and statistics, Big Data and Hadoop, knowledge of ML frameworks, data structures, and algorithms

Neural networks demand skills like data modelling, Mathematics, Linear Algebra and Graph Theory, programming, and probability and statistics.

Machine Learning is applied in areas like healthcare, retail, e-commerce (recommendation engines), BFSI, self-driving cars, online video streaming, IoT, and transportation and logistics, to name a few.

Neural Networks, on the other hand, are used to solve numerous business challenges, including sales forecasting, data validation, customer research, risk management, speech recognition, and character recognition, among other things.

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