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Machine Learning vs Neural Network

Machine Learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.it is the study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

Neural Network is computing systems vaguely inspired by the biological neural networks that constitute animal brains. it usually involves a large number of processors operating in parallel and arranged in tiers. The first tier receives the raw input information analogous to optic nerves in human visual processing. Each successive tier receives the output from the tier preceding it.

Machine Learning vs Neural Network

Machine Learning
Models follow the function that learned from the data.it can be two types

  • supervised is simply a process of learning algorithm from the training dataset.
  • unsupervised is modeling the underlying or hidden structure or distribution of the data to learn more about the data.

Its Eco-system is artificial intelligence.
there is a number of algorithms that can be applied to any data problem. These techniques include regression, k-means clustering, logistic regression, decision trees.
learning systems are adaptive and constantly evolving from new examples, so they are capable of determining the patterns in the data.
Applied areas of machine learning

  • Health Care.
  • Retail.
  • E-commerce.
  • Online recommendations.
  • Tracking price changes.
  • Better customer service and delivery systems.

Examples of Machine learning Siri, Google Maps, Google search.

Neural Network
deep learning models which are designed to frequently analyze data with the logic structure like how we humans would draw conclusions. It is a subset of machine learning.
Its Eco-system is artificial intelligence.
structures algorithms in layers of fashion, that can learn and make intelligent decisions on its own.
A typical neural network may have two to three layers, wherein deep learning network might have dozens or hundreds.
Applied areas of neural network

  • Finance.
  • Health Care.
  • Retail.
  • Machine learning.
  • Artificial intelligence.
  • Stock Exchange prediction.

Examples of image recognition, image compression, search engines.

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