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Introduction to Machine Learning

Machine Learning

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. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

Machine Learning – Methods – Applications

Machine learning methods are incorporated with algorithms such are often categorized as supervised or unsupervised.

Supervised Methods: 

  • Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.  
  • Starting from the analysis of a known training data set, the learning algorithm produces an inferred function to make predictions about the output values. 
  • The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.

  Unsupervised Methods:

  • Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled.
  • Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.
  • The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

Applications:

  • Retail Market  and the stock market analysis
  • Healthcare Analytics
  • Robotics 
  • Financial Analysis
  • Social Media etc…
MACHINE LEARNING APPLICATIONS

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