Machine Learning: Difference between revisions

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==Types of Machine Learning==
==Types of Machine Learning==
* '''Supervised Learning:''' This involves learning from a training dataset with labeled data using classification and regression models. This learning process continues until the required level of performance is achieved.
* Supervised Learning
* '''Unsupervised Learning:''' This involves using unlabelled data and then finding the underlying structure in the data in order to learn more and more about the data itself using factor and cluster analysis models.
* Unsupervised Learning
* '''Semi-supervised Learning:''' This involves using unlabelled data like Unsupervised Learning with a small amount of labeled data. Using labeled data vastly increases the learning accuracy and is also more cost-effective than Supervised Learning.
* Semi-supervised Learning
* '''Reinforcement Learning:''' This involves learning optimal actions through trial and error. So the next action is decided by learning behaviors that are based on the current state and that will maximize the reward in the future.
* Reinforcement Learning
 
==References==
* [https://www.geeksforgeeks.org/how-to-start-learning-machine-learning/ Start Learning Machine Learning]

Revision as of 00:04, 26 June 2021

Arthur Samuel coined the term Machine Learning in 1959 and defined it as a Field of study that gives computers the capability to learn without being explicitly programmed. And that was the beginning of Machine Learning! In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According to Indeed, Machine Learning Engineer Is The Best Job of 2019 with a 344% growth and an average base salary of $146,085 per year.

Prerequisites

  • Linear Algebra and Multivariate Calculus
  • Statistics
  • Python

Various ML Concepts

  • Terminologies of ML
  • Types of ML
  • Practice ML
  • Learning Resources for ML
  • Take part in Competitions

Types of Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning

References

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