Machine learning

Machine learning:                                    

                           In the field of computer science Machine learning is the statistical techniques to give computers, the ability to LEARN with data without being programmed explicitly by human."MACHINE LEARNING" name was stamped in 1959 by Arthur Samuel“.Machine learning and statistics are closely related to each other. 

         


The process of learning begins with observations or data. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust the actions accordingly signal.     
                                                                                     
 Task in Machine Learning:    

                            Task in machine Learning are classified two categories, depends on the learning ‘signal’ or 'Feedback' to the system,      

                  


Task in Machine Learning:   
                            Task in machine Learning are classified two categories, depends on the learning ‘signal’ or 'Feedback' to the system,     

v  Supervised learning: 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 dataset, the learning algorithm produces an inferred function to make predictions about the output values.               
Ø  Semi-supervised learning: fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy.           
Ø  Active learning:                                              
Ø  Reinforcement learning: is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning.  
v   Unsupervised learning: 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.

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