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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