Introduction
Machine learning is programming Computers to optimize a performance criterion using example data or past experience. Welcome have a model defined up to some parameters, and learning is the execution of a Computer program to optimize the parameters of the model using the training data or past experience. The model mat be predictive to make predictions in the future, or descriptive to gain knowledge from data or both.
Aurthur Samuel, an early American leader in the field of computer gaming and artificial intelligence, coined the term "Machine learning" in 1959 while at IBM. He Defined machine learning as the field of study that gives Computers the ability to learn without being explicitly programmed.
Components of learning.
1 Data storage
2 Abstraction
3 Generation
4 Evaluation
Unit 2 Decision Tree.
Decision Trees are a type of supervised machine learning that is you explain what the input is and what the corresponding output is in the training data. whare the data is continuously split according to a certain parameters.
Example.
1 Classification Trees
2 Regression trees
Diagram
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