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Supervised learning unsupervised learning and reinforcement learningWe will learn about the fundamental differences between the three different learning types and using conceptual examples we will develop an understanding of the practical problem domains where they can be applied. What is machine learning.
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It is seen as a part of artificial intelligenceMachine learning algorithms build a model based on sample data known as training data in order to make predictions or decisions without being explicitly programmed to do so.
. Machine Learning is a field of AI consisting of learning algorithms that. Boosting is a Ensemble learning meta-algorithm for primarily reducing variance in supervised learning. Is a widely used and effective machine learning algorithm based on the idea of bagging.
Unsupervised learning models are used when we only have the input variables X and no corresponding output variables. Machine learning ML is the study of computer algorithms that can improve automatically through experience and by the use of data. It is basically a family of machine learning algorithms that convert weak learners to strong ones.
Boosting is based on the question posed by Michael Kearns and Leslie Valiant 1988 1989 Can a set of weak learners create a single strong. Algorithms Bagging with Random Forests Boosting with XGBoost are examples of ensemble techniques. In this section we will take a look at the three types of machine learning.
Bootstrap aggregating also called bagging from bootstrap aggregating is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regressionIt also reduces variance and helps to avoid overfittingAlthough it is usually applied to decision tree methods it can be used with any. Boosting and Bagging Boosting. Machine Learning MCQ with Answers.
Machine Learning is a part of Data Science an area that deals with statistics.
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