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What is Machine Learning

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Definition of  Machine Learning and Introduction Concepts of Machine Learning Introduction What is machine learning ? History of Machine Learning Benefits of Machine Learning Advantages of Machine Learning Disadvantages of Machine Learning
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Know the Machine Learning Syllabus

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Learn Machine Learning Step-by-step INDEX  1. Introduction to Machine Learning What is Machine Learning? Applications of Machine Learning Machine Learning Lifecycle Types of Machine Learning   2. Exploratory Data Analysis Data Cleaning and Preprocessing Data Visualization Techniques Feature Extraction and Feature Selection  
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What is Analytical Machine Learning

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Analytical  and  Explanation-based learning  with domain theories  Analytical Learning Concepts Introduction Learning with perfect domain theories: PROLOG-EBG Explanation-based learning Explanation-based learning of search control knowledge Analytical Learning Definition :  Analytical learning is a type of machine learning that uses statistical and mathematical techniques to analyze and make predictions based on data.
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What is Well-posed learning

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  Perspectives and Issues of Well-posed learning What is well-posed learning? Well-posed learning is a type of machine learning where the problem is well-defined, and there exists a unique solution to the problem.  Introduction Designing a learning system Perspectives and issues in machine learning
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What is Bayes Theorem

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Bayesian Theorem and Concept Learning  Bayesian learning Topics Introduction Bayes theorem Concept learning Maximum Likelihood and least squared error hypotheses Maximum likelihood hypotheses for predicting probabilities Minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve Bayes classifier, an example: learning to classify text,  Bayesian belief networks, the EM algorithm. What is Bayesian Learning? Bayesian learning is a type of machine learning that uses Bayesian probability theory to make predictions and decisions based on data.
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