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Unit 1

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Machine Learning (ML) Tutorial Machine Learning , often abbreviated as  ML  is a branch of  Artificial Intelligence (AI)  that works on algorithm developments and statistical models that allow computers to learn from data and make predictions or decisions without being explicitly programmed. Hence, in simpler terms,  machine learning allows computers to learn from data and make decisions or predictions without being explicitly programmed to do so . Essentially, machine learning algorithms learn patterns and relationships from data, allowing them to generalize from instances and make predictions or conclusions on new and uncovered data. How does Machine Learning Work? Broadly Machine Learning process includes Project Setup, Data Preparation, Modeling and Deployment. The following figure demonstrates the common working process of Machine Learning. It follows some set of steps to do the task; a sequential process of its workflow is as follows - Stages of ...
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Syllabus_MLT

  Unit I – Learning, Types of Learning, Well defined learning problems, Designing a Learning System, History of ML, Introduction of Machine Learning Approaches – (Artificial Neural Network, Clustering, Reinforcement Learning, Decision Tree Learning, Bayesian networks, Support Vector Machine, Genetic Algorithm), Issues in Machine Learning and Data Science Vs Machine Learning;   Unit   II REGRESSION: Linear Regression and Logistic Regression BAYESIAN LEARNING - Bayes theorem, Concept learning, Bayes Optimal Classifier, Naïve Bayes classifier, Bayesian belief networks, EM algorithm. SUPPORT VECTOR MACHINE: Introduction, Types of support vector kernel – (Linear kernel, polynomial kernel and Gaussian kernel), Hyperplane – (Decision surface), Properties of SVM, and Issues in SVM. Unit III DECISION TREE LEARNING - Decision tree learning algorithm, Inductive bias, Inductive inference with decision trees, Entropy and information theory, Information gain, ID-3 Algorithm, Iss...
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