Explore the world of machine learning algorithms with this comprehensive course from Sungkyunkwan University. Designed for learners comfortable with Python programming and basic mathematics, this course delves into the fundamentals of naïve Bayesian, Support Vector Machine, Decision Tree, and Clustering algorithms.
Throughout the course, you will gain a thorough understanding of probability, conditional probability, Bayesian reasoning, and independence. You will also learn about the formal descriptions of algorithms, classifier learning, regularization, linear support vector machine, dual form learning, kernel methods, optimal margin classifiers, decision tree key ideas, regression tree, random forests, and clustering techniques such as k-means, k-medoids, Gaussian mixture model, mixture models, and more. The course wraps up with quizzes and a final test to consolidate your learning.
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Dive into the complexities of machine learning algorithms through four comprehensive modules on naïve Bayesian, Support Vector Machine, Decision Tree, and Clustering. Gain an in-depth understanding of the concepts, applications, and techniques involved in these algorithms.
Delve into the fundamentals of naïve Bayesian algorithm, covering probability, conditional probability, Bayesian reasoning, classifier learning, regularization, and more. This module provides a solid foundation for understanding machine learning algorithms.
Gain insights into the Support Vector Machine algorithm, covering linear support vector machine, dual form learning, kernel methods, and optimal margin classifiers. Explore the principles and techniques behind this powerful machine learning algorithm.
Explore the key ideas of the Decision Tree algorithm, including regression tree, random forests, game theory, linear programming, and its applications. Understand the complexities and intricacies of decision tree algorithms.
Dive into the world of Clustering, covering k-means, k-medoids, Gaussian mixture model, mixture models, approximation algorithms, and clustering of stable instances. Gain a comprehensive understanding of clustering techniques and their applications.
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