courseraПрограммирование

Machine Learning Algorithms

Курс от Sungkyunkwan University

Уровень не указан≈ 15.9 чАнглийский

О курсе

In this course you will: a) understand the naïve Bayesian algorithm. b) understand the Support Vector Machine algorithm. c) understand the Decision Tree algorithm. d) understand the Clustering. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.

Навыки, которые вы освоите

Decision Tree Learning Machine Learning Algorithms Probability & Statistics Applied Machine Learning Unsupervised Learning Statistical Machine Learning Linear Algebra Classification Algorithms Machine Learning Python Programming Algorithms Supervised Learning Bayesian Statistics

Программа курса

4 модулей · 37 учебных материалов

01Naïve Bayes9 материалов

Probability Theory Ⅰ

Probability and Conditional ProbabilityВидео Learning Classifiers based on Bayes RuleЧтение Quiz 01Задание

Probability Theory Ⅱ

Bayesian Reasoning, and Independence

Учитесь у экспертов

Jaekwang KIM

Assistant Professor

Machine Learning Algorithms

О курсе

Навыки, которые вы освоите

Программа курса

Probability Theory Ⅰ

Probability Theory Ⅱ

Учитесь у экспертов

Jaekwang KIM

Naïve Bayesian Classifier

Support Vector Machine Ⅰ

Support Vector Machine Ⅱ

Support Vector Machine Ⅲ

Decision Tree Ⅰ

Decision Tree Ⅱ

Decision Tree Ⅲ

k-Means, k-Medoids

k-Means Example, Hierarchical Clustering

Gaussian Mixture Model