courseraАнализ данных

Data Mining Methods

Курс от University of Colorado Boulder

Средний≈ 24.4 чАнглийский

О курсе

This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE

Навыки, которые вы освоите

Algorithms Correlation Analysis Data Mining Bayesian Network Classification Algorithms Artificial Neural Networks Decision Tree Learning Unsupervised Learning Model Evaluation Supervised Learning Data Science Machine Learning Anomaly Detection Data Analysis Machine Learning Algorithms Machine Learning Methods

Программа курса

4 модулей · 53 учебных материалов

01Frequent Pattern Analysis24 материалов

Course Introduction

Course Updates and Accessibility SupportЧтение Earn Academic Credit for Your Work! Чтение Course SupportЧтение About This CourseЧтение

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Qin (Christine) Lv

Associate Professor

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Обучение на Coursera

≈ 24.4 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Немецкий, Русский, Тайский, Индонезийский, Шведский, Турецкий, Испанский, Хинди, Японский, Казахский, Польский

Data Mining Methods

О курсе

Навыки, которые вы освоите

Программа курса

Course Introduction

Учитесь у экспертов

Qin (Christine) Lv

Introduction to Apriori Algorithms

FP-growth Algorithm, Association, Correlation

Decision Tree Induction, Bayesian Classification

Support Vector Machines, Neural Network, Ensemble, Evaluation

Partitioning, Hierarchical, Grid-based, and Density-based Clustering

Probabilistic, High-dimensional, Bi-clustering, Graph, Constraint-based Clustering

Types of Outliers, Outlier Detection Methods

Mining Complex Data, Research Frontiers of Data Mining