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Machine Learning and Data Analytics Part 2 · LearnSpace
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Machine Learning and Data Analytics Part 2

Курс от Northeastern University
Средний≈ 18.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course delves into both the theoretical aspects and practical applications of data mining within the field of engineering. It provides a comprehensive review of the essential fundamentals and central concepts underpinning data mining. Additionally, it introduces pivotal data mining methodologies and offers a guide to executing these techniques through various algorithms. Students will be introduced to a range of data mining techniques, such as clustering, the extraction of association rules, support vector machines, neural networks, and the exploration of other complex techniques. Additionally, we will use case studies to explore the application of data mining across diverse sectors, including but not limited to manufacturing, healthcare, medicine, business, and various service industries.

Навыки, которые вы освоите

Data MiningMachine Learning AlgorithmsData PreprocessingRegression AnalysisClassification AlgorithmsText MiningArtificial Neural NetworksUnsupervised LearningTime Series Analysis and ForecastingSupervised LearningUnstructured DataFeature EngineeringTrend AnalysisPredictive ModelingForecastingDeep Learning

Программа курса

7 модулей · 64 учебных материалов

01Key Regression Techniques - Part 213 материалов

Getting Started

Course IntroductionЧтениеMeet Your Faculty: Chinthaka Pathum "Dinesh" Herath GedaraЧтениеMachine Learning and Data Analytics Part 2 SyllabusЧтениеAcademic IntegrityЧтение

Lesson 1: Ridge Regression

Учитесь у экспертов

Chinthaka Pathum Dinesh Herath Gedara

Assistant Teaching Professor

Xuemin Jin

Преподаватель курса

Machine Learning and Data Analytics Part 2
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 18.4 ч

7 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Казахский, Венгерский

Часть программы вашего университета
Ridge RegressionЧтение
Ridge RegressionВидео
Advantages and Limitations of Ridge RegressionЧтение

Lesson 2: Lasso Regression

Lasso Regression ЧтениеLasso Regression ВидеоAdvantages and Limitations of Lasso RegressionЧтение

Lesson 3: Feature Selection Machine Learning Model

Feature Selection Machine Learning ModelЧтениеFeature Selection Machine Learning ModelВидео

Assess Your Learning

Module 8: Assess Your Learning: Key Regression TechniquesЗадание
02Clustering: A Focus on Core Algorithms11 материалов

Lesson 1: K-Means Clustering

K-Means ClusteringЧтениеK-Means ClusteringВидеоK-Means Clustering: Advantages and LimitationsЧтение

Lesson 2: Hierarchical Clustering (Distance Matrices)

Hierarchical Clustering (Distance Matrices)ЧтениеHierarchical Clustering (Distance Matrices)ВидеоHierarchical Clustering Advantages and LimitationsЧтение

Lesson 3: DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

DBSCANЧтениеDBSCANВидеоSilhouette Coefficient and Variance Ratio CriterionЧтениеDBSCAN Advantages and LimitationsЧтение

Assess Your Learning

Module 9: Assess Your Learning: Clustering AlgorithmsЗадание
03Core Algorithms in Association Rule Mining10 материалов

Lesson 1: Association Rule Mining

Association Rule MiningЧтениеAssociation Rule MiningВидеоApriori Algorithm Advantages and LimitationsЧтение

Lesson 2: FP-Growth (Frequent Pattern Growth) Algorithm

FP-Growth Чтение FP-Growth ВидеоFP-Growth: Advantages and LimitationsЧтение

Lesson 3: Collaborative Filtering

Collaborative FilteringЧтениеCollaborative FilteringВидеоCollaborative Filtering: Advantages and LimitationsЧтение

Assess Your Learning

Module 10: Assess Your Learning: Association Rule MiningЗадание
04Support Vector Machines (SVM)9 материалов

Lesson 1: Support Vector Machines—Hard Margins

SVM Hard MarginsЧтениеSVM Hard MarginsВидео

Lesson 2: SVM Soft Margins

SVM Soft MarginsЧтениеSVM Soft MarginsВидео

Lesson 3: Kernels

KernelsЧтениеKernelsВидеоKernel Trick ExampleЧтениеKernels Advantages and LimitationsЧтение

Assess Your Learning

Module 11: Assess Your Learning: Support Vector MachinesЗадание
05The Neural Network7 материалов

Lesson 1: Neural Networks

Neural NetworksЧтениеNeural NetworksВидео

Lesson 2: Implementing Neural Networks

Implementing Neural NetworksЧтениеImplementing Neural NetworksВидеоChain RuleВидеоAdvantages and Limitations of Neural NetworksЧтение

Assess Your Knowledge

Module 12: Assess Your Learning: Neural NetworksЗадание
06Text Mining6 материалов

Lesson 1: Introduction to Text Mining

Text MiningЧтениеText MiningВидео

Lesson 2: Text Processing and Feature Extraction

Text Preprocessing and Feature ExtractionЧтениеText Preprocessing and Feature ExtractionВидеоText Preprocessing Advantages and LimitationsЧтение

Assess Your Learning

Module 13: Assess Your Learning: Text MiningЗадание
07Time Series Analysis8 материалов

Lesson 1: Time Series Analysis

Time Series AnalysisЧтениеTime Series AnalysisВидео

Lesson 2: ARIMA Models

ARIMA ModelsЧтениеARIMA ModelВидеоKey Time Series Models and TechniquesВидеоARIMA Models Advantages and LimitationsЧтение

Assess Your Learning

Module 14: Assess Your Learning: Time Series AnalysisЗаданиеCongratulations!Чтение