К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
Machine Learning and Data Analytics Part 1 · LearnSpace
Назад в каталог
courseraАнализ данных

Machine Learning and Data Analytics Part 1

Курс от Northeastern University
Средний≈ 15.3 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 data preprocessing, the extraction of association rules, classification, prediction, clustering, and the exploration of complex data, and will implement a capstone project exploring the same. 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

Model EvaluationData MiningClassification AlgorithmsRegression AnalysisExploratory Data AnalysisDimensionality ReductionPerformance MeasurementStatistical ModelingPredictive ModelingData ProcessingData PreprocessingMachine Learning AlgorithmsStatistical AnalysisData AnalysisData ScienceAnalyticsPerformance AnalysisCase StudiesMachine Learning MethodsApplied Machine Learning

Программа курса

7 модулей · 106 учебных материалов

01Intro to Data Mining in Engineering14 материалов

Getting Started

Course IntroductionЧтениеCourse OverviewВидеоMeet Your Faculty: Chinthaka Pathum "Dinesh" Herath GedaraЧтениеMachine Learning and Data Analytics Part 1 SyllabusЧтение

Учитесь у экспертов

Chinthaka Pathum Dinesh Herath Gedara

Assistant Teaching Professor

Xuemin Jin

Преподаватель курса

Machine Learning and Data Analytics Part 1
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 15.3 ч

7 модулей

Язык: Английский

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

Часть программы вашего университета
Academic IntegrityЧтение

Lesson 1: Intro to Data Mining - Data

Intro to Data Mining-DataЧтениеIntro to Data Mining-DataВидео

Lesson 2: Intro to Data Mining - Mining

Intro to Data Mining-MiningЧтение Intro to Data Mining-MiningВидеоData Mining Life CycleЧтение

Lesson 3: Data Mining Techniques

Data Mining Techniques ЧтениеData Mining Techniques ВидеоTypes of Machine LearningЧтение

Assess Your Learning

Module 1: Assess Your Learning: Introduction to Data Mining in EngineeringЗадание
02Exploratory Data Analysis and Visualization17 материалов

Lesson 1: Introduction to Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA)ЧтениеExploratory Data Analysis (EDA)Видео

Lesson 2: Data Cleaning and Preprocessing

Data Cleaning and PreprocessingЧтениеData Cleaning and PreprocessingВидеоData Cleaning and Preprocessing MethodsЧтение

Lesson 3: Data Visualization Techniques

Data Visualization Techniques: Charts and PlotsЧтениеBar and Pie ChartsЧтениеLine Graphs and Scatter PlotsЧтениеPearson Correlation, Pair Plots, and Radar ChartsЧтениеParallel Coordinates Plot and Sankey PlotЧтениеHistograms, Box Plots and Violin ChartЧтениеArea Plots and Bubble ChartsЧтение Heat, Tree, and Choropleth MapsЧтениеWord Clouds and Network GraphsЧтение

Lesson 4: Data Transformation

Data Transformation ЧтениеData Transformation Видео

Assess Your Learning

Module 2: Assess Your Learning: Exploratory Data Analysis and VisualizationЗадание
03Dimensionality Reduction17 материалов

Lesson 1: Dimensionality Reduction

Dimensionality Reduction ЧтениеWhy Dimensionality Reduction?Чтение

Lesson 2: Feature Selection-Linear Methods PCA

Feature Selection and ExtractionЧтениеFeature Extraction–Linear Methods PCAЧтениеFeature Selection–Linear Methods PCAВидео

Lesson 3: Principal Component Analysis (CPA)

Principal Component Analysis (PCA)ЧтениеPCAВидео

Lesson 4: Covariance & Correlation Matrix

Covariance MatrixЧтениеCorrelation MatrixЧтениеPCA ExampleЧтение

Lesson 5: t-SNE

t-SNEЧтениеt-SNE: What is it?Видеоt-SNE: How it WorksВидео

Lesson 6: Linear Discriminant Analysis (LDA)

Linear Discriminant Analysis (LDA) ЧтениеLinear Discriminant Analysis (LDA) ВидеоLDA ExampleЧтение

Assess Your Learning

Module 3: Assess Your Learning: Dimensionality ReductionЗадание
04Performance Evaluation Matrices15 материалов

Lesson 1: Introduction to Performance Evaluation Metric

Performance Evaluations MetricЧтениеPerformance Evaluations MetricВидео

Lesson 2: Bias Variance Trade-Off

Bias Variance Trade-Off ЧтениеBias Variance Trade-Off Видео

Lesson 3: Regression Metrics

Regression MetricsЧтениеRegression ExampleЧтениеRegression MetricsВидео

Lesson 4: Classification Metrics

Classification MetricsЧтениеClassification Metrics- Accuracy, Precision, RecallВидеоClassification Metrics- F1 Score, ROC-AUCВидеоClassification ReviewЧтениеAUCЧтениеLift and Gains ChartЧтениеPractical Application of Lift and Gains Chart

Assess Your Learning

Module 4: Assess Your Learning: Performance Evaluation MetricsЗадание
05Foundational Classification Algorithms - Part 116 материалов

Lesson 1: Classification

Classification ЧтениеClassificationВидео

Lesson 2: K-Nearest Neighbors (KNN) Model Distances

K-Nearest Neighbors (KNN) Model Distances ЧтениеK-Nearest Neighbors (KNN) Model Distances Видео

Lesson 3: Performing KNN, Picking Best K, Propensity Score, and Regression Prediction

Performing KNN, Picking Best K, Propensity Score, and Regression Prediction ЧтениеPerforming KNN, Picking Best K, Propensity Score, and Regression Prediction ВидеоKNN ExampleЧтениеKNN–Advantages and LimitationsЧтение

Lesson 4: Logistic Regression, Intuitions, Odds/Logits, and Interpretation

Logistic Regression, Intuitions, Odds/Logits, and InterpretationЧтениеLogistic Regression, Intuitions, Odds/Logits, and InterpretationВидео

Lesson 5: Parameter Estimation

Parameter EstimationЧтениеParameter EstimationВидеоLogistic Regression ExampleЧтение

Lesson 6: Multiclass Classification

Multiclass ClassificationЧтениеMulticlass ClassificationВидео

Assess Your Learning

Module 5: Assess Your Learning: Foundational Classification Algorithms - Part 1Задание
06Foundational Classification Algorithms - Part 217 материалов

Lesson 1: Bayes Classifier

Bayes Classifier ЧтениеBayes Classifier ВидеоNaive BayesЧтениеBayesian Decision TheoryЧтениеHow to Apply Bayes Classifier in a DatasetЧтениеHow to Apply Naive Bayes Classifier in a DatasetЧтение

Lesson 2: Decision Trees

Decision Trees ЧтениеDecision Trees ВидеоDecision Trees: Regression Task ЧтениеDecision Trees: Regression Analysis ВидеоDecision Trees ExamplesЧтениеDecision Tree Advantages and LimitationsЧтение

Lesson 3: Ensemble Learning

Ensemble LearningЧтениеEnsemble LearningВидеоBoosting Algorithms AdaBoost (Adaptive Boosting)ЧтениеBoosting Algorithms: Gradient Boosting Machines (GBM)Чтение

Assess Your Learning

Module 6: Assess Your Learning: Foundational Classification Algorithms - Part 2Задание
07Key Regression Techniques - Part 110 материалов

Lesson 1: Linear Regression

Linear RegressionЧтениеLinear RegressionВидеоLinear Regression: Calculating Coefficients and Minimizing Cost FunctionЧтениеLinear Regression: Calculating Coefficients and Minimizing Cost FunctionВидеоApplying Linear Regression in a DatasetЧтениеAdvantages and Disadvantages of Linear Regression ModelsЧтение

Lesson 2: Polynomial Regression

Polynomial RegressionЧтениеPolynomial RegressionВидео

Assess Your Learning

Module 7: Assess Your Learning: Linear, Multiple and Logistic Regression TechniquesЗаданиеCongratulations!Чтение
Чтение