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Model Evaluation, Interpretation, & Communication · LearnSpace
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Model Evaluation, Interpretation, & Communication

Курс от Coursera
Уровень не указан≈ 24.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

By completing this course, you will be able to evaluate machine learning models using metrics aligned to real business objectives, diagnose training behavior through learning curves, interpret predictions using statistical and explainability techniques, and clearly communicate results and implications to diverse audiences. This course helps you move beyond simply building models to truly understanding, validating, and explaining them. You will learn how to assess whether a model is performing well for the right reasons, identify the key drivers behind predictions, and recognize when models may be misleading, overfitting, or degrading over time. Just as importantly, you will develop the ability to translate complex technical findings into clear, actionable insights that stakeholders can trust and act upon. What makes this course unique is its end-to-end focus on evaluation, interpretation, and communication—skills that are often treated as secondary but are critical in real-world machine learning. Drawing on expertise from multiple leading institutions, the course combines rigorous evaluation methods, modern explainability frameworks like SHAP and LIME, and practical data storytelling techniques. Whether you are preparing models for production, presenting results to leadership, or validating model behavior for ethical and business reasons, this course equips you with the tools and confidence to make your machine learning work transparent, credible, and impactful.

Навыки, которые вы освоите

Statistical Machine LearningModel EvaluationDecision Tree LearningStorytellingRegression AnalysisPredictive ModelingMachine Learning MethodsBusiness EthicsClassification AlgorithmsData PresentationModel OptimizationData StorytellingAnalyticsResponsible AIData PipelinesData EthicsMachine Learning AlgorithmsModel TrainingApplied Machine LearningSupervised Learning

Программа курса

7 модулей · 93 учебных материалов

01Start Here: Get Oriented and Check Your Skills2 материалов
Start Here: How This Skill-Based Course WorksЧтениеSkill Diagnostic: Find Your Recommended Starting PointЗадание
02Supervised Learning31 материалов

Supervised Learning Fundamentals

What Is Supervised Learning?Чтение

Учитесь у экспертов

Professionals from the Industry

Преподаватель курса

Model Evaluation, Interpretation, & Communication
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

≈ 24.1 ч

7 модулей

Язык: Английский

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

Часть программы вашего университета
How Supervised Models Are Trained and Used in Real LifeЧтение
Regression in Action: Predicting Sales From Advertising Видео
Classification in Action: Predicting Diabetes From Patient DataВидео
Knowledge Check: Supervised Learning BasicsЗадание

Linear Regression

What Is Linear Regression and How Does It Work? ЧтениеEvaluating a Linear Regression ModelЧтениеUnderstanding Regression Through a Real-World ExampleВидеоScript-Building and Evaluating a Simple Linear Regression ModelВидеоPredicting House Prices Using Linear RegressionЛабораторнаяKnowledge Check: Linear Regression Key ConceptsЗадание

Logistic Regression

What Is Logistic Regression and Why Do We Use It? ЧтениеHow Do We Know If Our Classification Model Works?ЧтениеGetting Started with Logistic Regression for Binary ClassificationВидеоEvaluating Binary Classification Models with Logistic RegressionВидеоPredicting Loan Approval Using Logistic RegressionЛабораторнаяKnowledge Check: Logistic Regression Key ConceptsЗадание

Decision Trees & Random Forests

How Do Decision Trees Work?ЧтениеHow Decision Trees Make Predictions in HealthcareВидеоEvaluating Decision Tree Performance and Avoiding OverfittingВидеоDecision Trees: Pros, Cons, and an AlternativeЧтениеImproving Model Accuracy with Random ForestsВидеоAttrition Prediction Using Decision Trees & Random ForestsЛабораторнаяKnowledge Check: Decision Trees & Random Forests Key ConceptsЗадание

Support Vector Machines (SVM)

How Support Vector Machines Make Decisions ЧтениеUnderstanding the Kernel Trick in SVMsЧтениеUsing SVMs to Recognize Handwritten DigitsВидеоHow SVMs Make Decisions: Margins and Support VectorsВидеоUsing the RBF Kernel to Improve ClassificationВидеоClassifying Handwritten Digits Using SVMsЛабораторнаяKnowledge Check: SVM Key ConceptsЗадание
03Linear and Logistic Regression8 материалов

Linear Regression

Introduction to RegressionВидеоIntroduction to Simple Linear RegressionВидеоMultiple Linear RegressionВидеоPolynomial and Non-Linear RegressionВидеоPractice Quiz: Linear Regression Задание

Logistic Regression

Introduction to Logistic RegressionВидеоTraining a Logistic Regression ModelВидеоPractice Quiz: Logistic RegressionЗадание
04Evaluating and Validating Machine Learning Models13 материалов

Evaluating Machine Learning Models

Classification Metrics and Evaluation TechniquesВидеоLab: Evaluating Classification ModelsВнешний инструментRegression Metrics and Evaluation TechniquesВидеоLab: Evaluating Random Forest PerformanceВнешний инструментEvaluating Unsupervised Learning Models: Heuristics and TechniquesВидеоLab: Evaluating K-means ClusteringВнешний инструментPractice Quiz: Evaluating Machine Learning ModelsЗадание

Best Practices for Ensuring Model Generalizability

Cross-Validation and Advanced Model Validation TechniquesВидеоRegularization in Regression and ClassificationВидеоLab: Regularization in Linear RegressionВнешний инструментData Leakage and Other PitfallsВидеоLab: Machine Learning Pipelines and GridSearchCVВнешний инструментPractice Quiz: Best Practices for Ensuring Model GeneralizabilityЗадание
05Local Explainability Methods for Deep Learning Models20 материалов

Local Interpretable Model Agnostic Explanations

Local Interpretable Model Agnostic Explanations (LIME)ВидеоLIME in Time-Series ClassificationВидеоWhy Should I Trust You?ЧтениеPractical Exercise: Interpretability of heartbeat classification using LIME and an NNMLP modelЧтениеPractical Exercise: Interpretability of heartbeat classification using LIME and a CNN modelЧтениеPractical Exercise: Interpretability of heartbeat classification using LIME and an LSTM modelЧтение

Shapley Additive Explanations

Shapley Additive ExplanationsВидеоA Unified Approach to Interpreting Model PredictionsЧтение

Model-Specific Explanations for Deep Learning: Visualisation Methods

Model-Specific Explanations: Visualisation MethodsВидеоCAM in Time-Series ClassificationВидеоPractical Exercise: Interpretability of CNN models using Class Activation MapsЧтениеClass Activation MappingЧтениеPractice QuizЗадание

Interactive notebook examples

Interpretability of heartbeat classification using a CNN model and Class Activation MapsЛабораторнаяInterpretability of heartbeat classification using a CNN model and CAMЛабораторнаяLIME interpretability for heartbeat classification with a convolutional neural networkЛабораторнаяInterpretability of heartbeat classification using an LSTM model and CAMЛабораторнаяLIME interpretability for heartbeat classification with a long short-term memory networkЛабораторнаяLight - LIME interpretability for heartbeat classification with a long short-term memory networkЛабораторная
06Data storytelling fundamentals17 материалов

Data storytelling fundamentals

From technical skills to business valueВидеоData storytellingВидеоCrafting a narrativeВидеоPractice QuizЗадание

Creating a report

Identifying your main conclusionВидеоChoosing supporting evidenceВидеоOrdering evidenceВидеоDesigning your reportВидеоComparing two reportsЧтениеGetting feedback on your reportВидеоPractice QuizЗадание

Choosing the right format

Creating a memoВидеоCreating a notebookВидеоCreating a slide deckВидеоCreating appendicesВидеоPractice QuizЗадание

Graded Lab

Opening a Coffee Shop in NYC - Coworker's NotebookЛабораторная
07Assessment2 материалов

Lesson

Learner Expectations for Skill AssessmentЧтениеSkill AssessmentЗадание
LIME interpretability for heartbeat classification with a multi-layer perceptronЛабораторная