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Applied Machine Learning Without Coding

Курс от Edureka
Средний≈ 8.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Machine learning is no longer exclusive to developers. This course gives you the hands-on skills to build, evaluate, and optimize regression and classification models using Orange Data Mining — a powerful visual ML platform — without writing a single line of code. Throughout this course, you'll move from core ML fundamentals and essential mathematics through to practical model building, evaluation, and tuning — all through an intuitive visual workflow interface designed for data professionals and business users alike. Every technique is demonstrated through clear, instructor-led video walkthroughs that you can follow along on your own Orange setup, pausing and replaying as needed to build confidence at every step. By the end of this course, you'll be able to: - Build and evaluate regression models using linear regression, SVMs, and Random Forests with visual Orange workflows. - Apply classification algorithms including logistic regression, decision trees, KNN, and Naive Bayes to solve real-world prediction problems. - Evaluate model performance using RMSE, MAE, R², confusion matrices, and ROC curves to compare and select optimal models. - Perform feature selection and hyperparameter tuning in Orange to improve model accuracy and generalization without coding. This course is designed for a diverse audience: aspiring data analysts, machine learning beginners, business analysts, domain experts, and non-technical professionals who want to explore predictive analytics through a no-code approach. Basic familiarity with data concepts and spreadsheets, is recommended before enrolling. Gain the confidence to build and interpret machine learning models that solve real business problems — all through an intuitive visual interface with Orange Data Mining.

Навыки, которые вы освоите

Model EvaluationApplied Machine LearningClassification AlgorithmsRegression AnalysisFeature EngineeringMachine Learning SoftwareLogistic RegressionRandom Forest AlgorithmData ProcessingModel OptimizationPredictive ModelingStatistical ModelingData ManipulationData AnalysisMachine LearningMachine Learning MethodsData ScienceData VisualizationExploratory Data AnalysisSupervised Learning

Программа курса

4 модулей · 64 учебных материалов

01Introduction to Orange, ML Foundations and Mathematics20 материалов

Introduction to Orange Data Mining

Course IntroductionВидеоCourse Outline: No-Code Machine Learning with OrangeЧтениеWhat is Orange? Visual Programming for Data ScienceВидеоOrange Interface Widgets, Canvas and Workflow ConceptsВидео

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Edureka

Преподаватель курса

Applied Machine Learning Without Coding
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Обучение на Coursera

≈ 8.6 ч

4 модулей

Язык: Английский

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

Часть программы вашего университета
Hands-On: Installing Orange and Creating the First WorkflowВидео
Introduction to Orange Data MiningЧтение
Practice Assignment: Introduction to Orange Data MiningЗадание

Basic Mathematics for Machine Learning

Basic Linear Algebra Vectors, Matrices and Simple OperationsВидеоHands-On: Probability Basics and Continuous DistributionsВидеоUnderstanding Slopes and Optimization GradientsВидеоBasic Mathematics for Machine LearningЧтениеPractice Assignment: Basic Mathematics for Machine LearningЗадание

Introduction to Machine Learning Concepts

Machine Learning FundamentalsВидеоOverfitting, Underfitting, and Bias-Variance TradeoffВидеоTrain-Test Split, Cross-Validation and Model SelectionВидеоIntroduction to Machine Learning ConceptsЧтениеPractice Assignment : Introduction to Machine Learning ConceptsЗадание

Module Wrap-Up and Assessment

Module Summary: Introduction to Orange, ML Foundations and MathematicsЧтениеKnowledge Check: Introduction to Orange, ML Foundations and MathematicsЗаданиеLaunching Your First No-Code ML Workflow in OrangeDIALOGUE
02Regression Modeling - Basic to Advanced20 материалов

Linear Regression Fundamentals

Understanding Regression Types and Mathematics of Linear RegressionВидеоHands-On: Feature Selection for Linear RegressionВидеоHands-On: Building Linear Regression Models in OrangeВидеоLinear Regression FundamentalsЧтениеPractice Assignment: Linear Regression FundamentalsЗадание

Advanced Regression - SVM & Random Forest

Support Vector Machines and Random Forest for RegressionВидеоHands-On: Building SVM Regression Models in OrangeВидеоHands-On: Building Random Forest Regression Models in OrangeВидеоAdvanced Regression: SVM and Random ForestЧтениеPractice Assignment : Advanced Regression: SVM and Random ForestЗадание

Regression Evaluation & Hyperparameter Tuning

Regression Metrics: RMSE, MAE, R² Score and Model EvaluationВидеоModel SelectionВидеоHands-On: Model Evaluation and Residual Analysis in OrangeВидеоHyperparameter Tuning ConceptsВидеоHands-On: Hyperparameter Tuning in OrangeВидеоRegression Evaluation and Hyperparameter TuningЧтение

Module Wrap-Up and Assessment

Module Summary : Regression Modeling - Basic to AdvancedЧтениеGraded Assignment: Regression Modeling: Basic to AdvancedЗаданиеDesigning and Optimizing Regression Models in OrangeDIALOGUE
03Classification Modeling - Basic to Advanced18 материалов

Classification Fundamentals and Basic Algorithms

Understanding Classification - Types and Mathematics of Classification AlgorithmsВидеоHands-On: Building Logistic Regression and Decision Tree Classification Models in OrangeВидеоHands-On: K-Nearest Neighbors and Naive Bayes Classification ModelsВидеоClassification Fundamentals and Basic AlgorithmsЧтениеPractice Assignment : Classification Fundamentals and Basic AlgorithmsЗадание

Advanced Classification - SVM & Random Forest

Support Vector Machines and Random Forest for ClassificationВидеоHands-On: Building SVM Classification Models in OrangeВидеоHands-On: Building Random Forest Classifiers and Feature ImportanceВидеоAdvanced Classification : SVM and Random ForestЧтениеPractice Assignment : Advanced Classification: SVM and Random ForestЗадание

Classification Evaluation & Hyperparameter Tuning

Confusion Matrix, ROC Curves and Classification MetricsВидеоHands-On: Model Evaluation with ROC-AUC and Performance ComparisonВидеоHands-On: Hyperparameter Tuning and Final Model SelectionВидеоClassification Evaluation and Hyperparameter TuningЧтениеPractice Assignment : Classification Evaluation & Hyperparameter TuningЗадание

Module Wrap-Up and Assessment

Module Summary : Classification Modeling : Basic to AdvancedЧтениеGraded Assignment: Classification Modeling: Basic to AdvancedЗаданиеDesigning and Optimizing Classification Models in OrangeDIALOGUE
04Course Wrap-Up6 материалов

Course Wrap-up and Assessments

Final Checkpoint: Reflecting on End-to-End No-Code Machine Learning with OrangeDIALOGUEPractice Project: Building an End-to-End No-Code ML System for FinNova AnalyticsЧтениеKnowledge Check: No-Code Machine Learning with OrangeЗаданиеEnd-to-End No-Code Machine Learning Strategy Using OrangeЗаданиеEnd-to-End No-Code ML Consulting SimulationDIALOGUECourse SummaryВидео
Practice Assignment : Regression Evaluation and Hyperparameter TuningЗадание