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Machine Learning and its Applications · LearnSpace
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Machine Learning and its Applications

Курс от University of Glasgow
Начальный≈ 18.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course provides a practical introduction to machine learning techniques for data analysis in MATLAB, focusing on widely used methods for real-world technical applications. You will begin by exploring the core concepts behind machine learning, including model workflows, data preparation, and the factors that affect model performance. The course then focuses on two popular techniques—support vector machines and artificial neural networks—as well as MATLAB apps that make model building and evaluation more accessible. Using practical examples, you will prepare data, build machine learning workflows, and apply classification and regression methods to science and engineering problems. By the end of the course, you will be able to use MATLAB to develop, test, and evaluate predictive models for real-world applications. In partnership with MathWorks, enrolled learners receive access to MATLAB for the duration of the course.

Навыки, которые вы освоите

Classification AlgorithmsMachine Learning MethodsData QualityData PreprocessingModel TrainingModel EvaluationMachine Learning SoftwareStatistical Machine LearningMachine Learning AlgorithmsStatistical MethodsData ValidationMathematical SoftwareData Processing

Программа курса

5 модулей · 119 учебных материалов

01Machine Learning Fundamentals I: Basic Concepts27 материалов

Welcome

Welcome to the specialization - Applied AI for Engineers and Scientists: FoundationsВидеоSpecialization and Course StructureЧтениеSpecialization Sample CertificateЧтениеStay connected with UofG OnlineЧтение

Module 7 Introduction

Учитесь у экспертов

Bo Liu

Professor

Machine Learning and its Applications
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Обучение на Coursera

≈ 18.6 ч

5 модулей

Язык: Английский

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

Часть программы вашего университета
Introduction to Module 7's StudyВидео
How to Access MATLAB OnlineЧтение
Machine Learning in Engineering Practice: Tool, Replacement, or Decision Aid?Чтение

Module 7-1 Machine Learning Fundamentals

Machine Learning Fundamentals: What is Machine LearningВидеоMachine Learning Fundamentals: Fundamental Concepts in Machine Learning (1)ВидеоMachine Learning Fundamentals: Fundamental Concepts in Machine Learning (2)ВидеоMaterials for Machine Learning FundamentalsЧтениеFrom Explicit Rules to Learned Models: What Actually Changes?ЧтениеModule 1 Learning CoachDIALOGUE

Module 7-2 Mapping Inputs to Outputs

Mapping Inputs to Outputs: Data RepresentationВидеоMapping Inputs to Outputs: Parametric ML ModelВидеоMapping Inputs to Outputs: Non-Parametric ML ModelВидеоMapping Inputs to Outputs: Evaluate OutputВидеоMaterials for Mapping Inputs to OutputsЧтениеHow Data Representation and Model Choice Shape What a Model Can LearnЧтение

Module 7-3 MATLAB Implementation

MATLAB Implementation: Simple Linear Regression and KNNВидеоMaterials about MATLAB ImplementationЧтениеFrom Concept to Code: What the Model Is Actually DoingЧтение

Module 7 Quiz

Module 7 QuizЗадание

Module 7 Assignments

Module 7 Assignment 1Внешний инструментModule 7 Assignment 2Внешний инструментCSV File for Module 7 Assignment ЧтениеModule 7 RecapЧтение
02Machine Learning Fundamentals II: Model Training and Evaluation20 материалов

Module 8 Introduction

Introduction to Module 8's StudyВидеоWhat Does “Good” Mean in Machine Learning Models?Чтение

Module 8-1 ML Model Training Fundamentals

ML Model Training Fundamentals: Parameters, Hyperparameters, and Loss FunctionsВидеоML Model Training Fundamentals: Loss Functions and Gradient DescentВидеоML Model Training Fundamentals: Gradient DescentВидеоMaterials for ML model trainingЧтениеWhat Does It Actually Mean to “Train” a Machine Learning Model?Чтение

Module 8-2 ML Model Evaluation Fundamentals

ML Model Evaluation Fundamentals: Fundamental Concepts in ML Model EvaluationВидеоML Model Evaluation Fundamentals: Fundamental Concepts in ML Model EvaluationВидеоMaterials on ML Model Evaluation FundamentalsЧтениеWhen a Model Fails: Is It Bias, Variance, or Something Else?Чтение

Module 8-3 Summary of the ML Process

Summary of the ML ProcessВидеоMaterials for ML ProcessЧтениеFrom Data to Decisions: Reconstructing the Machine Learning PipelineЧтениеModule 8 Learning CoachDIALOGUE

Module 8 Quiz

Module 8 QuizЗадание

Module 8 Assignments

Module 8 Assignment 1Внешний инструментModule 8 Assignment 2Внешний инструментCSV file for Module 8 AssignmentЧтениеModule 8 RecapЧтение
03Data Preparation26 материалов

Module 9 Introduction

Introduction to Module 9's StudyВидеоWhy Data Preparation Is Not Optional in Machine LearningЧтение

Module 9-1 Basic Data Cleaning Review

Basic Data Cleaning Review ВидеоMaterials on Basic Data Cleaning ЧтениеCleaning Data Is a Decision, Not a ChecklistЧтение

Module 9-2 Distributions, Outliers and Their Removal

Distributions, Outliers and Their Removal: Gaussian Distribution, Skewness and OutliersВидеоDistributions, Outliers and Their Removal: The Z-Score and IQR Method for Outliers RemovalВидеоMaterials on Distributions, Outliers and Their RemovalЧтениеOutliers, Assumptions, and When “Cleaning” Becomes DamageЧтение

Module 9-3 Data Transform

Data Transform: Normalization, Standardization, Power TransformВидеоMaterials on Data TransformЧтениеWhen Scaling Helps — and When It Quietly Breaks Your ModelЧтение

Module 9-4 Construct Training and Test Sets for Model Evaluation

Construct Training and Test Sets for Model Evaluation: Methods, Implementation, and Stratified SamplingВидеоConstruct Training and Test Sets for Model Evaluation: Cross-ValidationВидеоMaterials on Training/Test Sets GenerationЧтениеEvaluation Starts with the Split: When Performance Numbers LieЧтение

Module 9-5 Data Preparation Overview

Data Preparation OverviewВидеоMaterials on Data PreparationЧтениеFrom Raw Data to Reliable Learning: What Actually Matters?ЧтениеData Preparation and Evaluation CoachDIALOGUE

Module 9 Quiz

Module 9 QuizЗадание

Module 9 Assignments

Module 9 Assignment 1Внешний инструментModule 9 Assignment 2Внешний инструментModule 9 Assignment 3Внешний инструментXLS file for Module 9 AssignmentЧтениеModule 9 RecapЧтение
04Support Vector Machines22 материалов

Module 10 Introduction

Introduction to Module 10's StudyВидеоWhy Support Vector Machines?Чтение

Module 10-1 Support Vector Machine Fundamentals

Support Vector Machine Fundamentals: ConceptsВидеоSupport Vector Machine Fundamentals: Types of SVMВидеоMaterials on Support Vector Machine FundamentalsЧтение

Module 10-2 Support Vector Machines

Support Vector Machines: Linear SVM of Hard Margin ClassifierВидеоSupport Vector Machines: Linear SVM of Soft Margin ClassifierВидеоSupport Vector Machines: Non-Linear SVMВидеоSupport Vector Machines: Multi-Class SVMВидеоMaterials on Support Vector MachinesЧтение

Module 10-3 Support Vector Machine Implementation and Case Studies

Support Vector Machine Implementation: MATLAB Implementation of SVMВидеоSupport Vector Machine Implementation: Iris Flower ExampleВидеоSupport Vector Machine Implementation: 2D Point Classification ExampleВидеоCase Study 1: Banknote Classification (Linear SVM)ВидеоCase Study 2: Raisin Classification (Non-Linear SVM)ВидеоMaterials on Support Vector Machine Implementation and Case StudiesЧтение

Module 10 Quiz

Module 10 QuizЗадание

Module 10 Assignments

Module 10 Assignment 1Внешний инструментModule 10 Assignment 2Внешний инструментCSV file for Module 10 AssignmentЧтениеModule 10 RecapЧтение
05Artificial Neural Networks24 материалов

Module 11 Introduction

Introduction to Module 11's StudyВидео

Module 11-1 Artificial Neural Network Introduction

Artificial Neural Network Introduction: IntroductionВидеоArtificial Neural Network Introduction: ANN StructureВидеоMaterials on ANN fundamentalsЧтение

Module 11-2 Neural Network Training

Neural Network Training: Forward PropagationВидеоNeural Network Training: Backward PropagationВидеоNeural Network Training: More on Neural Network TrainingВидеоMaterials on Neural Network TrainingЧтение

Module 11-3 Underfitting and Overfitting

Underfitting and Overfitting: ConceptsВидеоUnderfitting and Overfitting: Methods to Improve OverfittingВидеоMaterials on Underfitting and OverfittingЧтение

Module 11-4 ANN Implementation in MATLAB and Case Studies

ANN Implementation in MATLAB and Case Studies: Build an ANN Using MATLABВидеоANN Implementation in MATLAB and Case Studies: A Case Study on Diabetes DiagnosisВидеоMaterials on ANN ImplementationЧтение

Module 11 Tutorials

Tutorial: MATLAB Classification and Regression Learner AppsВидеоTutorial: Classification LearnerВидеоTutorial: Export ModelВидеоTutorial: Regression LearnerВидеоArtificial Neural Network Learning CoachDIALOGUE

Module 11 Quiz

Module 11 QuizЗадание

Module 11 Assignment

Module 11 AssignmentВнешний инструментMAT files for Module 11 AssignmentЧтениеModule 11 RecapЧтениеKeeping Learning with UofG OnlineЧтение
Support Vector Machine Learning CoachDIALOGUE