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 Fundamentals for Java Developers · LearnSpace
Назад в каталог
courseraАнализ данных

Machine Learning Fundamentals for Java Developers

Курс от Board Infinity
Начальный≈ 12 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Course Description Master the fundamentals of machine learning using Java in this hands-on course tailored for developers. You’ll use tools like Weka, Smile, and Deeplearning4j to implement ML techniques including regression, classification, and clustering while strengthening your Java skills. In the first module, you’ll get introduced to core machine learning concepts, explore widely-used Java libraries, and understand the full ML workflow from data to model evaluation. The second module focuses on supervised learning. You'll implement regression, logistic regression, and decision trees in Java with step-by-step guidance. In the third module, you’ll dive into unsupervised learning—learning how to use K-Means clustering and apply dimensionality reduction techniques like PCA. The final module brings everything together through end-to-end projects, including data preprocessing, model training, cross-validation, debugging, and deploying your ML models. By the end, you will: -Understand and apply core ML techniques using Java libraries -Apply supervised and unsupervised learning techniques such as regression, classification, and clustering. -Create end-to-end ML workflows in Java, including data preprocessing, model training, and performance evaluation. This course is ideal for: -Java developers who want to transition into machine learning without switching to Python -Software engineers and backend developers looking to add ML capabilities to their Java-based applications -Students or professionals in computer science with basic Java skills who want to explore ML with hands-on implementation -Tech professionals preparing for roles in AI/ML, data science, or intelligent systems where Java is part of the stack" Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.

Навыки, которые вы освоите

Java ProgrammingLogistic RegressionData PreprocessingDecision Tree LearningModel TrainingModel EvaluationClassification AlgorithmsModel DeploymentMachine Learning Software

Программа курса

4 модулей · 62 учебных материалов

01Introduction to Machine Learning and Java19 материалов

Introduction to the Course

Course Welcome Video!Видео

Understanding Machine Learning

Introduction to Machine LearningВидеоSupervised, Unsupervised, and Reinforcement LearningВидеоReal-World Applications of Machine LearningВидео

Учитесь у экспертов

Board Infinity

Instructor

Machine Learning Fundamentals for Java Developers
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

≈ 12 ч

4 модулей

Язык: Английский

Субтитры: Венгерский, Узбекский, Казахский

Часть программы вашего университета
Read more about the fundamentals, types, and real-world applications of Machine Learning.Чтение
Foundations First: Exploring ML Concepts & Real-World Use CasesDIALOGUE
Practice Quiz: Understanding Machine LearningЗадание

Java Basics for ML

Java Syntax Refresher for Machine LearningВидеоSetting Up Java for ML Development (JDK, IDE, Maven)ВидеоOverview of ML Libraries in Java: Weka, Smile, Deeplearning4jВидеоRead more about Java basics, ML libraries, and JDK installation.ЧтениеPractice Quiz: Java Basics for MLЗадание

ML Project Workflow

Machine Learning Workflow: From Data to DeploymentВидеоCourse Project Walkthrough and ExpectationsВидеоRead more about Machine Learning lifecycleЧтениеFoundations of Machine Learning with Java: Concepts, Libraries & WorkflowDIALOGUEPractice Quiz: ML Project WorkflowЗаданиеGraded Quiz: Introduction to Machine Learning and JavaЗаданиеQuick Course Check-InPLUGIN
02Supervised Learning in Java18 материалов

Regression Techniques

Overview of Supervised LearningВидеоUnderstanding Linear RegressionВидеоImplementing Linear Regression in JavaВидеоRead more about Supervised learning, covering linear and polynomial regression in machine learning.ЧтениеPractice Quiz: Regression TechniquesЗадание

Classification Techniques

Classification Basics: Binary vs MulticlassВидеоUnderstanding Logistic RegressionВидеоImplementing Logistic Regression in JavaВидеоRead more about Classification basics, understanding Logistic Regression and implementing it.ЧтениеPractice Quiz: Classification TechniquesЗадание

Tree Models & Evaluation

Concepts Behind Decision Trees and Splitting CriteriaВидеоBuilding Decision Trees in JavaВидеоEvaluation Metrics: Accuracy, Precision, RecallВидеоConfusion Matrix and F1 Score ExplainedВидеоRead more about Decision Trees, their splitting criteria, and key evaluation metrics like F1 Score for machine learning models.ЧтениеMastering Supervised Learning: Regression, Classification & Evaluation in JavaDIALOGUE
03Unsupervised Learning in Java12 материалов

Clustering

Introduction to Unsupervised LearningВидеоK-Means Clustering ExplainedВидеоImplementing K-Means in JavaВидеоCluster Evaluation Techniques: Inertia and Silhouette ScoreВидеоRead more about Unsupervised Learning, K-Means Clustering, and Evaluating Clustering Performance.ЧтениеPractice Quiz: ClusteringЗадание

Dimensionality Reduction

When and Why to Use Dimensionality ReductionВидеоPrincipal Component Analysis (PCA) in JavaВидеоRead more about Dimensionality Reduction and Principal Component Analysis (PCA) in Machine Learning.ЧтениеExploring Unsupervised Learning: Clustering & Dimensionality Reduction in JavaDIALOGUEPractice Quiz: Dimensionality ReductionЗаданиеGraded Quiz: Unsupervised Learning in JavaЗадание
04Applied ML with Java13 материалов

Preprocessing and Validation

Data Preprocessing Techniques: Handling Missing Values and NormalizationВидеоData Splitting and Cross-Validation StrategiesВидеоBuilding ML Pipelines in JavaВидеоRead more about essential Machine Learning practices, including handling missing values, cross-validation, and building efficient ML pipelines.ЧтениеPractice Quiz: Preprocessing and ValidationЗадание

Project and Debugging

End-to-End Classification Project with JavaВидеоDebugging and Optimizing ML ModelsВидеоFinal Project Demonstration and Course Wrap-UpВидеоRead more about ML projects and essential debugging techniquesЧтениеHands-On ML in Java: Preprocessing, Validation & Project ImplementationDIALOGUEPractice Quiz: Project and DebuggingЗаданиеGraded Quiz: Applied ML with JavaЗаданиеCourse Closure!Видео
Practice Quiz: Tree Models & EvaluationЗадание
Graded Quiz: Supervised Learning in JavaЗадание