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Курс от Johns Hopkins University
courseraАнализ данных
Applied Machine Learning: Techniques and Applications
Курс от Johns Hopkins UniversityО курсе
The course "Applied Machine Learning: Techniques and Applications" focuses on the practical use of machine learning across various domains, particularly in computer vision, data feature analysis, and model evaluation. Learners will gain hands-on experience with key techniques, such as image processing and supervised learning methods while mastering essential skills in data pre-processing and model evaluation. This course stands out for its balance between foundational concepts and real-world applications, giving learners the opportunity to work with widely-used datasets and tools like scikit-learn. Topics include image classification, object detection, feature extraction, and the selection of evaluation metrics for assessing model performance. By completing this course, learners will be equipped with the practical skills necessary to implement machine learning solutions, enabling them to apply these techniques to solve complex problems in data processing, computer vision, and more.
Навыки, которые вы освоите
Applied Machine LearningData PreprocessingModel EvaluationSupervised LearningData TransformationData CleansingFeature EngineeringClassification AlgorithmsRegression AnalysisData ProcessingData IntegrationModel OptimizationComputer VisionMachine LearningModel TrainingMachine Learning MethodsScikit Learn (Machine Learning Library)Machine Learning SoftwareImage AnalysisMachine Learning Algorithms
Программа курса
4 модулей · 48 учебных материалов
01Application of Machine Learning in Computer Vision13 материалов
Foundations of Applied Machine Learning in Computer Vision
Introduction to Applied Machine LearningВидеоApplication of Machine Learning in Computer Vision OverviewВидеоУчитесь у экспертов
Erhan Guven
Преподаватель курса