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Курс от Johns Hopkins University
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Practical Machine Learning
Курс от Johns Hopkins UniversityО курсе
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
Навыки, которые вы освоите
Predictive ModelingRandom Forest AlgorithmModel EvaluationMachine Learning AlgorithmsRegression AnalysisR ProgrammingData PreprocessingClassification And Regression Tree (CART)Feature EngineeringSupervised LearningClassification AlgorithmsModel TrainingMachine Learning SoftwarePredictive AnalyticsApplied Machine LearningMachine Learning MethodsMachine Learning
Программа курса
4 модулей · 39 учебных материалов
01Week 1: Prediction, Errors, and Cross Validation14 материалов
Учитесь у экспертов
Jeff Leek, PhD
Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics in Public Health Sciences
Roger D. Peng, PhD
Professor of Statistics and Data Sciences
Brian Caffo, PhD
Professor, Biostatistics