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Курс от University of Washington
courseraАнализ данных
Practical Predictive Analytics: Models and Methods
Курс от University of WashingtonО курсе
Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems. Learning Goals: After completing this course, you will be able to: 1. Design effective experiments and analyze the results 2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation 3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants) 4. Explain and apply a set of unsupervised learning concepts and methods 5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection
Навыки, которые вы освоите
Decision Tree LearningPredictive AnalyticsR ProgrammingStatistical InferenceData AnalysisNetwork AnalysisStatistical AnalysisStatistical MethodsAnalyticsApplied Machine LearningClassification AlgorithmsMachine Learning MethodsMachine LearningUnsupervised LearningBig DataStatisticsModel OptimizationGraph TheorySupervised LearningData Science
Программа курса
4 модулей · 72 учебных материалов
01Practical Statistical Inference28 материалов
Lesson 1: Basics of Statistical Inference
Appetite Whetting: Bad ScienceВидеоHypothesis TestingВидеоSignificance Tests and P-ValuesВидеоУчитесь у экспертов
Bill Howe
Director of Research