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.
Este curso forma parte de Programa especializado: ciencia de los datos a escala
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ofrecido por
Programa especializado: ciencia de los datos a escalaUniversidad de WashingtonAcerca de este Curso
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Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 2 de 4 en
Aprox. 7 horas para completar
Inglés (English)
SubtÃtulos: Francés (French), Portugués (europeo), Coreano, Ruso (Russian), Inglés (English), Español (Spanish)
Habilidades que obtendrás
- Random Forest
- Predictive Analytics
- Machine Learning
- R Programming
Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 2 de 4 en
Aprox. 7 horas para completar
Inglés (English)
SubtÃtulos: Francés (French), Portugués (europeo), Coreano, Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
Universidad de Washington
Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.
Programa - Qué aprenderás en este curso
2 horas para completar
Practical Statistical Inference
Learn the basics of statistical inference, comparing classical methods with resampling methods that allow you to use a simple program to make a rigorous statistical argument. Motivate your study with current topics at the foundations of science: publication bias and reproducibility.
2 horas para completar
28 videos (Total 121 minutos)
3 horas para completar
Supervised Learning
Follow a tour through the important methods, algorithms, and techniques in machine learning. You will learn how these methods build upon each other and can be combined into practical algorithms that perform well on a variety of tasks. Learn how to evaluate machine learning methods and the pitfalls to avoid.
3 horas para completar
26 videos (Total 111 minutos), 1 lectura, 1 cuestionario
1 hora para completar
Optimization
You will learn how to optimize a cost function using gradient descent, including popular variants that use randomization and parallelization to improve performance. You will gain an intuition for popular methods used in practice and see how similar they are fundamentally.
1 hora para completar
11 videos (Total 41 minutos)
1 hora para completar
Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
1 hora para completar
4 videos (Total 21 minutos)
Reseñas
- 5 stars48,05Â %
- 4 stars32,14Â %
- 3 stars10,06Â %
- 2 stars5,51Â %
- 1 star4,22Â %
Principales reseñas sobre PRACTICAL PREDICTIVE ANALYTICS: MODELS AND METHODS
por SM23 de feb. de 2016
Professor Bill Howe gives great reactions to when there are typos on the slides!
por GJ16 de jul. de 2021
This course helpemd me understand more about machine learning and a set of tools to help with the same.
por KR10 de nov. de 2015
Very nice assignments and content. You learn a lot when you complete all assignments.
por ZP22 de dic. de 2015
More dynamic visualisation please, and it will be 5*.
Acerca de Programa especializado: ciencia de los datos a escala
Learn scalable data management, evaluate big data technologies, and design effective visualizations.
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