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.
ofrecido por
Programa especializado: Data Science at ScaleUniversidad de WashingtonAcerca de este Curso
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Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 2 de 4 en
Aprox. 7 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Coreano, Ruso (Russian), Inglés (English), Español (Spanish)
Habilidades que obtendrás
Random ForestPredictive AnalyticsMachine LearningR Programming
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 2 de 4 en
Aprox. 7 horas para completar
Inglés (English)
Subtítulos: 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
Principales reseñas sobre PRACTICAL PREDICTIVE ANALYTICS: MODELS AND METHODS
por NE7 de jun. de 2017
I think the amount of course work to lectures was more appropriate than the first segment. I enjoyed the exercises and felt that they mixed the correct amount of theory and applicaiton.
por SP22 de dic. de 2016
Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.
por WK5 de jun. de 2017
Excellent Lectures. Since the course is several years old the organization of some of the assignments needs updating. That's the only reason I gave it 4 instead of 5 stars.
por KP7 de feb. de 2016
I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .
Acerca de Programa especializado: Data Science at Scale
Learn scalable data management, evaluate big data technologies, and design effective visualizations.
Preguntas Frecuentes
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