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.
Ce cours fait partie de la Spécialisation Science des données à grande échelle
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À propos de ce cours
Compétences que vous acquerrez
- Random Forest
- Predictive Analytics
- Machine Learning
- R Programming
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Université 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.
Programme de cours : ce que vous apprendrez dans ce cours
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.
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.
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.
Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
Avis
- 5 stars48,05 %
- 4 stars32,14 %
- 3 stars10,06 %
- 2 stars5,51 %
- 1 star4,22 %
Meilleurs avis pour PRACTICAL PREDICTIVE ANALYTICS: MODELS AND METHODS
Professor Bill Howe gives great reactions to when there are typos on the slides!
Nive that the course covered a broad range of topics. And good to get pushed to do some kaggle competition and peer review.
A quick overview of technology terms used for Machine Learning, and gentle introduction into learning through Kaggle.
Very nice assignments and content. You learn a lot when you complete all assignments.
À propos du Spécialisation Science des données à grande échelle
Learn scalable data management, evaluate big data technologies, and design effective visualizations.

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