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.
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このコースについて
習得するスキル
- Random Forest
- Predictive Analytics
- Machine Learning
- R Programming
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ワシントン大学(University of 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.
シラバス - 本コースの学習内容
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.
レビュー
- 5 stars48.05%
- 4 stars32.14%
- 3 stars10.06%
- 2 stars5.51%
- 1 star4.22%
PRACTICAL PREDICTIVE ANALYTICS: MODELS AND METHODS からの人気レビュー
Hands on practices are very good. learning predictive model was a challenge.
Need some background in R or Python and the lectures are from around 2013. Most of the material is still relevant.
A quick overview of technology terms used for Machine Learning, and gentle introduction into learning through Kaggle.
Professor Bill Howe gives great reactions to when there are typos on the slides!
規模に応じたデータサイエンス専門講座について
Learn scalable data management, evaluate big data technologies, and design effective visualizations.

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