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.
This course is part of the Data Science at Scale Specialization
Offered By


About this Course
Skills you will gain
- Random Forest
- Predictive Analytics
- Machine Learning
- R Programming
Offered by

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.
Syllabus - What you will learn from this course
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.
Reviews
- 5 stars48.05%
- 4 stars32.14%
- 3 stars10.06%
- 2 stars5.51%
- 1 star4.22%
TOP REVIEWS FROM PRACTICAL PREDICTIVE ANALYTICS: MODELS AND METHODS
Professor Bill Howe gives great reactions to when there are typos on the slides!
More dynamic visualisation please, and it will be 5*.
Its Hard! but AWESOME, some much info packed in a few lectures!
This course helpemd me understand more about machine learning and a set of tools to help with the same.
About the Data Science at Scale Specialization
Learn scalable data management, evaluate big data technologies, and design effective visualizations.

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