Back to Practical Predictive Analytics: Models and Methods
University of Washington

Practical Predictive Analytics: Models and Methods

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. Learning Goals: After completing this course, you will be able to: 1. Design effective experiments and analyze the results 2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation 3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants) 4. Explain and apply a set of unsupervised learning concepts and methods 5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection

Status: Decision Tree Learning
Status: R Programming
Course8 hours

Featured reviews

FY

Reviewed Jan 18, 2016

Its Hard! but AWESOME, some much info packed in a few lectures!

ZP

Reviewed Dec 22, 2015

More dynamic visualisation please, and it will be 5*.

GJ

Reviewed Jul 16, 2021

This course helpemd me understand more about machine learning and a set of tools to help with the same.

NE

Reviewed Jun 7, 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.

BL

Reviewed Aug 6, 2019

Too little people participated and long peer review time.But the course content is good.

KR

Reviewed Nov 10, 2015

Very nice assignments and content. You learn a lot when you complete all assignments.

HD

Reviewed Aug 30, 2016

The entire course is an overview! This course will be a revision if you already know the concepts.

PV

Reviewed Nov 11, 2015

The topic the professor covers are awesome. Going from statistics to machine learning is something very awesome about this course

CY

Reviewed Jul 19, 2016

Nive that the course covered a broad range of topics.And good to get pushed to do some kaggle competition and peer review.

WL

Reviewed Jun 5, 2016

A quick overview of technology terms used for Machine Learning, and gentle introduction into learning through Kaggle.

TR

Reviewed Feb 16, 2016

Its a great review course. Prior knowledge is necessary

SP

Reviewed Dec 22, 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.

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