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: Data Analysis
Status: Probability & Statistics
Course8 hours

Featured reviews

DS

5.0Reviewed Jul 2, 2020

Hands on practices are very good. learning predictive model was a challenge.

KR

5.0Reviewed Nov 10, 2015

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

NE

4.0Reviewed 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.

KP

5.0Reviewed Feb 7, 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 .

GJ

5.0Reviewed Jul 16, 2021

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

RS

4.0Reviewed Jun 12, 2017

Very good approach to each method; the assignments are a good test for the topics.

FY

5.0Reviewed Jan 18, 2016

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

TR

5.0Reviewed Feb 16, 2016

Its a great review course. Prior knowledge is necessary

HD

4.0Reviewed Aug 30, 2016

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

PV

5.0Reviewed Nov 11, 2015

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

CY

5.0Reviewed 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.

AS

5.0Reviewed Nov 23, 2015

Excellent course with amazing practical exercises!

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