Created by:  University of Washington

  • Emily Fox

    Taught by:  Emily Fox, Amazon Professor of Machine Learning

    Statistics

  • Carlos Guestrin

    Taught by:  Carlos Guestrin, Amazon Professor of Machine Learning

    Computer Science and Engineering
Basic Info
Course 4 of 4 in the Machine Learning Specialization.
Commitment6 weeks of study, 5-8 hours/week
Language
English
How To PassPass all graded assignments to complete the course.
User Ratings
4.6 stars
Average User Rating 4.6See what learners said
Syllabus

FAQs
How It Works
Coursework
Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates
Certificates

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Creators
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.
Pricing
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Ratings and Reviews
Rated 4.6 out of 5 of 769 ratings

awesome course!

sometimes difficult,but import so many useful knowledge

The materials presented are excellent with well prepared skeleton codes for all ML models. Comparing this course to its three preceding ones, this course is more challenging both conceptually and computationally. The slight drawback is that, because of the highly technical nature of the last three weeks' materials, there isn't enough guidance about how one may construct the ML algorithms from scratch, that is, learners with less experience in computing will, more or less, have to accept the sample codes with little confidence about how to (re)write such codes in the first place.

As a result, I believe that learners with more experience in algorithms and data structure (or learners who proceed to learn more about this area) are likely to gain more from this course for at least two reasons: i) they are more comfortable with the complicated ML algorithms; ii) they can improve the algorithms to speed up the estimation time (some advanced techniques are quite computationally expensive, say over 20 minutes).

In general, I have learnt very much from this course and love it.

I liked the slides.