Created by:  University of Washington

  • Carlos Guestrin

    Taught by:  Carlos Guestrin, Amazon Professor of Machine Learning

    Computer Science and Engineering

  • Emily Fox

    Taught by:  Emily Fox, Amazon Professor of Machine Learning

    Statistics
Basic Info
Course 1 of 4 in the Machine Learning Specialization.
Commitment6 weeks of study, 5-8 hours/week
Language
English, Subtitles: Korean, Vietnamese, Chinese (Simplified)
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.

Help from Your Peers
Help from Your Peers

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

Certificates
Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

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
AuditPurchase Course
Access to course materials

Available

Available

Access to graded materials

Not available

Available

Receive a final grade

Not available

Available

Earn a shareable Course Certificate

Not available

Available

Ratings and Reviews
Rated 4.6 out of 5 of 5,504 ratings

A very good introduction to Machine Learning Concepts & Principles. Easy to understand, while at the same time, giving a solid foundation on the models.

The only detractor is: usage of GraphLab - feels very custom and thrust upon. Definitely easier to use due to the abstraction inherent ion the programming constructs provided but IMHO, would have been much better to use more generic and popular packages like sci-kit, TensorFlow, etc.

Finally, the DL concepts in week6 felt a bit rushed and confusing. Admittedly, it maybe hard to abstract into a week.

I liked the interactive python programming however the course could be more rigorous for my tastes.

This course provides a broad overview of many of the topics in machine learning such as regression, classification, deep learning, and recommender systems. The lectures are well done and interesting and the Jupyter Notebooks provide walkthroughs of the concepts covered. The only negative is that this course is not rigorous enough. There is very little in-depth programming and all of the assignments focus on a high-level introduction rather than a rigorous implementation. Overall, a good introduction to the concepts, but this course alone will not provide learners with the skills needed to build a novel implementation of any of the covered machine learning techniques.

Excellent course for folks who need to understand ML and how it can be used in an array of day to day applications