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

Basic Info
Course 1 of 4 in the Machine Learning Specialization.
Commitment6 weeks of study, 5-8 hours/week
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

How It Works

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

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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.
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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