Créé par :   University of Washington

  • Emily Fox

    Enseigné par :    Emily Fox, Amazon Professor of Machine Learning

    Statistics

  • Carlos Guestrin

    Enseigné par :    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
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Enroll and get full access to every course in the Specialization for 7 days. Cancel any time.
Programme de cours

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Travail en cours
Travail en cours

Chaque cours fonctionne comme un manuel interactif en proposant des vidéos préenregistrées, des quiz et des projets.

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Créateurs
University of Washington
Tarification
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Notation et examens
Note moyenne 4.6 sur 5 sur 703 notes

E

For me, this was the toughest of the first four courses in this specialization (now that the last two are cancelled, these are the only four courses in the specialization). I'm satisfied with what I gained in the process of completing these four courses. While I've forgotten most of the details, especially those in the earlier courses, I now have a clearer picture of the lay of the land and am reasonably confident that I can use some of these concepts in my work. I also recognize that learning of this kind is a life-long process. My plan next is to go through [https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370], which, based on my reading of the first chapter, promises to be an excellent way to review and clarify the concepts taught in these courses.

What I liked most about the courses in this specializations are: good use of visualization to explain challenging concepts and use of programming exercises to connect abstract discussions with real-world data. What I'd have liked to have more of is exercises that serve as building blocks -- these are short and simple exercises (can be programming or otherwise) that progressively build one's understanding of a concept before tackling real-world data problems. edX does a good job in this respect.

My greatest difficulty was in keeping the matrix notations straight. I don't have any linear algebra background beyond some matrix mathematics at the high school level. That hasn't been much of a problem in the earlier three courses, but in this one I really started to feel the need to gain some fluency in linear algebra. [There's an excellent course on the subject at edX: https://courses.edx.org/courses/course-v1%3AUTAustinX%2BUT.5.05x%2B1T2017/ and I'm currently working through it.]

Regardless of what various machine learning course mention as prerequisites, I think students would benefit from first developing a strong foundation in programming (in this case Python), calculus, probability, and linear algebra. That doesn't mean one needs to know these subjects at an advanced level (of course, the more the better), but rather that the foundational concepts are absolutely clear. I'm hoping this course at Coursera would be helpful in this regard: https://www.coursera.org/learn/datasciencemathskills/

Really a good course, succinct and concise.

Great course on machine learning, however, left us in middle of learning, Recommender System + Deep Learning Capstone is missing