Desenvolvido por:   University of Washington

  • Emily Fox

    Ministrado por:    Emily Fox, Amazon Professor of Machine Learning

    Statistics

  • Carlos Guestrin

    Ministrado por:    Carlos Guestrin, Amazon Professor of Machine Learning

    Computer Science and Engineering
Basic Info
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
Programa

Perguntas frequentes
Como funciona
Trabalho
Trabalho

Cada curso é como um livro didático interativo, com vídeos pré-gravados, testes e projetos.

Ajuda dos seus colegas
Ajuda dos seus colegas

Conecte-se com milhares de outros aprendizes, debata ideias, discuta sobre os materiais do curso e obtenha ajuda para dominar conceitos.

Certificados
Certificados

Obtenha reconhecimento oficial pelo seu trabalho e compartilhe seu sucesso com amigos, colegas e empregadores.

Desenvolvedores
University of Washington
Custo
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Acesso aos materiais do curso

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Acesso a materiais valendo nota

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Receba uma nota final

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Obtenha um certificado compartilhável

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Classificações e avaliações
Avaliado em 4.6 de 5 decorrente de 694 avaliações

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

Some themes are shown very superficially it would be great to go deeper. Despite of this the course is great!

Thanks.



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