It’s no doubt that the Machine Learning certification offered by Stanford University via Coursera is a massive success . This is undoubtedly in-part thanks to the excellent ability of the course’s creator Andrew Ng to simplify some of the more complex aspects of ML into intuitive and easy-to-learn concepts.
Read moreWhat is cs229?
Course Description This course provides a broad introduction to machine learning and statistical pattern recognition.
Read moreIs cs229 same as Coursera?
There is nothing to indicate the Coursera class and CS 229 are the same . CS 229 is a grad-level machine learning class that assumes heavy math prerequisites; the syllabus is completely different. The Coursera class is closest to CS 229a at Stanford.
Read moreIs Andrew Ng a data scientist?
Ng was a co-founder and head of Google Brain and was the former chief scientist at Baidu, building the company’s Artificial Intelligence Group into a team of several thousand people. … Andrew NgScientific careerFieldsArtificial intelligence, machine learning, natural language processing, computer visionAndrew Ng – Wikipedia en.wikipedia.org › wiki › Andrew_Ng
Read moreIs Andrew Ng’s machine learning course still the best machine learning course available?
Stanford’s Machine Learning course taught by Andrew Ng was released in 2011. 8 years after publication, Andrew Ng’s course is still ranked as one of the top machine learning courses .
Read moreIs Andrew Ng’s course good?
It turns out that it’s really difficult to write good programming assignments for a course, and Andrew Ng does a really good job. It’s one of the best-run technical courses on Coursera . The main reason is that the programming assignments are substantial and require a solid understanding of the material to complete.
Read moreIs Andrew Ng course still relevant?
Stanford’s Machine Learning course taught by Andrew Ng was released in 2011. 8 years after publication, Andrew Ng’s course is still ranked as one of the top machine learning courses . This has become a staple course of Coursera and, to be honest, in machine learning.
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