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Machine Learning (Andrew Ng)
Stanford University
8 recommenders · 11 threads · recommended in 7 years, 2011–2023 · score 11.5
Andrew Ng's Machine Learning course from Stanford, most often discussed as the older Stanford CS229 lectures (recorded around 2008-2009) rather than the Coursera adaptation. It covers foundational ML algorithms and theory at a deeper level than the Coursera version.
Commenters call the Stanford lectures much more meaty and mathematically deep than the Coursera class, and a strong next step after the introductory version.
Machine Learning & AIintermediatefreevideo lectures#machine learning#stanford cs229#andrew ng#statistics#theory
Recommendations by year
20112023
Correct, the coursera course uses octave or matlab, but if you take the Stanford video lectures and look up the problem sets they are in python. His courses are fantastic.
His Stanford lecture is excellent, and much more "meaty" than the Coursera class. It's a full-on lecture, that delves much deeper into the theory than the coursera ml-class. As for your questions: it's an excellent starting point in ML, and covers a lot of basics. I think it can help you in…
Ng's Coursera class is for absolute beginners. For grad school, I suggest you take a look at his Stanford-Lectures instead. They are a bit older, but very high quality content-wise and on a very decent level.
That's too bad. I took that course when it first came out of Stanford before it became part of Coursera. It seems the videos of Andrew Ng's Stanford lectures are still available on YouTube. https://www.youtube.com/watch?v=jGwO\_UgTS7I His Stanford lectures go much deeper into the math behind the…
You can try the Machine learning course by Stanford University and after that, you can start with specialized fields. You can check this link (https://online.stanford.edu/programs/artificial-intelligence-graduate-program)
Learn as much math as you can. Linear algebra and multivariate calculus are required. The linear algebra course by Gilbert Strang on MIT OpenCourseWare is excellent. There's a calculus course on MIT OCW as well. Can find them here and here. As for machine learning books, the usually recommended…
Well...I think you should: 1. Go to Codecademy.com and take the HTML/CSS, Javascript Course, and PHP course. These will introduce you to web development, programming syntax and some universal concepts. (1 month) 2. Take CS50 on edx.org to build your resilience, resourcefulness and critical…
If by "the ML course by A. Ng" you mean the one on coursera, then: that is a class that is **heavily** focused on applying the stuff, as opposed to actually give a deep understanding of the algorithms. It gives you a short overview over all important topics, but always with a strong focus on…
I went through mlclass on their first offering, and looked through most of the Learning From Data lectures back when they were offered here. Ng's coursera class is very introductory. He skims over many topics, but never goes in deep. You can probably follow it without any college-level knowledge…
Two pieces of advice: * have more confidence, and present yourself with that confidence. When you write cover letters or try to network with people this will help you get past the first stages. You have a masters, which is a prerequisite for many of the jobs you want. This already puts you in a…
While these are some decent slides, slides are generally a difficult thing to learn from alone, as they were meant to be looked at along with a lecture. If you would like to watch a lecture, in addition to looking at slides, I recommend the stanford lectures on ML. Hope this helps.
Not very familiar. My plan was to go through the Stanford course on ML. The only issue is that is all run through in Python so it wouldn't be doing what the interviewers want me to go through. I'll have a look at the pattern recognition book.