4 ms·
Ask HN: Good resources for learning about ML/AI?
Hey HN.
I've recently started learning about Machine Learning from Stanford's CS229 lecture videos and I was wondering if people with more experience could point me to (preferably free) supplementary resources for the same?
- glamp 14y agoBerkley Course: http://alex.smola.org/teaching/berkeley2012/statistics.html http://alex.smola.org/teaching/berkeley2012/statistics.html Scikit-Learn Docs: http://scikit-learn.org/dev/ http://scikit-learn.org/dev/ Google Research: http://research.google.com/pubs/papers.html http://research.google.com/pubs/papers.html Caltech Course: http://work.caltech.edu/telecourse.html http://work.caltech.edu/telecourse.html Basics in R: http://www.statmethods.net/advstats/glm.html http://www.statmethods.net/advstats/glm.html R packages for data science: http://blog.yhathq.com/posts/10-R-packages-I-wish-I-knew-about-earlier.html http://blog.yhathq.com/posts/10-R-packages-I-wish-I-knew-abo...
- m_ke 14y agohttp://videolectures.net/Top/Computer_Science/ http://videolectures.net/Top/Computer_Science/ has a ton of lectures and conference talks from the top researchers in ML and AI
- jclos 14y agoJenn Vaughan's class notes at http://www.cs.ucla.edu/~jenn/courses/F11.html http://www.cs.ucla.edu/~jenn/courses/F11.html are really good in my opinion.
- r41nbowcrash 14y agoWeka: http://www.cs.waikato.ac.nz/ml/weka/ http://www.cs.waikato.ac.nz/ml/weka/ Lets you test dozens of different algorithms on a given dataset, to see what works and what not, in like one evening. Also success stories from Kaggle competition: http://blog.kaggle.com/category/dojo/ http://blog.kaggle.com/category/dojo/