Machine Learning Resources

ML_resources.md

General

http://www.stanford.edu/class/cs276/

http://cs224d.stanford.edu/syllabus.html

http://www.erogol.com/large-set-machine-learning-resources-beginners-mavens/

https://m2dsupsdlclass.github.io/lectures-labs/

https://medium.freecodecamp.org/every-single-machine-learning-course-on-the-internet-ranked-by-your-reviews-3c4a7b8026c0

http://datasciencemasters.org/

Model-based machine-learning http://www.mbmlbook.com/toc.html

System to extract value from dark data: http://deepdive.stanford.edu/

http://alex.smola.org/teaching/cmu2013-10-701x/index.html

https://github.com/edobashira/speech-language-processing

http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms

List of university courses for learning Computer Science

http://www.boozallen.com/media/file/The-Field-Guide-to-Data-Science.pdf

http://p.migdal.pl/2017/04/30/teaching-deep-learning.html

http://www.quora.com/Text-Analytics/What-are-some-of-the-most-important-papers-in-text-mining

http://blog.revolutionanalytics.com/2013/04/coursera-data-analysis-course-videos.html

http://www.kaggle.com/wiki/Tutorials

http://statsmodels.sourceforge.net/stable/gettingstarted.html#model-fit-and-summary

http://work.caltech.edu/telecourse.html

http://hortonworks.com/blog/big-data-needs-data-science/#.Uj2FF2Sc714

https://www.otexts.org/fpp/

http://www.alteryx.com/predictive-analytics

http://strata.oreilly.com/2013/11/data-wrangling-gets-a-fresh-look.html

https://github.com/johnmyleswhite/MLNotes

http://www.kaggle.com/wiki/BooksAndCourses

http://work.caltech.edu/library/

http://guidetodatamining.com/

http://engineeringblog.yelp.com/2013/12/cool-new-space-cool-new-tech.html

Weka

https://github.com/datasciencemasters/go/ (list of other resources)

http://strata.oreilly.com/2014/01/ipython-a-unified-environment-for-interactive-data-analysis.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+oreilly%2Fstrata+%28O%27Reilly+Strata%29

https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks

http://www.loria.fr/~rougier/teaching/numpy.100/index.html

Foundations of Data Science1 - John Hopcroft, Ravindran Kannan

a-practical-intro-to-data-science

http://pyvideo.org/video/2561/exploring-machine-learning-with-scikit-learn

http://christonard.com/12-free-data-mining-books/

http://datascienceatthecommandline.com/

http://homepages.inf.ed.ac.uk/vlavrenk/iaml.html

http://www-nlp.stanford.edu/IR-book/

https://itunes.apple.com/au/course/machine-learning/id515364596?ign-mpt=uo%3D2

Common mistakes

Text classification

https://neptune.ai/blog/text-classification-tips-and-tricks-kaggle-competitions