I can vouch for Coursera's ML courses by University of Washington. It gives you a brief overview of the possibilities ML presents in the foundations course - predictive models using regression, document classification, recommender systems in the very first course - good for whetting your appetite with a black box approach. The next courses delve deep into each example, be it Regression, or Classification - with generous doses of math(some of it optional). They use jupyter notebooks and have adapted to present solutions in scikit-learn apart from the proprietary stuff they initially started off with because one of the instructors was a founder of an AI and ML start-up that they tried promoting ( Maybe you've heard of Dato, now goes by the name of Turi - acquired by Apple).
It should give you a good, firm grasp on the basics and enough to keep you busy for a while. On Wed, Jun 7, 2017 at 7:41 PM, Anand Chitipothu <anandol...@gmail.com> wrote: > On Tue, Jun 6, 2017 at 8:59 PM, Ramkrishna P <ramkrishna...@gmail.com> > wrote: > > > Hello Team, > > I have started out to work on pandas and numpy libraries to pick some > > machine learning concepts. > > I feel apart from working on datasets and getting some results, the > > core concepts of machine learning are still missing. > > > > If you guys could suggest some resources, it will be of great help. > > > > I find Learn Data Science very good place to start, esp. for the beginners. > > http://learnds.com/ > > Anand > _______________________________________________ > BangPypers mailing list > BangPypers@python.org > https://mail.python.org/mailman/listinfo/bangpypers > -- Arjunil Pathak _______________________________________________ BangPypers mailing list BangPypers@python.org https://mail.python.org/mailman/listinfo/bangpypers
