Hello, while not having finished Andrew Ng's coursera course (yet), I started it and like it, too. I don't think it's an disadvantage that it's Matlab (or it's open source counterpart, Octave) - based (and I'm much more proficient in Python than in Matlab). Thanks to Abhinav and Harsh for the other recommendations. Cheers, Nenad
2017-06-06 17:44 GMT+02:00 Abhinav Upadhyay <er.abhinav.upadh...@gmail.com>: > 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. > > Andrew Ng's coursera course is probably the best place to start, he > covers a broad range of models which are commonly used and builds > mathematical intuitions for each of them (without bogging you down > with proofs, which have their place but not at this stage). Although, > all the programming exercises in the course use GNU Octave or Matlab. > > For a slightly more in depth coverage, you may consider the University > of Washington's specialization on ML (available on Coursera). It is a > set of 4 courses. The first course is just dedicated to regression, > while the second one just covers classification models. So every > course is able to go into more details than Ng's course. As a bonus, > all the exercises in the courses use Python. > > For a more statistics oriented introduction there is a course on > Stanford Online from Trevor Hastie and Rob Tibshirani based on their > book Introduction to Statistical Learning. All the exercises use R. > > PS: All the courses can be easily found with the help of Google, I > didn't have the links handy. > > - > Abhinav > _______________________________________________ > BangPypers mailing list > BangPypers@python.org > https://mail.python.org/mailman/listinfo/bangpypers > _______________________________________________ BangPypers mailing list BangPypers@python.org https://mail.python.org/mailman/listinfo/bangpypers
