I collected some ML resources for inter hostel data analytic competition here https://github.com/Azad-Hall/data-analytics
Other the Andrew ng's course, Caltech's "Learning from Data" ( http://work.caltech.edu/telecourse.html) course is really good for the theoretical foundations of ML> On 6 June 2017 at 21:14, Abhinav Upadhyay <er.abhinav.upadh...@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. > > 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 > -- Harsh Sent from a GNU/Linux _______________________________________________ BangPypers mailing list BangPypers@python.org https://mail.python.org/mailman/listinfo/bangpypers
