[BangPypers] Resource for ML

[Propadovic Nenad]

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.upadhyay at gmail.com>:

> On Tue, Jun 6, 2017 at 8:59 PM, Ramkrishna P <ramkrishna001 at 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
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