[BangPypers] Resource for ML

[Abhinav Upadhyay]

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