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
On Tue, Jun 6, 2017 at 8:59 PM, Ramkrishna P
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.
>
> I
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
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
On Tue, Jun 6, 2017 at 8:59 PM, Ramkrishna P 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
Hey Ramkrishna,
I have found the following book very useful.
- https://github.com/jakevdp/PythonDataScienceHandbook
Thank you,
Bhargav
On Tue, Jun 6, 2017 at 8:59 PM, Ramkrishna P
wrote:
> Hello Team,
> I have started out to work on pandas and numpy libraries to pick some
> machine learning co
