Re: [BangPypers] Resource for ML

[Arjunil Pathak]

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 with a black box approach. The next courses delve
deep into each example, be it Regression, or Classification - with generous
doses of math(some of it optional).
They use jupyter notebooks and have adapted to present solutions in
scikit-learn apart from the proprietary stuff they initially started off
with because one of the instructors was a founder of an AI and ML start-up
that they tried promoting  ( Maybe you've heard of Dato, now goes by the
name of Turi - acquired by Apple).

It should give you a good, firm grasp on the basics and enough to keep you
busy for a while.

On Wed, Jun 7, 2017 at 7:41 PM, Anand Chitipothu 
wrote:

> 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 you guys could suggest some resources, it will be of great help.
> >
>
> I find Learn Data Science very good place to start, esp. for the beginners.
>
> http://learnds.com/
>
> Anand
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-- 
Arjunil Pathak
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Re: [BangPypers] Resource for ML

[Anand Chitipothu]

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 you guys could suggest some resources, it will be of great help.
>

I find Learn Data Science very good place to start, esp. for the beginners.

http://learnds.com/

Anand
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Re: [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 :

> 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 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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Re: [BangPypers] Resource for ML

[Harsh Gupta]

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 
wrote:

> 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 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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>



-- 
Harsh
Sent from a GNU/Linux
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Re: [BangPypers] Resource for ML

[Bhargav Kowshik]

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 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.
>
>
> Regards,
> Ramkrishna.P
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>
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[BangPypers] Resource for ML

[Ramkrishna P]

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.


Regards,
Ramkrishna.P
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