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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