I am catching up to all the replies, apologies for the delay. (replied in
reverse order)
@ Gaël,
Thanks for your comments. I actually started with 1) Data Camp courses and
2) Python for Data Science book.
Here's my review:
1) The course: it is fantastic! But they only give you a flavor of A FEW
t
And, answering your last question, a good way to learn Data science
using Python is, for I, "Python data science handbook" that you can read
as Jupyter notebooks:
https://github.com/jakevdp/PythonDataScienceHandbook
Le 20/06/2017 à 06:28, Gaël Pegliasco via scikit-learn a écrit :
Hi,
You may
Hi,
You may find these R/Python comparison-sheets useful in understanding
both languages syntaxes and concepts:
* https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis
* http://pandas.pydata.org/pandas-docs/stable/comparison_with_r.html
Gaël,
Le 18/06/2017 à 18:02, C
Point taken.
G
On Mon, Jun 19, 2017 at 05:29:34PM +1000, Joel Nothman wrote:
> There's a PR about handling missing values in RF, and a PR about imputing with
> more sophistication than a single, global feature-wise statistic, but nothing
> about RF imputation.
> On 19 June 2017 at 16:13, Gael Va
There's a PR about handling missing values in RF, and a PR about imputing
with more sophistication than a single, global feature-wise statistic, but
nothing about RF imputation.
On 19 June 2017 at 16:13, Gael Varoquaux
wrote:
> > I misspoke. I didn't mean that there is a reason not to support it
