[Python Glasgow] Talks and Speakers for the 14th

[Dougal Matthews]

Hi all,

I have now confirmed the speakers and talks for next month. You can
grab tickets here:
https://python-glasgow-talks-june-2016.eventbrite.co.uk/


Talk 1: Iteration, Iteration, Iteration by John Sutherland

There should be something for everyone in this whistle–stop tour of iteration
in Python setting off from for–loops, and riding cross–country to multiplexing
coroutines! See and hear the amazing sights and sounds of list comprehensions,
and generators. Take in the amazing vistas from itertools, and be amazed at
the magnificent yield! We’ll take detours to higher–order functions, closures,
and decorators. And cover the FP inspired builtins map, filter, and reduce, as
well as the epitome of Pythonic programming, enumerate.


Talk 2: A tour of Python for Machine Learning by Martina Pugliese

Machine Learning is living a golden moment these days in terms of the problems
it is helping people to solve by either recognising hidden patterns in data or
automatising tasks which would be expensive to perform manually. The number of
available tools and libraries for analysing and creating software with data is
increasing at a fast rate due to the explosion of this field and more and more
industries are starting to extract knowledge out of their datasets. This talk
will propose a path through the Python toolkit for data processing and Machine
Learning, from the scientific data manipulation libraries Numpy/Pandas/Scipy
to the collection of algorithms and validation techniques in Scikit­learn,
without forgetting data visualisation with Matplotlib/Seaborn/Bokeh. The whole
journey will be conducted via examples for every bit, the purpose being
showing the capabilities and potential of the software and its ease of use.
Everything will be interactively displayed in Jupyter notebooks. Python is a
particularly friendly language for Data Science as it allows the user to
concentrate on implementing and validating the data models rather than
spending time on the code. The API’s are standardised in a way that everything
is consistent across the libraries and they can communicate easily so the data
scientist can quickly get a model up and running by crunching the data,
testing hypotheses, performing statistical analyses. Finally, the talk will
demonstrate how to scale ML in a distributed fashion by using PySpark, the
Python wrappers for Spark.



They should be great! Ticket's are moving fast, so grab one soon if you want
to come along.

Cheers,

Dougal