The document discusses the use of parallelism in machine learning with Python, highlighting various libraries and frameworks such as NumPy, Pandas, Scikit-Learn, and Dask. It provides links to resources for further learning and development in this domain. The focus is on enhancing computational efficiency through techniques like delayed futures and distributed computing.

1. Parallelism for Machine Learning
with Python
Matheus Pereira
@matheper

2. https://devgrid.co.uk/

6. http://www.numpy.org
https://pandas.pydata.org
https://scikit-learn.org/stable/
https://dask.org/
https://github.com/dask

7. Why

10. What is

13. http://jupyter.org/

15. Delayed

26. Futures

33. Distributed

36. Matheus Pereira
@matheper