Medical information
Regarding the medical aspects, the rare diseases that will be the focus of Rarehacks is pediatric melanoma, which is a kind of skin cancer. Here you will find an introductory description along with some Q&A. http://www.danafarberbostonchildrens.org/conditions/solid-tumors/pediatric_melanoma.aspx
Python Language
Python is the most
used programming language nowadays, and that is even more important
in the artificial intelligence area. Here you can find some useful
links on Python, including the official Python site and some tutorial
sites, including an interactive
tutorial:
https://www.python.org/
https://docs.python.org/3/tutorial/
https://www.learnpython.org/
ML with Python
Python has an
outstanding machine learning library with tens of methods for
different tasks. It is called Scikit-learn (sklearn for friends), and
the official website contains lots of code examples and reference
documentation:
https://scikit-learn.org/stable/
On
the other hand, as one of the main task at Rarehacks will be to
develop an intelligent chatbot, specialized text processing will be
needed. Python also has an excellent library of natural language
processing called NLTK:
https://www.nltk.org/
R Language
R is a
powerful language oriented towards data analysis and statistics. It
features a wide ecosystem of specialized packages that allow to
easily use most of the data analysis tools available.
Here you
have the official R site, the R-Studio site (a convenient environment
for R), as well as a tutorial
site:
https://www.r-project.org/
https://www.rstudio.com/
https://www.statmethods.net/r-tutorial/index.html
ML with R
R philosophy is a bit different from Python’s, as they favor smaller, more specialized packages as compared with sklearn and NLTK. Some interesting packages for machine learning in R include Kernlab and Caret:
https://cran.r-project.org/web/packages/kernlab/
https://cran.r-project.org/web/packages/caret/
As well as package NLP for natural language processing:
https://cran.r-project.org/web/packages/NLP/
There is also a very interesting free online book that teaches how to analyze data in R, you can read it here:
https://r4ds.had.co.nz/