Skip to content

mladenuzelac/DSEFP

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Data Science Engineering Fundamentals

The Open Source resources in Data Engineering, Cloud Computing, Machine Learning, Data Science and applied AI topics, inspired by Data Science Masters.

Table of contents:

Reference Books and Research Papers:

Books:

Docker for Data Science by Joshua Cook Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server

Data Wrangling with Python by Jacqueline Kazil, Katharine Jarmul

Introduction to Machine Learning with Python by Andreas C. Mueller , Sarah Guido

Python Machine Learning (2nd Ed.) Code Repository by Sebastian Raschka, Vahid Mirjalili

The Elements of Statistical Learning T.Hastie, R.Tibshirani, J.Friedman

Natural Language Processing with Python Analyzing Text with the Natural Language Toolkit by Steven Bird, Ewan Klein, and Edward Loper http://www.nltk.org/book/ http://www.nltk.org/book_1ed/

Deep Learning Book by Ian Goodfellow, Yoshua Bengio, Aaron Courville https://www.deeplearningbook.org/

Papers:

Paper by Tomas Mikolov Google http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf

ResourcesDSportals: https://medium.com/ https://towardsdatascience.com/machine-learning/home https://www.datasciencecentral.com/ https://www.kdnuggets.com/

EducationalResources: https://www.coursera.org/ https://eu.udacity.com/ https://www.datacamp.com/ https://cloudacademy.com https://www.edx.org/ http://www.deeplearningbook.org/

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published