Source code and notebooks for my OReilly Live Course about automating tasks with AI tools and Python.

https://learning.oreilly.com/live-training/courses/python-the-next-level/0636920282044/

Exercises for Jessica McKellar's Introduction to Python and Intermediate Python courses [O'Reilly]

https://learning.oreilly.com/live-training/courses/test-driven-development-in-python/0636920295891/

Basically about Python, ML, DL & NLP, resources including Manning, Oreilly Publications, Stanford, Oxford & NJU Courses

https://learning.oreilly.com/live-training/courses/pythonic-design-patterns/0636920296348/

https://learning.oreilly.com/live-training/courses/scaling-python-with-generators/0636920295761/

https://learning.oreilly.com/live-training/courses/beyond-python-scripts-logging-modules-and-dependency-management/0636920296386/

python crush course oreilly

oreilly live course - python

Oreilly Fullstack ReactJS/Python Course

Python DevOps Course on Oreilly

Providing code solution to Oreilly Python Crash Course

Useful python tips and tricks from oreilly course

examples from https://learning.oreilly.com/library/view/python-crash-course/9781098156664/c06.xhtml#h1-502703c06-0004

No description available

code from David's Beazley python course on oreilly

No description available

https://learning.oreilly.com/library/view/python-crash-course

https://learning.oreilly.com/live-training/courses/fraud-analytics-using-python/0636920361213/

Course of Test Driven Development with Python - http://chimera.labs.oreilly.com/books/1234000000754

This is a repo for Ruthless web scraping course on Oreilly created by Noah Gift. It contains basics of web scraping using Python and Beautiful Soup

Python game devleopment course - https://learning.oreilly.com/videos/the-art-of/9781803231587/9781803231587-video1_1/

Based on Daniel Y. Chen's course 'Pandas Data Analysis with Python Fundamentals' (https://learning.oreilly.com/videos/pandas-data-analysis/9780134692272/9780134692272-PAND_04_03) we will recreate the plots shown in this course using seaborn. The tips data set was copied from https://www.kaggle.com/ranjeetjain3/seaborn-tips-dataset and the anscombe data set was downloaded from https://gist.github.com/ericbusboom/b2ac1d366c005cd2ed8c.