All Twitter examples are now updated to the Twitter V2 APIs.
Downloads for my O'Reilly Online Learning live training course Python Data Science Full Throttle: Introductory Artificial Intelligence (AI), Big Data and Cloud Case Studies
These are the commands I used to set up a fresh environment. They create a custom environment named pydsft — you can choose any name you like:
- Create the custom environment
conda create --name pydsft
- Activate the environment you just created
conda activate pydsft
- The following two commands install most of what you need for the content from ch11, ch12, ch13, ch14, ch15 and a good chunk of ch16—if you’re working from our books or videos, you might need to subsequently install a few other packages. NOTE: You might want to install one package at a time from the following command as it can take a significant amount of time to resolve all the interdependencies if you do it all at once.
conda install nodejs jupyterlab ipympl wordcloud spacy tweepy geopy folium scikit-learn tensorflow matplotlib seaborn pymongo dnspython imageio pyaudio pydub
pip install -U dweepy pubnub ibm-watson tweet-preprocessor textblob deep_translator
The pip command is needed because a few packages are not available through the conda repository. If any package install fails with conda on Windows, use pip to install it.
February 28, 2022: https://learning.oreilly.com/live-events/python-data-science-full-throttle-with-paul-deitel-introductory-ai-big-data-and-cloud-case-studies/0636920289197/0636920066757/
I'll keep this repository up-to-date with any changes I make for future presentations.
These files are for your personal use and may not be redistributed or reposted.
If you have any questions, open an issue in the Issues tab or email us: deitel at deitel dot com.
Copyright 2019 by Deitel & Associates, Inc. and Pearson Education, Inc. All Rights Reserved.
The notebooks contain links to my videos and book on O'Reilly Online Learning where I discuss setup issues.
The content of this course is based on our book Python for Programmers, which is a subset of our book Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud.
The authors and publisher of this book have used their best efforts in preparing this book. These efforts include the development, research, and testing of the theories and programs to determine their effectiveness. The authors and publisher make no warranty of any kind, expressed or implied, with regard to these programs or to the documentation contained in this book. The authors and publisher shall not be liable in any event for incidental or consequential damages in connection with, or arising out of, the furnishing, performance, or use of these programs.