dlab-berkeley / Python-Text-Analysis-Fundamentals

D-Lab's 9 hour introduction to text analysis with Python. Learn how to perform bag-of-words, sentiment analysis, topic modeling, word embeddings, and more, using scikit-learn, NLTK, gensim, and spaCy in Python.
Creative Commons Attribution 4.0 International
43 stars 43 forks source link

Minor updates to Readme #6

Open brooksjessup opened 3 years ago

brooksjessup commented 3 years ago

What you'll learn:

Resources:

EastBayEv commented 3 years ago

Hi @brooksjessup -- Can you commit and push these changes? Please close this comment when you are done. Let me know if you have any questions. Thanks!

pattyf commented 3 years ago

@J. Brooks Jessup brooks.jessup@berkeley.edu You can add edx classes https://www.edx.org/course/introducing-text-analytics-and-natural-language-processing-with-python too since, unlike coursera, they are free to uc berkeley people.

On Thu, Feb 11, 2021 at 1:53 PM Evan Muzzall notifications@github.com wrote:

Hi @brooksjessup https://github.com/brooksjessup -- Can you commit and push these changes? Please close this comment when you are done. Let me know if you have any questions. Thanks!

— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub https://github.com/dlab-berkeley/computational-text-analysis-spring-2019/issues/6#issuecomment-777816929, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAQGZYNTCHCGSDF3RJJDCETS6RGUTANCNFSM4VRTN5TQ .

-- Patty Frontiera, PhD Data Services Lead, Social Sciences Data Lab Co-Director, Berkeley Federal Statistical Research Data Center 356 Social Sciences Building, University of California Berkeley http://dlab.berkeley.edu