Python-Text-Analysis-Fundamentals
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dlab-berkeley
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 lda = LatentDirichletAllocation() change parameter `n_topics=` to `n_components=` Also, remove the deprecation warning.
Some of the big trends in NLP have been transformers and deep learning. Do we want to mention these in the introductory overview?
In the opening comments to this notebook, there is a reference to "our next two weeks". Depending on how the workshop is taught, this probably needs to be updated to...
Day 2
Day 2: 1) Some of the prose and introductory material might be better located in separate slides in rder to avoid long blocks of text 2) Reduce jargon in sections...
Day 3
Day 3: 1) Define “EDA” 2) Consider talking about regular expressions on day 1. They are mentioned several times in the preprocessing step 3) In the classification section, describe the...
Day 1
General comments: 1) Include some inline comments to help organize the code blocks 2) Include more scaffolding in the challenge questions Day 1: 1) Introduction slides are very clear and...
Intro to Python Machine Learning as a *recommended* prerequisite for Text Analysis Fundamentals? Preface this in the README and slides/intro materials - perhaps something like: "We strongly recommend you take...
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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...