Text Analysis and Machine Learning (TAML) Group

  • Welcome to TAML!

Python Basics

  • i. How to use this book
  • ii. Start coding!
  • iii. Boilerplate code review
  • iv. Numeric data wrangling
  • v. Visualization essentials

Winter Quarter 2022

  • Chapter 1 - English text preprocessing basics
  • Chapter 2 - Core machine learning concepts; building text vocabularies
  • Chapter 3 - Document encoding (TF-IDF), topic modeling, sentiment analysis, building text classifiers
  • Chapter 4 - The BERT algorithm
  • Chapter 5 - Ensemble machine learning, deep learning
  • Chapter 6 - Writing about numeric data
  • Chapter 7 - Combine the basics in powerful ways!

Miscellaneous

  • Resources
  • Launch JupyterLab in Binder
  • Guest Speakers
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Contents
  • Python programming
  • Data wrangling
  • Data visualization
  • Text analysis
  • Machine learning
  • Statistics and Machine Learning
  • Other
  • Download

Resources¶

Python programming¶

Free online Python books

W3 Schools list comprehensions

Data wrangling¶

10 minutes to pandas

pandas getting started tutorials

Pandas data wrangling cheatsheet

Data visualization¶

Overview of seaborn plotting functions

Fundamentals of Data Visualization

Text analysis¶

NLTK Book

spaCy 101

Lemmatization Approaches with Examples in Python

Regular expression operations

Regex Cheat Sheet

Machine learning¶

W3 Schools machine learning

scikit-learn tutorials

scikit-learn user guide

An Introduction to Statistical Learning - with applications in R

Bishop, 2006. Pattern Recognition and Machine Learning

Statistics and Machine Learning¶

Breiman, 2001. Statistical modeling: The two cultures

Srivastava, 2015. Difference between Machine Learning & Statistical Modeling

Welling, 2015. Are ML and statistics complimentary?

Other¶

Jupyter Book

Learn HTML

Download¶

Python Anaconda Distribution 3.9


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