Introduction

Last updated: July 16, 2023

My Personal Python Notes!

This notebook collects my personal Python notes for linguistics. These notes are based on many different sources. Please note that these notes may have not been organized in a very systematic way. This notebook is being updated on a regular basis. Most of the materials here are for educational purposes only.

Table of Contents

There are four main sections in Python Notes for Linguistics:

Python Basics

This section covers the fundamental concepts of the Python language.

Corpus Linguistics with Python

This section covers the corpus processing skills and techniques using Python.

Statistical Analyses

This section covers statistical analyses using Python.

NLP with Python

This section covers computational text analytics using Python.

Useful Resources

There are many resources for Python-learning. Here I keep track of a few useful sources of inspirations for my Python journey.

Books

The materials collected here were based on several useful reference books, which are listed as follows in terms of three categories:

• Python Basics [1]

• Data Analysis with Python [2]

• Natural Language Processing with Python [5, 3, 4, 6, 7]

• Deep Learning [8]

Online Resources

In addition to books, there are many wonderful on-line resources, esp. professional blogs, providing useful tutorials and intuitive understanding of many complex ideas in NLP and AI development. Among them, here is a list of my favorites:

• Toward Data Science

• LeeMeng

• Dipanzan Sarkar’s articles

• Python Graph Libraries

• KGPTalkie NLP

• Jason Brownlee’s Blog: Machine Learning Mastery

• Jay Alammar’s Blog

• Chris McCormich’s Blog

• GLUE: General Language Understanding Evaluation Benchmark

• SuperGLUE

YouTube Channels

• Chris McCormick AI

• Corey Schafer

• Edureka!

• Deeplearning.ai

• Free Code Camp

• Giant Neural Network

• Tech with Tim

• PyData

• Python Programmer

• Keith Galli

• Data Science Dojo

• Calculus: Single Variable by Professor Robert Ghrist

References

1

Paul Gerrard. Lean Python: Learn Just Enough Python to Build Useful Tools. Apress, 2016.

2

Wes McKinney. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. " O'Reilly Media, Inc.", 2012.

3

Dipanjan Sarkar. Text analytics with Python: a practitioner's guide to natural language processing. Apress, 2019.

4

Steven Bird, Ewan Klein, and Edward Loper. Natural language processing with Python: analyzing text with the natural language toolkit. " O'Reilly Media, Inc.", 2009.

5

Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana. Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems. O'Reilly Media, 2020.

6

Jacob Perkins. Python 3 text processing with NLTK 3 cookbook. Packt Publishing Ltd, 2014.

7

Bhargav Srinivasa-Desikan. Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras. Packt Publishing Ltd, 2018.

8

Chollet Francois. Deep learning with Python. Manning Publications Company, 2017.