Python Tutorials for NLP, ML, AI

(C) 2016-2024 by Damir Cavar

NLP-Lab at Indiana University.

Notebooks

• Anthropic / VoyageAI Embeddings
• OpenAI Embeddings
• BERT Embeddings
• Claude 3 Interaction using the Anthropic API
• GPT-4 interaction using the OpenAI API
• Neo4j interaction
• Simple Transformer-based Text Classification
• Stanza Tutorial
• Converting SEC CIKs to a Knowledge Graph
• Allegro Graph example
• Extracting Abbreviations
• Bayesian Classification for Machine Learning for Computational Linguistics
• Python Tutorial 1: Part-of-Speech Tagging 1
• Lexical Clustering
• Linear Algebra
• Neural Network Example with Keras
• Computing Finite State Automata
• Parallel Processing on Multiple Threads
• Perceptron Learning in Python
• Clustering with Scikit-learn
• Simple Language ID with N-grams
• Support Vector Machine (SVM) Classifier Example
• Scikit-Learn for Computational Linguists
• Tutorial: Tokens and N-grams
• Tutorial 1: Part-of-Speech Tagging 1
• Tutorial 2: Hidden Markov Models
• Word Sense Disambiguation
• Python examples and notes for Machine Learning for Computational Linguistics
• RDFlib Graphs
• Scikit-learn Logistic Regression
• Convert the Stanford Sentiment Treebank Data to CSV
• TextRank Example

NLTK Notebooks

• NLTK: Texts and Frequencies - N-gram models and frequency profiles
• Parsing with NLTK
• Parsing with NLTK and Foma
• Categorial Grammar Parsing in NLTK
• Dependency Grammar in NLTK
• Document Classification Tutorial 1 - Amazon Reviews
• WordNet using NLTK
• WordNet and NLTK
• Framenet in NLTK
• FrameNet Examples using NLTK
• PropBank in NLTK
• Machine Translation in Python 3 with NLTK
• N-gram Models from Text for Language Models
• Probabilistic Context-free Grammar (PCFG) Parsing using NLTK
• Python for Text Similarities 1

spaCy Notebooks

• spaCy Tutorial
• spaCy 3.x Tutorial: Transformers Spanish
• spaCy Model from CoNLL Data
• Train spaCy Model for Marathi (mr)
• Linear Algebra and Embeddings - spaCy

See the licensing details on the individual documents and in the LICENSE file in the code folder.

Introduction

The files in this folder are Jupyter-based tutorials for NLP, ML, AI in Python for classes I teach in Computational Linguistics, Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI) at Indiana University.

If you find this material useful, please cite the author and source (that is Damir Cavar and all the sources cited in the relevant notebooks). Please let me know if you have some suggestions on how to correct the notebooks, improve them, or add some material and explanations.

The instructions below are somewhat outdated. I use just Jupyter-Lab now. Follow the instructions here to set it up on different machine types and operating systems.

To run this material in Jupyter you need to have Python 3.x and Jupyter installed. You can save yourself some trouble by using the Anaconda Python 3.x distribution.

Clone the project folder using:

git clone https://github.com/dcavar/python-tutorial-for-ipython.git

Some of the notebooks may contain code that requires various kinds of [Python] modules to be installed in specific versions. Some of the installations might be complicated and problematic. I am working on a more detailed description of installation procedures and dependencies for each notebook. Stay tuned, this is coming soon.

Installing Jupyter

Jupyter is a great tool for computational publications, tutorials, and exercises. I set up my favorite components for Jupyter on Linux (for example Ubuntu) this way:

Assuming that I have some of the development tools installed, as for example gcc, make, etc., I install the packages python3-pip and python3-dev:

sudo apt install python3-pip python3-dev

After that I update the global system version of pip to the newest version:

sudo -H pip3 install -U pip

Then I install the newest Jupyter and Jupyterlab modules globally, updating any previously installed version:

sudo -H pip3 install -U jupyter jupyterlab

The module that we should not forget is plotly:

sudo -H pip3 install -U plotly

Scala, Clojure, and Groovy are extremely interesting languages as well, and I love working with Apache Spark, thus I install BeakerX as well. This requires two other [Python] modules: py4j and pandas. This presupposes that there is an existing Java JDK version 8 or newer already installed on the system. I install all the BeakerX related packages:

sudo -H pip3 install -U py4j
sudo -H pip3 install -U pandas
sudo -H pip3 install -U beakerx

To configure and install all BeakerX components I run:

sudo -H beakerx install

Some of the components I like to use require Node.js. On Ubuntu I usually add the newest Node.js as a PPA and not via Ubuntu Snap. Some instructions how to achieve that can be found here. To install Node.js on Ubuntu simply run:

sudo apt install nodejs

The following commands will add plugins and extensions to Jupyter globally:

sudo -H jupyter labextension install @jupyter-widgets/jupyterlab-manager
sudo -H jupyter labextension install @jupyterlab/plotly-extension
sudo -H jupyter labextension install beakerx-jupyterlab

Another useful package is Voilà, which allows you to turn Jupyter notebooks into standalone web applications. I install it using:

sudo -H pip3 install voila

Now the initial version of the platform is ready to go.

To start the Jupyter notebook viewer/editor on your local machine change into the notebooks folder within the cloned project folder and run the following command:

jupyter notebook

A browser window should open up that allows you full access to the notebooks.

Alternatively, check out the instructions how to launch JupyterLab, BeakerX, etc.

Enjoy!

Damir