'Python' is a high-level, general-purpose programming language that emphasises human readability and minimum use of indentation, and is frequently used in Digital Humanities projects.
This lesson uses word embeddings and clustering algorithms in Python to identify groups of similar documents in a corpus of approximately 9,000 academic abstracts. It will teach you the basics of dimensionality reduction for extracting structure from a large corpus and how to evaluate your results.
Google Vision and Tesseract are both popular and powerful OCR tools, but they each have their weaknesses. In this lesson, you will learn how to combine the two to make the most of their individual strengths and achieve even more accurate OCR results.
In this lesson, you will use Qt Designer and Python to design and implement a simple graphical user interface and application to merge PDF files. This lesson also demonstrates how to package the application for distribution to other personal computers.
In this lesson, you will learn how to apply a Generative Pre-trained Transformer language model to a large-scale corpus so that you can locate broad themes and trends within written text.
This is the first of a two-part lesson introducing deep learning based computer vision methods for humanities research. Using a dataset of historical newspaper advertisements and the fastai Python library, the lesson walks through the pipeline of training a computer vision model to perform image classification.
This is the second of a two-part lesson introducing deep learning based computer vision methods for humanities research. This lesson digs deeper into the details of training a deep learning based computer vision model. It covers some challenges one may face due to the training data used and the importance of choosing an appropriate metric for your model. It presents some methods for evaluating the performance of a model.
This lesson is the second in a two-part lesson focusing on regression analysis. It provides an overview of logistic regression, how to use Python (Scikit-learn) to make a logistic regression model, and a discussion of interpreting the results of such analysis.
This course from dariahTeach introduces learners to the theoretical and practical foundations of an analysis of socio-cultural objects using Python through theoretical grounding and hands-on case studies. Students will work through several research use cases using basic machine learning, and employ network analysis to split a small community network into groups and clusters before finally learning more about visualisation and image analysis.
The aim of this virtual course is to offer basic knowledge and skills in programming in Python. Target audiences are undergraduate and graduate students in the Humanities and Social Sciences who want to acquire hands-on knowledge and skills in working with textual data or quantitative data in language and humanities research.
