Lecturer: Augustine Farinola
The course encompasses five comprehensive modules, carefully crafted to cater to the diverse research needs of participants, considering their disciplines, research subjects, and preliminary computational backgrounds.
- Word_Cloud_from_Corpus.ipynb
- Dependency_Parsing_.ipynb
- Language_Detection_and_Identification.ipynb
- Machine_Translation.ipynb
- Sentiment_Analysis.ipynb
- Text_Classification.ipynb
- Text_Cleaning.ipynb
- Text_Clustering.ipynb
- Text_Generation.ipynb
- Text_Summarization.ipynb
- Topic_Modeling.ipynb
- Coreference_Resolution.ipynb
- Word_Embeddings.ipynb
- Named_Entity_Recognition.ipynb
- POS_Tagging_.ipynb
- SVO_Analysis.ipynb
- Understanding DH Data
- Linux Command and Git Control
- python_basics_1.ipynb
- python_advanced_2.ipynb
- python_conditional_questions.ipynb
- python_practice_questions_Loop.ipynb
- Voyant Tools
- Web Scraping
- Scripting: HTML, CSS,TEI, and XML
- Nvivo
- Docanno
- QGis
- Open Refine
- Tableau
- Recogito
- Web Archiving: WordPress & Drupal
- Basic Introduction to DH and NLP
- Introduction_to_Python.ipynb
- NLP_Techniques.ipynb
- Voyant Tools for Textual Interpretation
- Machine Translation (e.g., Google Translate)
- Sentiment Analysis (e.g., determining sentiment of text)
- Chatbots (e.g., ChatGPT, Bard, Claude)
- Speech Recognition (e.g., Siri, Alexa)
- Information Retrieval (e.g., search engines)
- Word Embeddings: Neural networks (Word2Vec, FastText)
- Language Modeling: RNNs, LSTM, Transformers (GPT, BERT)
- Sentiment Analysis: SVMs, Naive Bayes, Neural Networks, Deep Learning
- Named Entity Recognition: CRFs, RNNs, Transformers
- Machine Translation: Seq-to-seq models using RNNs, Transformer architecture (BERT, GPT)
- Speech Recognition: DNNs, CNNs for audio features, RNNs for sequencing
- Coreference Resolution: Decision Trees, Clustering, Deep Learning
- Text Summarization: Seq-to-seq models, Attention mechanisms, Transformers
- Topic Modeling: LDA, NMF
- Using Wole Soyinka Corpus: Corpus_of_African_Literature.ipynb
- Using Amos Tutuola's Corpus: Word_Frequency_Trend.ipynb