- Home
- Participant Homepage
- Course Offers
- Current Course Programme
- Operating Systems & Programming
- Digitale Selbstverteidigung: Einführung in die IT-Sicherheit für Anwender:innen
- Linux: Bash Workshop (TheAlternative.ch)
- Linux: Introduction to Open Source Software (TheAlternative.ch)
- Python: Machine Learning for Beginners
- Git: Continuous Integration und Deployment in GitLab@UZH
- Python: Basics
- Python: Automatisierung, Web-Scraping, Bildbearbeitung
- Python: Intermediate
- Microsoft Power Automate: Digitalisierung erster Prozesse
- Science IT: Linux Command Line
- Image Editing, Illustration and Presentation
- Collaboration, Social Media and Webpublishing
- CMS: Create accessible websites
- Social Media and Science Communication
- Images for your Website
- CMS Introduction Magnolia
- Creating and Publishing Web Pages
- Basic Introduction to JavaScript
- UZH365: Create an Intranet with SharePoint
- UZH365: Basics of collaboration in the cloud
- UZH365: Effective communication with teams Telephony
- UZH365: Microsoft Outlook (im Web) Grundlagen
- Microsoft Planner: Task management with Kanban
- UZH365: Outlook Desktop Productivity Training
- UZH365: SharePoint Basics
- TOPdesk: Hands-on Essentials
- Data Science
- QGIS: Spatial data analysis and map creation
- Python: Introduction to Natural Language Processing (NLP)
- Python: For the Digital Humanities
- Einführungskurs in das Statistikpaket SPSS
- Introduction to Programming with MATLAB
- Qualitative Datenanalyse mit MAXQDA
- Python: Data Analysis Essentials
- PowerBI Data Analysis and Visualisation
- R: Basic Introduction
- R: Crash Course in Statistics using R
- R: Reporting using Quarto & R Markdown
- R: tidyverse for Data Science
- Databases, Spreadsheet
- E-Learning & Examination
- Scientific Computing
- Text Processing & Publishing
- Knowledge Management
- UZH365: Digital Communication and Collaboration
- IT Courses from other Organisational Units of the UZH
Python: Introduction to Natural Language Processing (NLP)
As a universal programming language Python is used in a huge variety of application domains and is often used in data analysis tasks. For the analysis of textual data and especially in the interdisciplinary field of Natural Language Processing (NLP), Python is a very powerful tool.
NLP lies at the intersection of computational linguistics and artificial intelligence. It is an increasingly used domain as NLP enables computers to understand human languages and retrieve meaning from their analysis. Applications of NLP can be found in Machine Translation, Sentiment Analysis, Chatbots, Intelligent Systems, Spell Checking, Predictive Typing, Grammar correction etc.
General information
| Duration | 9 hours |
|---|
- Writing and running Python in iPython/Anaconda
- Tokenization
- NLTK corpora
- Noise removal (digits, hyperlinks, contractions, punctuation marks, special characters, emoticons, whitespaces, spelling errors)
- Text Normalization (stop words, lower case, stemming, lemmatization)
- Information extraction (POS tagging, chunking, n-grams, named entities)
- TF-IDF (with scikit-learn)
- Semantic and sentiment analysis (lexical relations, synsets, semantic similarity)
APPB - Python Basics or equivalent knowledge is required. You should feel comfortable working with control structures, simple functions and different data types in Python.
This introductory course is directed for beginners and is suitable for anyone who wishes to analyze text in Python and gain a basic understanding of Natural Language Processing (NLP).
By the end of the introductory course, students will be able to
- work with different file types in Python.
- apply text pre-processing techniques for cleaning and preparing textual data.
- extract information from textual data.
- perform semantic and sentiment analysis.
Course materials will be provided by the lecturer. Useful background material to deepen your self-paces studies might be found here:
- https://www.youtube.com/watch?v=gRk53jBPYvE
- https://www.datacamp.com/community/tutorials/text-analytics-beginners-nltk
- https://towardsdatascience.com/text-preprocessing-in-natural-language-processing-using-python-6113ff5decd8
- https://towardsdatascience.com/nlp-text-preprocessing-a-practical-guide-and-template-d80874676e79
In this introductory course, students will explore the basics of text analytics and NLP with the powerful Python package Natural Language Toolkit (NLTK) and in parts with scikit-learn. The course content is disseminated over 9 hours through slides, live coding of the instructor and in-class exercises in individual & pair work.
Dates
| Code | Referents | Dates | Available seats | Place | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| There are currently no open courses | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||