Python: Introduction to Machine Learning for Text Classification
Machine Learning (ML), a subset of Artificial Intelligence, has a wide range of applications in various domains, including government, healthcare, education, marketing, business, and life sciences. By employing machine learning techniques, computers can learn from historical data and subsequently make predictions about new data.
This practical course aims to empower participants with the skills to build their own Machine Learning models for their own projects. Specifically, this course centers on Text Classification, teaching you to sort text into predefined categories. By mastering these core techniques, you develop a foundation that applies to advanced NLP applications, including machine translation, summarization, and information extraction.
Allgemeine Informationen
| Dauer | 12 hours |
|---|
- Introduction to Machine Learning
- Text representation techniques (CountVectorizer, TF-IDF)
- Supervised learning – classification tasks (Naïve Bayes, Random Forests)
- Evaluation metrics (confusion matrix, classification report)
- Tackle overfitting
- Run Grid Search for hyperparameter fine-tuning
It is recommended that participants take Python: Introduction to Natural Language Processing before taking this course.
- apply text representation techniques to their data.
- design the architecture of a Machine Learning model.
- train, optimize and evaluate a Machine Learning model.
- The course materials are going to be delivered throughout the course.
- The code snippets of each section will be delivered prior to each lesson.
Kursdaten
| Code | Leitung | Daten | Plätze frei | Standort | |
|---|---|---|---|---|---|
| FS26-APML-01 | Tsilimos Maria |
Mo. 01. Juni 2026
(17:00 Uhr - 19:00 Uhr)
Mo. 08. Juni 2026 (17:00 Uhr - 19:00 Uhr) Mo. 15. Juni 2026 (17:00 Uhr - 19:00 Uhr) Mo. 22. Juni 2026 (17:00 Uhr - 19:00 Uhr) Mo. 29. Juni 2026 (17:00 Uhr - 19:00 Uhr) Mo. 06. Juli 2026 (17:00 Uhr - 19:00 Uhr) |
Online-Kurs | Die Kursanmeldung beginnt am 1. Februar für das Frühjahrssemester und am 1. September für das Herbstsemester. |