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.

General information

Duration 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
Participants are expected to have a sound knowledge of Python (knowledge of Python syntax, data structures, control structures, working with libraries and files, creating functions).
It is recommended that participants take Python: Introduction to Natural Language Processing before taking this course.
Students and employees of the University of Zurich.
By the end of the course, participants should be able to:
  • 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.
Participants will perform live coding on their preferred notebook as they work through the content of each section.  At the end of each section, participants will complete one or two tasks to consolidate the content of the section.

Dates

Code Instructor Dates Available seats Venue
FS26-APML-01 Tsilimos Maria Mon 01 June 2026 (05:00pm - 07:00pm)
Mon 08 June 2026 (05:00pm - 07:00pm)
Mon 15 June 2026 (05:00pm - 07:00pm)
Mon 22 June 2026 (05:00pm - 07:00pm)
Mon 29 June 2026 (05:00pm - 07:00pm)
Mon 06 July 2026 (05:00pm - 07:00pm)
Online Course Course registration begins on 1 February for the spring semester and on 1 September for the autumn semester.