Python: Introduction to Machine Learning

Machine Learning (ML), a key subfield of Artificial Intelligence, is increasingly used across domains such as government, healthcare, education, marketing, business, and life sciences. By leveraging historical data, ML models can identify patterns and predict outcomes for new, unseen data.

In this practical, hands-on course, participants learn how to build their own ML models in Python and apply them to real-world problems. The course covers core ML concepts and a simplified data science workflow, including supervised, unsupervised, and basic reinforcement learning techniques. For their final project, participants will design and implement an end-to-end ML workflow using a provided dataset.

General information

Duration 12
  • Introduction to Machine Learning and Data Science workflows
  • Data selection, cleaning and preparing
  • Introduction to Supervised Learning (learning from labeled data)
    • Regression Models
    • Classification Models
    • Metrics & Evaluation
  • Introduction to Unsupervised Learning (No labels; discovering patterns)
    • Clustering Techniques
    • Dimensionality Reduction
  • Introduction to Reinforcement Learning
    • Basic RL Algorithms
    • Applications of RL
  • Capstone: End-to-End ML Workflow
Participants are expected to have a solid working knowledge of Python, including:
  • Python syntax and basic programming concepts
  • Data structures (lists, dictionaries, sets, tuples)
  • Control structures (conditionals, loops)
  • Working with libraries and files
  • Defining and using functions
It is recommended that participants complete the Python programming courses before attending this course.
Students and employees of the University of Zurich.
 
By the end of the course, participants will:
  • understand and apply the core concepts and techniques of supervised, unsupervised and reinforcement learning, including data preparation, model training and evaluation, using Python;
  • design and implement an end-to-end machine learning workflow, from data selection and preprocessing to modelling, evaluation and interpretation, culminating in a practical capstone project.
  • 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
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