Machine Learning with Python
virtual course

Übersicht

Overview
Datum: Date 03 November 2022 (1:00pm - 5:15pm)
10 November 2022 (1:00pm - 5:15pm)
17 November 2022 (1:00pm - 5:15pm)
24 November 2022 (1:00pm - 5:15pm)
Kurssprache / Language English
Leistungspunkte / Credit Points 0,5 (20 AE)
Anmelde­schluss: Registration Deadline 31. Oktober 2022
Diese Veranstaltung wurde abgesagt. This event has been cancelled.

Course description:


Machine learning can be used to derive knowledge from data sets. For this purpose, algorithms allow to recognize patterns in data. In this course, we will learn about the workflow and basics in a Machine Learning project. Further, different algorithms will be introduced. We will apply this knowledge in practice by working on real-world use cases with the programming language Python.

This course includes the option for individual consultations before and after the course to discuss your own Machine Learning tasks.

 

Recommendation / Prerequisite:

  • Basic Python with focus on pandas and seaborn  
  • Jupyter Notebook
  • Course for Data Analysis and Visualization is recommended if your are new to python
Content:

 Part 1: Workshop

  • Introdutcion to Machine Learning
  • Workflow in an ML project
  • Data prepration for ML
  • Focus on supervised learning algorithms (distance based, tree-based algorithms, neural networks etc.)
  • Dimension reduction (such as feature selection)
  • Hyperparameter optimization

I Implementation in Python (Focus: Machine Learning-Library scikit-learn)

 Structure/Methods:


  • Theoretical Introduction
  • Live Coding (screen-sharing: participants follow on their own computer)
  • Practical training session (participants work in smal groups for excersises)

 

In addition, individual advisory service is offered 

 

 Part 2: individual consultation

  • Before the course Participants present their usecases and data, possibilities will be discussed, case may be included into the course (optional)
  •  After the course: questions beyond the course can be discussed with focus on individual ML tasks
Learning Outcome:

  • know the basic procedures in a machine learning project
  • be able to implement ML projects yourself
Please note, that we can only give certificates to thos who participated at least in 80% of the course time.
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Your contact person for this course is

Dr. Theresa Wand, 03731-393366, theresa [dot] wandatgrafa [dot] tu-freiberg [dot] de