Machine Learning in Practice
This course is aimed at practitioners, who want to analyze data with machine learning algorithms.
The four-day workshop follows a "flipped classroom" format, where the participants study the theory and solve the exercises at their own speed and the results are then discussed in the group sessions.
Learning Outcomes
- Understand the fundamentals of machine learning.
- Overview of the most important supervised learning algorithms and what needs to be considered when using them.
- First practical experience in the application of machine learning algorithms by solving a realistic case study.
- Basic knowledge of the central tools of the Python data science ecosystem (numpy, scipy, matplotlib, pandas, scikit-learn).
Prerequisites
- Good command of the English language (all materials are in English)
- Math skills at university entrance level (especially linear algebra: matrices & vectors, functions)
- Good programming skills (ideally using Python)
- Intrinsically motivated to learn new things
- Desirable: First practical experience analyzing data
- Technical requirements: Python installation on your own computer or access to a cloud environment, where you can run Jupyter notebooks (e.g., Google Colab)
Agenda
The course consists of multiple group sessions, where we discuss questions and results, and self-study parts in between, where you read through some chapters of the book, take notes in the workbook, test yourself with short quizzes, and work through the programming exercises. Your answers to the questions in the workbook are the basis for the group discussions.
The group sessions take place remotely via Microsoft Teams, Google Meet, Zoom, Slack, or similar. Please join the calls with your camera turned on so the sessions feel a bit more personal.
How to get the most out of this course
Think of the questions in the workbook as questions an interested colleague might ask you after the course. Following the Feynman Technique, try to explain what you've learned in your own words, which is the easiest way to identify any gaps in your knowledge.
Don't try to memorize any facts, but instead connect them with what you already know and make sure you understand the "why" behind the answers.
And if anything seems confusing, please make a note of it and ask in the next meeting!
Day 1: Intro to ML & Python
[9:00] Group Session (~30min):
- Introduction & Orga
Self-Study Block 1.1:
- Read the chapter: "The Basics"
- Take notes and answer the questions in the workbook [docx]
- Answer Quiz 1 (also available as a pdf in case you can't access Google Forms)
Self-Study Block 1.2:
- Read the chapter: "ML with Python"
- Complete the Python tutorial
[15:30] Group Session (~90min):
- Discuss workbook questions
Day 2: Supervised Learning
Self-Study Block 2.1:
- Read the chapter: "Data Analysis & Preprocessing" (you can skip the last subsection on "Working with Text Data")
Self-Study Block 2.2:
- Read the chapter: "Supervised Learning Basics"
- Answer Quiz 2 [pdf]
[13:00] Group Session (~90min):
- Discuss workbook questions
Self-Study Block 2.3:
- Read the chapter: "Supervised Learning Models"
- In parallel, work through the respective sections of the Notebook: supervised comparison
Day 3: Avoiding Common Pitfalls
[9:00] Group Session (~90min):
- Discuss workbook questions
Self-Study Block 3.1:
- Read the chapter: "Avoiding Common Pitfalls"
- Work through the Notebook: analyze toy dataset
[15:30] Group Session (~90min):
- Discuss workbook questions
Day 4: Case Study
[9:00] Group Session (~30min):
- Intro to case study
- Recommended: Determine groups for the work on the case study
Self-Study Block 4.1:
- Start with the case study in the Notebook: quality prediction
[13:00] Group Session (~60min):
- Discuss progress and questions about the case study
Self-Study Block 4.2:
- Finish the Notebook: quality prediction and prepare a presentation of your final results
[15:30] Group Session (~90min):
- Presentation of the case study results
- Closing remarks
Outlook
If you want to learn more:
- Read the chapters "Unsupervised Learning" and "Advanced Topics"
- Have a look at the remaining programming exercises
- Check out the section "Additional Resources"
- Do an online course, e.g., the Coursera Deep Learning Specialization (by Andrew Ng)
- Understand how to use ML in production with the book "Designing Machine Learning Systems"