How to use this course
This is the machine learning course that first-year students of Applied Data Science & AI at Breda University of Applied Sciences take, adapted for independent learning. It is free, with no registration, no deadlines and no certificate. You decide how far you go.
The course has three parts (see the overview):
- Self-study: notebooks and reading guides that teach the concepts;
- DataLab: a client project in three iterations, where you apply them to real molecules;
- Assessment: a Kaggle competition that scores your predictions on molecules you haven’t seen.
Choose your path
Just reading
Read the pages and notebooks here on the website. Nothing to install. The notebooks show the code and, in the first theme, its output. Start with What is Machine Learning?, which you can try out in your browser. You will understand the ideas, but you only really learn machine learning by writing the code yourself.
A quick look in Google Colab
Every notebook page has an Open in Colab button that runs the notebook in your browser. Good for trying a few notebooks. For the whole course, Colab is not practical: it forgets the files you create, and the DataLab notebooks build on each other’s files.
The full course (recommended)
Set up your computer once (about 15 minutes), then follow the 8-week plan below: Self-study and DataLab side by side, every exercise in the notebooks, and at the end your own model on the Kaggle leaderboard.
You already know machine learning
Go to the project. Read the Business Understanding and Data Understanding pages for the problem and the data, skim the Iteration 2 notebooks on reading the HOPV file and computing molecular descriptors, then start Iteration 3+. The Self-study pages are there when you want to look something up.
You are a teacher
You are welcome to use the course, or parts of it, in your own teaching: the content is licensed under CC BY 4.0 and the code under MIT (see the license). All notebooks, pages and data are in the course repository. We’d love to hear how you use it: post in Show and tell.
An 8-week plan
This plan follows the order of the on-campus course and takes about 24 hours a week, roughly three full days. Prefer a slower pace? Spread it over more weeks; the order is what matters, not the speed.
| Week | Self-study | DataLab |
|---|---|---|
| 1 | Theme 1: What is Machine Learning?, The Machine Learning Landscape, notebooks Getting Started, Classification, Regression, Unsupervised Learning | Iteration 1: your first classification, regression and clustering models |
| 2 | Theme 2: End-to-End ML Project, Data Preparation, Data Understanding, Modeling & Evaluation | Iteration 2: understand the raw HOPV data and prepare your own dataset |
| 3 | Theme 3: Classification, Regression, Unsupervised Learning (readings) | Iteration 2: classification, regression and clustering on the full dataset |
| 4 | Theme 4: Pipelines & ColumnTransformer | Iteration 3+: add the lab database and the Kaggle molecules |
| 5 | Theme 5: Decision Trees, Random Forests, Gradient Boosting and their readings | Iteration 3+: try tree-based models and boosting on the project |
| 6 | (project week) | Iteration 3+: your validation strategy, clustering all molecules, error analysis |
| 7 | Theme 6: Communicating Technical Work, Understanding ML Research, Research to Practice | Iteration 3+: keep iterating |
| 8 | Finish: your final model and Kaggle submissions; write down what you tried and what you learned |
What you need
- Prerequisites. Basic Python, NumPy, pandas and Matplotlib (see the overview). If you need a refresher: the Python tutorial, NumPy for absolute beginners, 10 minutes to pandas, the Matplotlib quick start, or Kaggle’s free Python and pandas courses. No chemistry is needed: the project explains what you need to know.
- The textbook. The reading guides follow Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow (3rd edition, O’Reilly). Many libraries offer it through the O’Reilly learning platform. Without the book you can still do the whole course: the reading guides say what each chapter is about, the book’s own notebooks are free, and the course notebooks stand on their own.
- A computer with about 1 GB of free disk space for the setup. Any recent Windows, macOS or Linux computer works.
Working through the notebooks
The notebooks mix explanations with exercises, marked Your Turn, and empty cells marked # YOUR CODE HERE. Many exercises show the expected output, so you can check your result. There are no published solutions: working out the code yourself is where the learning happens. Read the documentation of the functions you use, and if you ask an AI assistant, ask it to explain rather than to write the code for you.
When you are stuck
- Questions: ask in the course Discussions (Q&A), and help others when you can. Say which notebook and exercise, what you tried and the full error message. Please share hints and ideas, not complete solutions, so that others can still learn from the exercises.
- Mistakes in the course: use Report an issue under the table of contents of any page.
- There is no staff support or grading. The course team looks at the Discussions when it can and fixes reported mistakes, but cannot answer every question.
The Kaggle competition
The competition for this course opens in early 2027; see What’s new. Until then, use a validation strategy you trust: Iteration 3+ has pointers.