CS-E3210 - Machine Learning: Basic Principles is a subject from Aalto University, which I coursed during the months of September and October of 2018. This repository is a collection of all exercises, developed in Python thought Jupyter Notebooks, I've done during the course.
During the course, we've basically done assignments weekly (which can be found in the assignments folder) and, in the end, a project (of course, found in the project folder). More information about all the exercises can be found in the Jupyter Notebook itself.
The project was done together with @simonedesogus. Besides the report, one the criterias was to get a minimum in two Kaggle competitions: accuracy and log-loss. Our project (identified as Group 105 in both leaderboards) beat the benchmarks.
First, make sure you have Jupyter installed in your machine. If you don't, you can follow the instructions here.
Then, clone the repo:
git clone https://github.com/davicorreiajr/machine-learning-basic-principles.git
cd machine-learning-basic-principles
Finally, start the Jupyter:
jupyter notebook
All the assignments had a very good explanation and came with some code done. Also, in the references folder, you can find a notebook from the teacher that served as a good reference. All this was possible thanks to the staff of the course.
Please, if you find any problem or have some sugestion, don't hesitate to open an issue or even a pull request.