Introduction to Machine Learning

Notebook repository adapted from the Kaggle course Introduction to Machine Learning. Credits to Dan Becker.

Requirements

Notebooks need to be run with the following Python packages installed:

The data of this course are downloaded from an S3 bucket. The S3 client accesses the required S3 credentials through these environment variables:

Name		Name	Last commit message	Last commit date
Latest commit History 22 Commits
data		data
00-how-models-work.ipynb		00-how-models-work.ipynb
01-basic-data-exploration.ipynb		01-basic-data-exploration.ipynb
01exercise-explore-your-data.ipynb		01exercise-explore-your-data.ipynb
02-your-first-machine-learning-model.ipynb		02-your-first-machine-learning-model.ipynb
02exercise-your-first-machine-learning-model.ipynb		02exercise-your-first-machine-learning-model.ipynb
03-model-validation.ipynb		03-model-validation.ipynb
03exercise-model-validation.ipynb		03exercise-model-validation.ipynb
04-underfitting-and-overfitting.ipynb		04-underfitting-and-overfitting.ipynb
04exercise-underfitting-and-overfitting.ipynb		04exercise-underfitting-and-overfitting.ipynb
05-random-forests.ipynb		05-random-forests.ipynb
05exercise-random-forests.ipynb		05exercise-random-forests.ipynb
README.md		README.md