python-machine-learning-book
The "Python Machine Learning (1st edition)" book code repository and info resource
Explore code examples from the first edition of a comprehensive book on machine learning to help you learn.
From the README
- Italian translation via "Apogeo"
- German translation via "mitp Verlag"
- Japanese translation via "Impress Top Gear"
- Chinese translation (traditional Chinese)
- Chinese translation (simple Chinese)
- Korean translation via "Kyobo"
Excerpts from the project README on GitHub. Copyright and licensing remain with the respective authors.
Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.
Get an email alert on its next release or when it starts trending — never miss the moment.
Free · no card · unsubscribe anytimeThe "Python Machine Learning (1st edition)" book code repository and info resource
python-machine-learning-book has 12.6k stars on GitHub. It has been forked 4.4k times. python-machine-learning-book is written mainly in Jupyter Notebook. It has been in active development since 2015. python-machine-learning-book is available under the MIT license. Its main topics are data-mining, data-science, logistic-regression, machine-learning.
What is python-machine-learning-book?
The "Python Machine Learning (1st edition)" book code repository and info resource
Is python-machine-learning-book open source?
python-machine-learning-book is an open-source project. It is released under the MIT license.
Is python-machine-learning-book free?
Yes. python-machine-learning-book is free and open source — you can use, modify and self-host it.
What license does python-machine-learning-book use?
python-machine-learning-book is available under the MIT license.
What language is python-machine-learning-book written in?
python-machine-learning-book is written mainly in Jupyter Notebook.
Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.
[](https://olud.ai/project/rasbt-python-machine-learning-book.html)
Measured from GitHub topics shared by both projects, weighted by how rare each topic is.