Free and Open Machine Learning Book¶
This book is all about applying open machine learning solutions for real practical use cases. So the core focus is on how outlining how you can apply machine learning in a simple way. Books with lots of mathematical background information on how machine learning works are available for more than 70 years. So if you are keen on mathematical background information, you should take a look at the open learning resources section of this application.
Machine learning is an exciting powerful technology. The use of machine learning technology on larger scale opens new opportunities and will give improvements for everyone. Machine learning technology should be available for everyone. So without barriers. But with the rise of new machine learning enabled products it is important to be able to ask critical questions on how safety, security, privacy and ethical issues are handled.
This book is created to give you a head start to use and apply new Open Source machine learning technologies to solve your business problems. Important machine learning concepts are explained, but the main emphasis is on providing insights in the possibilities that are now available within the growing open source machine learning ecosystem. This so you can start applying machine learning in your business today. And be able to judge answers regarding important quality aspects, like safety, security and privacy.
This book gives an overview of all important OSS machine learning frameworks and OSS support tools that you can use for prototyping with machine learning or when using machine learning for real production use cases.
This document is in alfa-stage!! Collaboration is fun, so Help Us by contributing ! There are some chapters currently written and editing work (typos,spelling) is yet to be done! Some more background information of the project can be found in the readme on github.com. And do not forget to join the ROI movement!
Table of Contents¶
- Why focus on open source?
- What is machine learning
- ML, AI and NLP: What is what
- Statistics is not machine learning
- The paradigm shift: Creating smart software
- Overview machine learning methods
- Other common terms used in the ML world
- ML Reference Architecture
- Security,Privacy and Safety
- Machine Learning for Business Problems
- When to use machine learning for business problems?
- Common business use cases
- Example use cases
- Exiting ML business examples
- Business principles for Machine Learning applications
- Business ethics
- Catalogue of Open ML Software
- Acumos AI
- Apache MXNet
- Apache Spark MLlib
- Cookiecutter Data Science
- Data Science Version Control (DVC)
- NLP Architect
- NNI (Neural Network Intelligence)
- OpenCV: Open Source Computer Vision Library
- TextBlob: Simplified Text Processing
- What-If Tool
- Catalogue of Open NLP Software
- ML Learning resources
- NLP Learning resources
The following people have contributed to the Free and Open Machine Learning project:
[name] [OPTIONAL email] [Optional Organization name ]
If you like your name stated here: This book is open source. Issues and pull requests are welcome. All contributors will be added to this list.
So Get involved in the discussion to make it better!
If you wish to make comments regarding this document, please raise them as GitHub issues. Or send comments by email if you are unable to raise issues on GitHub. All comments are welcome!