We humans are since the beginning of the development of modern computers obsessed with creating computers that have super powers. Even before the birth of computers, research has been done on artificial intelligence (AI). The question what artificial intelligence really is, is hard and fuel for philosophical discussions.
Nowadays we see more and more products created that claim to have super powers that come close to AI. A look under the surface shows however that the real progress on AI is made by a tangible technique, called machine learning. So our the focus in this publication is on machine learning. And not on philosophical views on what will be possible in the future when machine learning evolves towards AI.
Machine learning today is capable of solving challenging problems that impact everyone around the world. Problems that were impossible to solve in the past. Or problems that where too expensive or too complex to solve using traditional computer technologies. Nowadays solving a certain type of complex problems is possible using new machine learning technology.
Very complex problems and meaningful problems are currently solved using applications based on machine learning algorithms. Many firms involved are willing to tell and show you how easy it is! But you must be aware: machine learning is a buzzword in the industry. The machine learning field is full of companies that use fads, all kind of vendor lock-in options and marketing buzz to take your money without delivering long running solutions. That is why this publication advocates for Free and Open machine learning.
This publication is aimed to give you practical information so you can start with applying free and open machine learning tools and frameworks. With minimum cost and no strings attached. This publication enables you also with the knowledge of what is possible with machine learning technology and what is still wishful thinking.
Everything described in this publication is with no strings attached. So the focus is on openness for machine learning tools, algorithms and knowledge. The core focus is outlining core concepts of machine learning and showing an open machine learning architecture that make machine learning possible for real business use cases. So this publication outlines open source machine learning solutions (FOSS) that make it possible to start your machine learning journey.
This publication enables business IT consultants, IT architects, and software developers to get a practical grounding in open machine learning and its business applications. So no programming exercises and no complex mathematical formulas in this publication. Showing programming code is avoided on purpose. In the reference section of this publication you can find good open references for hands-on machine learning tutorials. As an add-on to this publication some hands-on machine learning tutorials are published as addendum with the online version of this publication.
Understanding core concepts of machine learning and using open machine learning technology is possible without coding. This publication empowers you to start transforming your organization into an innovative and open company for the future using new open machine learning technologies. If your company is committed to openness and you endorse key open principles to create value, you are an open company. See ROI for showing your commitment to openness.
Machine learning is and should not be the exclusive domain of commercial companies, data scientists, mathematics, computer scientists or hackers. Every business and everyone involved with automation should be able to take advantage of the machine learning techniques and applications available. This is possible within the field of machine learning as you learn in this publication.
Nowadays knowledge is more and more openly shared, thanks to open access, open publication licenses and open source software. So everyone can and should benefit from the possibilities that open machine learning frameworks and tools provide.
To create this publication a lot of papers, books and reports on machine learning have been examined. And doing some ‘hands-on’ to experiences and feel the power of machine learning algorithms turned out to be crucial for understanding and creating this publication as well. This publication is focussed on making a the complex machine learning technology simple to use.
In my journey on learning how to apply machine learning for real business use cases, many books turned out to be either too theoretical, or too much focused on programming machine learning algorithms. As an IT architect I missed the overall machine learning architecture picture from a typical IT architecture point of view. So business, information, application, infrastructure, security and privacy perspective. This publication fills up that gap.
Applying machine learning should be easy and simple. When barriers for using machine learning technology are lowered many more great applications can be developed for the benefit for everyone. This publication simplifies the use of the complex field of machine learning frameworks, software and applications for real business use cases. Creating meaningful machine learning applications in a already complex context is another discipline than creating and understanding the complex machine learning algorithms behind the machine learning frameworks. So this publication is for everyone who is short on time but is dedicated to make use of machine learning capabilities.
This publication is not an end, but is constructed as a continuous effort to provide usable open and non commercial information for applying machine learning technology. You can join this project too. See the HELP section in this publication.
This publication was only possible with the help of you! If you have a suggestion or correction, please send an email to info [at] bm-support [dot] org. I add you to the contributor list, unless you ask to be omitted.