text corrections

Author: nocomplexity

abstract.md

@@ -9,7 +9,7 @@ machine learning for real practical business use cases. Machine learning
 is a fascinating technology. Free and Open machine learning should be
 the norm for business innovation. So simple to use for complex problems.
 Freedom to control machine learning technology is not self-evident. Free
-and Open Machine Learning puts you in full control.
+and Open Machine Learning gives you full control.
 
 This publication empowers everyone to make a head start using the
 powerful machine learning technology in a Free, Open and Simple way.
@@ -29,15 +29,15 @@ are impossible to solve by using traditional software technologies. This
 great machine learning technology should available for everyone. This
 means that everyone should be able to learn, play and create great
 applications using machine learning technology. But also reuse existing
-machine learning solutions, inspect solutions and improve solutions of
+machine learning solutions, inspect these solutions and improve solutions of
 others. Without borders or strings attached.
 
-The key focus of this publication in on Free and Open Machine Learning
+The key focus of this publication is on Free and Open Machine Learning
 technologies. This to remove barriers for learning, playing, using and
 reusing machine learning technologies for real practical use cases for
 everyone.
 
-Of course you can use or switch to cloud company solutions to deploy
+Of course you can use or switch to commercial cloud solutions to deploy
 your machine learning driven application in production. But besides
 vendor lock-in, crucial aspects like safety, privacy and security for
 machine learning applications are only possible when using fully
@@ -48,13 +48,13 @@ This document describes an open machine learning architecture. Including
 key aspects that are involved for real business use. This means e.g.
 that we focus on FOSS machine learning software and open datasets.
 
-Since the majority of humans are not a graduated mathematician, we skip
+Since the majority of humans are not graduated mathematicians, we skip
 deep mathematical background concepts of machine learning algorithms in
 this publication. Good books with lots of mathematical background
 information on how machine learning works are available for more than 70
 years. There are plenty excellent free and open publications available
 if you want to learn everything about the inner working of the
-mathematical algorithms that power the current exciting machine learning
+mathematical algorithms that power the current new machine learning
 applications. In the learning resources section in this publication you
 can find a list of good references. All references in this publication
 are publications available under a creative commons license (cc-by).
@@ -64,7 +64,7 @@ learning can be used for real business use cases. This is done by
 describing:
 
 -   Key machine learning concepts. The focus is on concepts that are
-    needed in order to use solid FOSS machine learning frameworks and
+    needed in order to use FOSS machine learning frameworks and
     datasets when creating a machine learning powered application.
 -   An open reference architecture for creating and maintaining a
     machine learning solution architecture and IT landscape.
@@ -85,7 +85,7 @@ machine learning technology for real business use cases. No programming
 knowledge is needed to enjoy and learn machine learning.
 
 This publication is created to give you a head start with using Free and
-Open machine learning technology to solve your business problems.
+Open machine learning technology to solve business problems.
 Without any strings attached, so the focus is on Free and Open
 transparent machine learning technologies and solutions only!
 

preface.md

@@ -1,5 +1,4 @@
-Preface
-=======
+# Preface
 
 We humans are since the beginning of the development of modern computers
 obsessed with creating computers that have super powers. Even before the
@@ -24,7 +23,7 @@ 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! So the machine learning
+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
@@ -40,11 +39,9 @@ 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 is
-also focused on outlining open source machine learning solutions (FOSS)
-that make it possible to start your machine learning journey.
+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 is to enable business IT consultants, IT architects,
+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
@@ -59,9 +56,7 @@ 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
-<https://www.bm-support.org/open-company-principles/> for showing your
-commitment to openness.
+key open principles to create value, you are an open company. See [ROI](https://www.bm-support.org/open-company-principles/) 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.
@@ -76,14 +71,14 @@ 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 are examined. And doing some 'hands-on' to experiences and feel
+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,
+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,