typos

Author: nocomplexity

introduction.md

@@ -1,5 +1,4 @@
-Introduction
-============
+# Introduction
 
 Machine learning (ML) is a rapidly advancing technology, made possible
 by the Internet, that already has significant impacts on our everyday
@@ -21,7 +20,7 @@ technology.
 
 You should be aware of the commercial buzz and fads surrounding AI and
 ML: Machine Learning, deep learning and a lot of tools developed are not
-\'a universal solvent\' for solving all current problems. There is magic
+\'a universal solvent\' for solving all current problems. There is no magic
 machine learning tool or method yet that can solve all your complex
 challenges. Machine learning is just a tool to solve a **certain type**
 of problems. Maybe in future the use of machine learning can be applied
@@ -34,7 +33,7 @@ practitioners, industry leaders, policymakers, and the general public.
 
 But despite the hype and money invested in machine learning technology
 the recent 5 years, one big questions remains: Can machine learning
-technology already help us to solve hard and complex business problems
+technology help us to solve hard and complex business problems
 like climate change, health welfare for all humans and other urgent
 problems?
 
@@ -46,16 +45,16 @@ machine learning technologies.
 
 ![Hope and Hype](/images/hope-and-hype.png){.align-center}
 
-Innovation needs openness. This is also more than valid for machine
+Innovation needs openness. This is also valid for machine
 learning technologies. Without real openness new developments and
 innovations in machine learning are impossible. As a practitioners in
 your business domain and with your unique expertise you can start making
 a difference. This publication gives you a starting point for trying to
 apply free and open machine learning technology on your unique use
 cases.
 
-What is covered in this book?
------------------------------
+## What is covered in this book?
+
 
 Nowadays many people are talking about the transformative power of
 machine learning and how it will revolutionize the economy, but what
@@ -65,25 +64,25 @@ improve or disrupt your business using FOSS machine learning tools that
 are widely available? This book gives you an introduction to get started
 with applying FOSS machine learning.
 
-Machine learning concepts are mostly taught by academics and for
+Machine learning concepts are mostly taught by academics for
 academics. That's why most learning material is dry and maths heavy. The
 theory behind machine learning is great, but requires also a very deep
-understanding within statistics and math. There is a large gap between
+understanding of statistics and math. There is a large gap between
 theory and practice. Practice counts, because in a practical business
 context you want to determine if you can solve your problems with
 machine learning tools. Or at minimum do a short and cost efficient run
 to determine if a project has potential and more investments make sense.
 
-To apply machine learning in real business use cases other skills
+To apply machine learning for real business use cases other skills
 besides some feelings for statistics and math are required. You need
 e.g. be able to have some knowledge about all typical IT things that are
 still needed before you can make use of the new paradigm that machine
 learning brings.
 
 This publication is created for applying free and open machine learning
 in practice for real world use cases. This is where the rubber meets the
-road. So the core focus is on the \'How\' questions. So key concepts are
-outlined and a conceptual and logical reference for free and open
+road. So the core focus is on the \'How\' questions. Key concepts are
+outlined and a conceptual and logical reference architecture for free and open
 machine learning architecture is given. This to empower you to make use
 of FOSS machine learning technology in a simple and efficient way.
 
@@ -106,7 +105,7 @@ learning software.
 The mentioned FOSS machine learning software building blocks in this
 publication are used at large. For real business use cases, and maybe
 with large similarities for your use case. And because a lot of ML
-software and tools needed is open (FOSS) software, solutions and tools
+software and tools needed is based on open source software(FOSS), solutions and tools
 available can be studied and improved.
 
 Given that machine learning tools and techniques are already an
@@ -136,7 +135,7 @@ mathematical algorithms behind machine learning. Also the algebra
 functions that form the foundation under machine learning algorithms and
 software libraries are only explained if needed for practical use and
 experiments. If you are interested in learning the mathematical
-foundations on which machine learning is developed you can find good
+foundations on which machine learning is developed, you can find good
 free and open material in the reference section of this book.
 
 This publication aims to cover the high level machine learning concepts
@@ -152,8 +151,8 @@ software, business and technology touch each other.
 explicitly mean FOSS as defined by the Free Software Foundation -
 FSF.org )
 
-Who should read this book?
---------------------------
+## Who should read this book?
+
 
 This book is created for everyone who wants to learn and get started
 with machine learning without being already forced into a specific
@@ -175,14 +174,14 @@ into mathematical or technical details. But after reading this book you
 will have a more complete and realistic overview of the possibilities
 applying machine learning (ML) for your use cases.
 
-Why another book on Machine Learning?
--------------------------------------
+## Why another book on Machine Learning?
+
 
-There are many books, courses and tutorials that learn you what machine
+There are many books, courses and tutorials that teach you what machine
 learning is. However most of these books and courses are focused on
 hands-on learning and requires you to program. Also many books are
 focused on explaining concepts without a clear focus on how tools can be
-used on real business use cases. Also a publication that is truly open
+used to solve real business use cases. Also a publication that is truly open
 and is focused on the broad landscape that is needed for Free and Open
 Machine learning was simply not available.
 
@@ -194,8 +193,8 @@ technology components that are present. Also some notion of the typical
 pitfalls and challenges for applying machine learning for business use
 is needed.
 
-Is Machine Learning complex?
-----------------------------
+## Is Machine Learning complex?
+
 
 You might get the impression when visiting presentations from commercial
 vendors that machine learning is simple. The hard work is already done
@@ -233,14 +232,13 @@ learning algorithm do the hard work.
 
 But don't be fooled. Even solving only \'some type of problems\' using
 machine learning tools is a relatively 'hard' problem. So only equipped
-with the right knowledge, tools and resources it is possible to get
-great results. Solving soft business problems with machine learning
+with the right knowledge, tools and resources it is possible to get results. Solving soft business problems with machine learning
 requires far more than a good computer scientist alone. Using machine
 learning for soft problems requires a variety of disciples and a lot of
 creativity, experimentation and tenacity.
 
-Organization of this book
--------------------------
+## Organization of this book
+
 
 The topics explored in this publication include:
 
@@ -291,15 +289,15 @@ The topics explored in this publication include:
     technology. This section provides references to open learning
     resources, including references to hands-on tutorials.
 
-Errata, updates and support
----------------------------
+## Errata, updates and support
+
 
 We made serious efforts to create a first readable version of this book.
 However if you notice typos, spelling and grammar errors please notify
 us so we can improve this publication. You can create a pull request on
 github or simply send an email to us.
 
-Since the world of machine learning is rapidly evolving this book be
+Since the world of machine learning is rapidly evolving this book will be
 continuously updated. That's why there is an open on-line version of
 this book available that always incorporates the latest updates.
 
@@ -309,6 +307,6 @@ Note
 :::
 
 If like to contribute to promote the Free and Open Machine Learning
-principles and to make this book better: Please CONTRIBUTE! See the HELP
+principles and to make this book better: Please **CONTRIBUTE!** See the HELP
 section.
 :::