Zoltán Somogyi - Step-by-Step Guide from Beginner to Expert
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This Springer imprint is published by the registered company Springer Nature Switzerland AG.
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
To Hannah and Matthies.
Big things in life are made not by luck but by hard work and curiosity!
Zoltn Somogyi
There are many books in the field of machine learning and artificial intelligence, but most of them are either too complex with a lot of mathematical theory and equations or full of all kinds of scripting (Python, R, etc.) source code. The aim of this book is to present a unique easy-to-understand view of the subject with many practical examples and without scripting and source code. Whether you are a complete novice in the field or an expert I am convinced that you will find a lot of valuable information in this book which is the result of several years of research and hard work. There are many topics which are not properly explained in easily understood lay terms in most of the books out there about machine learning, and this is one of the reasons why I decided to write this book. You will not find a single page in this book which is simply filling the space; everything is designed to help you understand this subject in a short period of time. The book contains several Expert sections with more details about specific subjects, where I felt that this was needed, and many practical real-world machine learning application examples explained step by step.
- Part I Introduction
The first chapter is a helicopter view of what machine learning and artificial intelligence are and what the different sub-fields supervised learning, unsupervised learning, and reinforcement learning mean.
- Part II In Depth Machine Learning
The second chapter is a detailed overview of the most important supervised learning, unsupervised learning, and reinforcement learning algorithms.
The third chapter contains an extensive overview of machine learning performance evaluation methods and tools.
The fourth chapter contains everything you need to know about machine learning data - how to collect, store, clean, and preprocess the data.
- Part III Automatic Speech Recognition
The fifth chapter is completely dedicated to the subject of automatic speech recognition (ASR). After reading this chapter you will understand how ASR works and how it can be used.
- Part IV Biometrics Recognition
The sixth and seventh chapters are dedicated to the subject of face and speaker recognition.
- Part V Machine Learning By Example
The eighth chapter contains numerous real-world machine learning case studies covering applications such as disease recognition, recommendation systems, root cause analysis, anomaly detection, intrusion detection, business process improvement, image recognition, and predictive maintenance. All case studies can be applied with the accompanying AI-TOOLKIT software (no programming or scripting skills are needed).
- Part VI The AI-TOOLKIT. Machine Learning Made Simple.
The ninth chapter contains a description of the AI-TOOLKIT and useful information about how to use it.
- Appendix from Regular Expressions to HMM
This appendix contains expert information about hidden Markov models (HMMs) which are often used in natural language processing (NLP) and automatic speech recognition.
The book is accompanied with software called the AI-TOOLKIT which is free for non-commercial purposes, enabling any reader to try out all of the examples in the book without having to invest in other programs. The AI-TOOLKIT contains several easy-to-use machine learning tools. At the time of writing, the following tools are included (which may be extended later): AI-TOOLKIT Professional (the flagship product), DeepAI Educational (a simple neural network learning visualization tool), VoiceBridge (an open source automatic speech recognition tool), VoiceData (a speech data generator for VoiceBridge), and DocumentSummary (a tool for summarizing documents).
The aim of the AI-TOOLKIT is to make the application of machine learning simple and accessible to everyone. No programming or scripting skills are needed except for the fully open source VoiceBridge tool, which is a ready-to-use C++ source code for high-performance speech recognition on MS Windows. The AI-TOOLKIT is compatible with the 64-bit version of MS Windows, and it supports all three major categories of machine learning: supervised, unsupervised, and reinforcement learning, with several types of algorithms per category, and also several machine learning applications. It includes many user-friendly built-in templates for all types of machine learning models.
I hope that you enjoy reading this book and that you find a lot of useful information in it!
You can find all of the data files referred to in the book along with extra information, errata, and all open source software on GitHub: https://github.com/AI-TOOLKIT
You can download the full version of AI-TOOLKIT Professional (MS Windows 64-bit) free of charge for non-commercial purposes here: https://ai-toolkit.blogspot.com/p/download-and-release-notes.html
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