Get more from your data through creating practical machine learning systems with Python
About the Authors
Luis Pedro Coelho is a computational biologist: someone who uses computers as a tool to understand biological systems. In particular, Luis analyzes DNA from microbial communities to characterize their behavior. Luis has also worked extensively in bioimage informatics—the application of machine learning techniques for the analysis of images of biological specimens. His main focus is on the processing and integration of large-scale datasets.
Luis has a PhD from Carnegie Mellon University, one of the leading universities in the world in the area of machine learning. He is the author of several scientific publications.
Luis started developing open source software in 1998 as a way to apply real code to what he was learning in his computer science courses at the Technical University of Lisbon. In 2004, he started developing in Python and has contributed to several open source libraries in this language. He is the lead developer on the popular computer vision package for Python and mahotas, as well as the contributor of several machine learning codes.
Preface
One could argue that it is a fortunate coincidence that you are holding this book in your hands (or have it on your eBook reader). After all, there are millions of books printed every year, which are read by millions of readers. And then there is this book read by you. One could also argue that a couple of machine learning algorithms played their role in leading you to this book—or this book to you. And we, the authors, are happy that you want to understand more about the hows and whys. Most of the book will cover the how. How has data to be processed so that machine learning algorithms can make the most out of it? How should one choose the right algorithm for a problem at hand? Occasionally, we will also cover the why. Why is it important to measure correctly? Why does one algorithm outperform another one in a given scenario? We know that there is much more to learn to be an expert in the field. After all, we only covered some hows and just a tiny fraction of the whys. But in the end, we hope that this mixture will help you to get up and running as quickly as possible.
Download Link:-https://drive.google.com/uc?id=1T3KwKcK-eEncMiR_n8WDZzxdL2-eT0Ak&export=download
About the Authors
Luis Pedro Coelho is a computational biologist: someone who uses computers as a tool to understand biological systems. In particular, Luis analyzes DNA from microbial communities to characterize their behavior. Luis has also worked extensively in bioimage informatics—the application of machine learning techniques for the analysis of images of biological specimens. His main focus is on the processing and integration of large-scale datasets.
Luis has a PhD from Carnegie Mellon University, one of the leading universities in the world in the area of machine learning. He is the author of several scientific publications.
Luis started developing open source software in 1998 as a way to apply real code to what he was learning in his computer science courses at the Technical University of Lisbon. In 2004, he started developing in Python and has contributed to several open source libraries in this language. He is the lead developer on the popular computer vision package for Python and mahotas, as well as the contributor of several machine learning codes.
Preface
One could argue that it is a fortunate coincidence that you are holding this book in your hands (or have it on your eBook reader). After all, there are millions of books printed every year, which are read by millions of readers. And then there is this book read by you. One could also argue that a couple of machine learning algorithms played their role in leading you to this book—or this book to you. And we, the authors, are happy that you want to understand more about the hows and whys. Most of the book will cover the how. How has data to be processed so that machine learning algorithms can make the most out of it? How should one choose the right algorithm for a problem at hand? Occasionally, we will also cover the why. Why is it important to measure correctly? Why does one algorithm outperform another one in a given scenario? We know that there is much more to learn to be an expert in the field. After all, we only covered some hows and just a tiny fraction of the whys. But in the end, we hope that this mixture will help you to get up and running as quickly as possible.
Download Link:-https://drive.google.com/uc?id=1T3KwKcK-eEncMiR_n8WDZzxdL2-eT0Ak&export=download
This is dummy text. It is not meant to be read. Accordingly, it is difficult to figure out when to end it. But then, this is dummy text. It is not meant to be read. Period.
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