Books
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V Lakshmanan, Data Science on the Google Cloud Platform, O'Reilly Media, Inc., 2017. ISBN: 9781491974551
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you’ll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You’ll learn how to:
[order on Amazon] [order on Google Play Books] |
V. Lakshmanan, Automating the Analysis of Spatial Grids: A Practical Guide to Data Mining Geospatial Images for Human and Environmental Applications. Springer, 2012. ISBN: 978-94-007-4074-7.
The aim of this book is for readers to be able to devise and implement automated techniques to extract information from spatial grids such as radar, satellite or high-resolution satellite imagery or from any data is that can be placed on a spatial grid.The book is based off a course that I taught in Spring 2011 at the University of Oklahoma to a diverse group of graduate students from Computer Science, Meteorology and Environmental Engineering. It should be suitable as a textbook for upper-level undergraduate students and graduate students.
Even though the material developed out of a graduate course, this book is targeted primarily at practitioners i.e. people who need to solve a problem and are looking for ways to address it. Hence, the book forgoes detailed descriptions of theory and mathematical development in favor of more practical issues of implementation.
A software implementation in the Java programming language is included for nearly all the techniques discussed in this book.
[read online at Springer].
Order a $25 print copy of the book. This works only if you access it from an University IP address.
[Order on Amazon]
The aim of this book is for readers to be able to devise and implement automated techniques to extract information from spatial grids such as radar, satellite or high-resolution satellite imagery or from any data is that can be placed on a spatial grid.The book is based off a course that I taught in Spring 2011 at the University of Oklahoma to a diverse group of graduate students from Computer Science, Meteorology and Environmental Engineering. It should be suitable as a textbook for upper-level undergraduate students and graduate students.
Even though the material developed out of a graduate course, this book is targeted primarily at practitioners i.e. people who need to solve a problem and are looking for ways to address it. Hence, the book forgoes detailed descriptions of theory and mathematical development in favor of more practical issues of implementation.
A software implementation in the Java programming language is included for nearly all the techniques discussed in this book.
[read online at Springer].
Order a $25 print copy of the book. This works only if you access it from an University IP address.
[Order on Amazon]
V. Lakshmanan, E. Gilleland, A. McGovern, and M. Tingley, eds., Machine Learning and Data Mining Approaches to Climate Science: Proceedings of the Fourth International Workshop on Climate Informatics. Springer, 2015.
This is an edited volume of contributed papers that arose out of Climate Informatics 2014. I was program chair of this conference, and this was the first year that we were able to put together such a collection of papers representing the state of the science.
[read online at Springer].
This is an edited volume of contributed papers that arose out of Climate Informatics 2014. I was program chair of this conference, and this was the first year that we were able to put together such a collection of papers representing the state of the science.
[read online at Springer].
