Geographic Data Science with Python
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. Social media, new forms of data, and new computational techniques are revolutionizing social science. In the new world of pervasive, large, frequent, and rapid data, we have new opportunities to understand and analyse the role of geography in everyday life. This book provides the first comprehensive curriculum in geographic data science.
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearning #datascience #geospatial #geospatialdata #geographic #python #data #science
@data_science_weekly
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. Social media, new forms of data, and new computational techniques are revolutionizing social science. In the new world of pervasive, large, frequent, and rapid data, we have new opportunities to understand and analyse the role of geography in everyday life. This book provides the first comprehensive curriculum in geographic data science.
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearning #datascience #geospatial #geospatialdata #geographic #python #data #science
@data_science_weekly
Supervised Machine Learning for Science. How to stop worrying and love your black box by Christoph Molnar & Timo Freiesleben
Machine learning has revolutionized science, from folding proteins and predicting tornadoes to studying human nature. While science has always had an intimate relationship with prediction, machine learning amplified this focus. But can this hyper-focus on prediction models be justified? Can a machine learning model be part of a scientific model? Or are we on the wrong track?
In this book, authors explore and justify supervised machine learning in science. However, a naive application of supervised learning won’t get you far because machine learning in raw form is unsuitable for science. After all, it lacks interpretability, uncertainty quantification, causality, and many more desirable attributes. Yet, we already have all the puzzle pieces needed to improve machine learning, from incorporating domain knowledge and ensuring the representativeness of the training data to creating robust, interpretable, and causal models. The problem is that the solutions are scattered everywhere.
In this book, authors bring together the philosophical justification and the solutions that make supervised machine learning a powerful tool for science.
The book consists of two parts:
- Part 1 discusses the relationship between science and machine learning.
- Part 2 addresses the shortcomings of supervised machine learning.
Link: https://ml-science-book.com/
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearning #ml #science #supervised
@data_science_weekly
Machine learning has revolutionized science, from folding proteins and predicting tornadoes to studying human nature. While science has always had an intimate relationship with prediction, machine learning amplified this focus. But can this hyper-focus on prediction models be justified? Can a machine learning model be part of a scientific model? Or are we on the wrong track?
In this book, authors explore and justify supervised machine learning in science. However, a naive application of supervised learning won’t get you far because machine learning in raw form is unsuitable for science. After all, it lacks interpretability, uncertainty quantification, causality, and many more desirable attributes. Yet, we already have all the puzzle pieces needed to improve machine learning, from incorporating domain knowledge and ensuring the representativeness of the training data to creating robust, interpretable, and causal models. The problem is that the solutions are scattered everywhere.
In this book, authors bring together the philosophical justification and the solutions that make supervised machine learning a powerful tool for science.
The book consists of two parts:
- Part 1 discusses the relationship between science and machine learning.
- Part 2 addresses the shortcomings of supervised machine learning.
Link: https://ml-science-book.com/
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearning #ml #science #supervised
@data_science_weekly