MACHINE LEARNING
found 109 books
Machine Learning Yearning
by Andrew Ng
AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to: - Prioritize the most promising direc...
Azure Machine Learning
by Jeff Barnes
This third ebook in the series introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and princ...
The Big Book of Machine Learning Use Cases
by Ricardo Portilla, Brenner Heintz, Denny Lee
The world of machine learning is evolving so quickly that it's challenging to find real-life use cases that are relevant to your day-to-day work. That's why we've created this comprehensive guide you can start using right away. Get everything you need - use cases, code samples and notebooks - so you can start putting the Databrick...
Machine Learning for Cyber Physical Systems
by Jürgen Beyerer, Christian Kühnert, Oliver Niggemann
This book proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to...
Automated Machine Learning
by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created ...
Multiple-Aspect Analysis of Semantic Trajectories
by Konstantinos Tserpes, Chiara Renso, Stan Matwin
This free book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 ful...
An Introduction to Machine Learning
by Miroslav Kubat
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural ...
Python Machine Learning Projects
by Lisa Tagliaferri, Michelle Morales, Ellie Birkbeck, Alvin Wan
As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions - sometimes without final input from humans who may be impacted by these findings - it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers ...
Machine Learning for Cyber Physical Systems
by Jürgen Beyerer, Alexander Maier, Oliver Niggemann
This open proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. Cyber Physical Systems are characterized by their ability to ada...
Foundations of Machine Learning
by Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basi...