Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data (Princeton Series …
Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows …
Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts …
Natural Language Processing (NLP) is the field of artificial intelligence and computational linguistics that deals with the interactions between computers …
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and …
Machine learning is the study of building computer programs that improve their performance through experience. To meet the challenge of …
A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October …
Machine learning deals with the issue of how to build computer programs that improve their performance at some tasks through …
Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and …
This book constitutes the refereed proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES …
The two-volume set LNCS 9242 + 9243 constitutes the proceedings of the 5th International Conference on Intelligence Science and Big …
Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and …
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, …
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to …
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand …
My goal here is for something that is partly a tutorial and partly a reference book. I like how tutorials …
Introduction To Conformal Prediction With Python is the quickest way to learn an easy-to-use and very general technique for uncertainty …
TheInternationalConferenceonMachineLearningandDataMining(MLDM)is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer …
MLDM / ICDM Medaillie Meissner Porcellan, the “White Gold” of King August the Strongest of Saxonia Gottfried Wilhelm von Leibniz, …
This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition, …
This book constitutes the refereed proceedings of the 4th International Conference on Machine Learning and Data Mining in Pattern Recognition, …
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that …
This book constitutes the refereed proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition, …
This book constitutes the refereed proceedings of the 7th International Conference on Machine Learning and Data Mining in Pattern Recognition, …
This book constitutes the refereed proceedings of the 8th International Conference, MLDM 2012, held in Berlin, Germany in July 2012. …
On order equivalences between distance and similarity measures on sequences and trees.- Scalable Corpus Annotation by Graph Construction and Label …
This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition, …
This book contains the Proceedings of the US-Japan Seminar on Learning Process in Control Systems. The seminar, held in Nagoya, …
Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET …
Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and …
Industrial robots carry out simple tasks in customized environments for which it is typical that nearly all e?ector movements can …
Implement natural language processing applications with Python using a problem-solution approach. This book has numerous coding exercises that will help …
Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key …
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins …
