This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book helps readers to understand the concepts of ML algorithms and enables them to develop the skills necessary to choose an apt ML algorithm for a problem they wish to solve. In addition, this book includes numerous case studies, ranging from simple forecasting to massive network optimization and house price prediction using a massive database such as Zillow. Lastly, this book also provides practical implementation examples and assignments for the readers to practice and improve their programming capabilities for the ML applications.
- ISBN13 9783030967550
- Publish Date 4 August 2022
- Publish Status Forthcoming
- Publish Country CH
- Imprint Springer Nature Switzerland AG
- Edition 1st ed. 2022
- Format Hardcover
- Pages 460
- Language English