Machine Learning for Computer Scientists and Data Analysts

The textbook "Machine Learning for Computer Scientists and Data Analysts" offers a comprehensive introduction to the theoretical foundations of machine learning (ML) algorithms. It starts with the basic concepts of neural networks and progresses to more complex structures such as generative adversarial networks and graph-based convolutional networks. The aim of the book is to provide readers with a deep understanding of ML algorithms and equip them with the skills necessary to select appropriate algorithms for specific problems. Through numerous case studies, ranging from simple time series forecasting to object detection and recommendation systems, the theoretical knowledge is complemented with practical applications. Additionally, the book includes practical implementation examples and exercises that help readers improve their programming skills in the field of ML applications.