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 time-series forecasting to object
recognition and recommender systems using massive databases. Lastly,
this book also provides practical implementation examples and
assignments for the readers to practice and improve their programming
capabilities for the ML applications.