Credit: World Scientific Have you ever wondered if it's possible to learn all there is to know about machine learning and deep learning from a book? Machine Learning--A Journey to Deep Learning, with Exercises and Answers is designed to give the self-taught student a solid foundation in machine learning with step-by-step solutions to the formative exercises and many concrete examples. By going through this text, readers should become able to apply and understand machine learning algorithms as well as create new ones. The main parts of the book address linear and nonlinear regression, supervised learning, learning theory, feature extraction and unsupervised learning. The statistical approach leads to the definition of regularization out of the example of regression. Building on regression, we develop the theory of perceptrons and logistic regression. The book investigates the relation between bias and variance as a consequence of a finite training sample set that is used in machine learning. Out of backpropagation, the authors develop the theory of convolutional networks and deep learning. During the development the authors introduce the RBF networks and support vector machines that indicate why neural networks with hidden nonlinear units can solve most problems.