The field of machine learning is gaining a lot of attention around the world, both in the research community and in the business world. Learning by machine is becoming increasingly important in many aspects of modern life. Deep learning neural networks have been responsible for several recent technological advances, including those in the fields of computer vision, voice processing, machine translation, and reinforcement learning. As a direct consequence of this, neural networks have developed into an indispensable instrument in the toolset of every data scientist. This book explains neural networks, including what they are, why they are effective algorithms and why they have the structure that they do. It starts by discussing the fundamental elements that make up a neural network (i.e., nodes, weights, activation functions, biases, and layers). This book is meant to serve as an introduction to machine learning, which is a field that is quickly becoming more significant in today’s technological landscape. Utilize to the fullest the vast potential that is afforded by various methodologies such as predictive analysis, classifiers, clustering, and Natural Language Processing (NLP). Since mathematical expertise is required not only for describing the algorithms but also for demonstrating to the reader how and where to set the hyperparameters for maximum accuracy, it is essential to have at least a surface-level understanding of the subject matter. This is because mathematical expertise is required. It should not come as a surprise that there are far too many elements to this issue for all of them to be covered. There are far too many facets to be included.