The book "Machine Learning Mathematics in Python" is a comprehensive guide to understanding the mathematical foundations of machine learning algorithms using Python programming language. The book covers topics such as linear algebra, calculus, probability, and statistics, providing readers with a solid foundation in the mathematical concepts that underlie machine learning. The author emphasizes the importance of understanding these concepts in order to develop a personal paradigm for perceiving the technological process of developing modern knowledge and its role in the survival of humanity. The book begins by introducing the reader to the basics of machine learning, including the different types of learning and their applications. It then delves into the mathematical concepts that are essential for understanding machine learning algorithms, such as linear regression, gradient descent, and neural networks. The author provides a detailed explanation of each concept, along with practical examples and exercises to help reinforce the reader's understanding. One of the unique aspects of this book is its focus on the need to study and understand the process of technology evolution. The author argues that the rapid pace of technological change requires individuals to constantly update their skills and knowledge in order to remain relevant in the job market. This means that it is no longer enough to simply learn a specific skill set and then stop, but rather, individuals must continuously adapt and evolve to stay ahead of the curve.

''