Introduction to Python: with Applications in Optimization, Image and Video Processing, and Machine Learning is intended primarily for advanced undergraduate and graduate students in quantitative sciences such as mathematics, Computer Science, and engineering. In addition to this, the book is written in such a way that it can also serve as a self-contained handbook for professionals working in quantitative fields including finance, IT, and many other industries where programming is a useful or essential tool. The book is written to be accessible and useful to those with no prior experience of Python, but those who are somewhat more adept will also benefit from the more advanced material that comes later in the book. Python is a high-level programming language that supports procedural, imperative, object-oriented, and functional modes. This book covers the first three modes. Python has a simple easy to learn syntax. Python supports modules, libraries, and packages, encouraging program modularity and code reuse. The book may be divided in two parts. The first part, comprised of Chapters 1 to 8, can be used by a person interested in learning Python from scratch; that is, heshe does not have any knowledge of Python. The learning goes from installing Python to implementing functions and generating complex plots. The second part covers advanced topics such as optimization, image and video processing, and Machine Learning using Keras, TensorFlow, and neural networks. Throughout the book, the use of external modules or libraries makes the Python programs very powerful.