Introduction to Python: With Applications in Optimization, Image and Video Processing, and Machine Learning

Python’s usefulness becomes especially clear when it moves from basic syntax into real quantitative work. This textbook builds from programming fundamentals toward applications in optimization, image and video processing, and machine learning, giving readers a connected introduction to both the language and the kinds of problems it can help address.

David Báez-López and David Alfredo Báez Villegas begin with the essentials and gradually introduce more advanced material. The progression makes the book relevant to students encountering Python for the first time, while its later applications also offer useful reference material for readers in technical and quantitative fields.

Start with the language, then build outward

Early chapters cover variables, input and output, conditionals, loops, functions, and common data structures such as strings, lists, tuples, and dictionaries. From there, the book turns to arrays, vectors, matrices, and data frames, including work with NumPy and Pandas. This foundation helps readers understand how Python code is organized and how data can be represented and manipulated.

Connect programming to quantitative applications

The book’s focus extends beyond general-purpose coding. Its contents move into plotting, optimization concepts and methods, image and video processing with OpenCV, machine learning, and neural networks. Seeing these subjects within one text gives readers a route from core programming ideas to several areas where computational methods are used.

For study and professional reference

The authors position the book for advanced undergraduate and graduate students in mathematics, computer science, engineering, and related quantitative disciplines. It may also suit researchers and professionals who want a broad Python reference spanning foundational topics and applied techniques. Chapter instructions, examples, and exercises support study alongside the explanations.

A broad path through Python

From loops and data structures to optimization and machine learning, Introduction to Python offers a substantial, application-oriented path through the language. It is a useful choice for readers who want to see how Python fundamentals connect with computational work in science, engineering, and other quantitative fields.