Название: Mastering Algorithm in Python Автор: Ed Norex Издательство: HiTeX Press Год: 2024 Страниц: 556 Язык: английский Формат: pdf, epub, mobi Размер: 10.1 MB
Master the art of solving complex problems with "Mastering Algorithm in Python," your comprehensive guide to understanding and applying algorithms using one of the most versatile programming languages. Whether you're a beginner eager to dive into the world of Computer Science or a seasoned professional looking to sharpen your skills, this book covers everything from fundamental concepts to advanced techniques.
Unlock the secrets of data structures, delve into the intricacies of searching and sorting algorithms, navigate through the complexities of graph algorithms, and conquer challenges with dynamic programming, greedy algorithms, divide and conquer strategies, and backtracking algorithms. Elevate your expertise further as you explore advanced topics including Machine Learning and graphical models, all illustrated through clear, practical Python examples.
This book is organized into structured chapters that each focus on a particular area of algorithms, starting from the basics and gradually moving to more advanced topics. Beginners will find the initial chapters on Python basics and data structures immensely helpful as a foundation. As the reader progresses, they will encounter chapters dedicated to searching and sorting algorithms, graph algorithms, dynamic programming, greedy algorithms, divide and conquer strategies, and backtracking algorithms, culminating in a discussion on advanced topics such as Machine Learning and graphical models in Python. Each chapter is divided into sections that focus on specific techniques or algorithms, making the book a valuable resource for both learning and reference.
The content within this book is designed for a broad audience. Students of Computer Science and related fields will find it an efficient way to strengthen their understanding of algorithms and Python programming. Professionals in the software industry, data science, and academia may use this book as a reference to brush up on specific algorithms or to learn new techniques. Hobbyists and self-taught programmers looking to deepen their knowledge will also find the step-by-step explanations and examples useful.
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