If you've ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. What if you could have your computer do that for you? In this second edition of Automate the Boring Stuff with Python, you'll learn the basics of programming in Python, the fastest growing programming language today, before moving on to create Pytho...(展开全部)

Al Sweigart 是一名软件开发者，还教小孩和成人编程。他为初学者写了几本Python 书籍，包括《Python 密码学编程》、《Python 游戏编程快速上手》和《Python 和Pygame 游戏开发指南》。

Al Sweigart 是一名软件开发者，还教小孩和成人编程。他为初学者写了几本Python 书籍，包括《Python 密码学编程》、《Python 游戏编程快速上手》和《Python 和Pygame 游戏开发指南》。

We have a problem with Stuff. With just 5 percent of the world’s population, we’re consuming 30 percent of the world’s resources and creating 30 percent of the world’s waste. If everyone consumed at U.S. rates, we would need three to five planets! This alarming fact drove Annie Leonard to create the Internet film sensation The Story of Stuff, which has been viewed over 10 mill...(展开全部)

大卫·无聊的冒险：一个19岁的保安，有着痛苦的内心生活和痴迷的天性。当他遇到梦中的女孩时，事情开始出错：看起来好得令人难以置信的事情显然是，在无聊的生活中，最真实的事情是，在适当的情况下（在这种情况下，复仇、羞辱和谋杀的狂欢级联），人类的原始本性将无情地凸显出来。

Mark Schultz 曾为DC漫画公司（DC Comics）和惊奇漫画公司（Marvel Comics）创作过大量作品，他的剧本和插图已经获得5项哈维奖、2项艾斯纳奖和1项英克波特奖，其中最著名的作品包括他的力作《恐龙快打》（Xenozoic Tales）系列漫画和《超人》（Superman）。 Illustrated by Zander Cannon and Kevin Cannon 插图画家，他们的客户既包括美国国家海洋与大气管理局，也包括DC漫画公司，共同创作的作品包括《替身上帝》（The Replacement God）和Smax，此外，《前十名》（Top 10）还获得2项艾斯纳奖。

Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python.The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions.By the end of this book, you’ll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.

Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python.The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions.By the end of this book, you’ll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.

This book is for experienced Python developers who are aspiring to become the architects of enterprise-grade applications or software architects who would like to leverage Python to create effective blueprints of applications.