Learning AI programming, learning AI programming language Python, these are right

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Primary school students have started to learn Python. God, it is right to learn Python after reading these.

Amway book list

Getting Started with Python

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Getting started with Python programming quickly - automation of tedious tasks

Author: [US] Al Svegat (Svegat)

3 Python3 programming from entry to practice

Amazon's best-selling Python programming book

This book is a practical guide to programming in Python. This book not only introduces the basic knowledge of Python language, but also teaches readers how to apply these knowledge and skills through project practice. The first part of the book introduces the basic concepts of Python programming, and the second part covers a few different tasks. By writing Python programs, computers can do it automatically. Each chapter of the second part has some project procedures for readers to learn. At the end of each chapter, some exercises and in-depth practical projects are provided to help readers consolidate their knowledge. The appendix provides answers to all exercises.

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Learning Python "The Stupid Way" (Third Edition)

Author: [US] Zed A. Shaw

"Stupid Ways to Learn Python" (3rd Edition) is an introductory book on Python, suitable for readers who don't know much about computers and have never learned programming, but are interested in programming. This book guides readers to learn programming step by step in the form of exercises, from simple printing to the realization of complete projects, allowing beginners to start with basic programming techniques and finally experience the basic process of software development.

"The Stupid Way to Learn Python" (3rd Edition) has a very simple structure and includes 52 exercises, 26 of which cover the three topics of input/output, variables, and functions, and the other 26 cover some advanced topics, such as conditional judgment , loops, classes and objects, code testing and project implementation. The format of each chapter is basically the same. Start with code exercises, write the code as instructed, run and check the results, and then do additional exercises.

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A Beginner's Guide to Python Programming

Author: [US] Michael Dawson

A Beginner's Guide to Python Programming tries to help beginners master the Python language and programming skills in an easy and fun way. This book has 12 chapters. Each chapter will use a complete game to demonstrate key knowledge points, and learn programming by writing fun small software, which will arouse readers' interest and reduce learning difficulty. At the end of each chapter, the knowledge points of the chapter will be summarized, and some small exercises will be given to let readers try the skills. The author cleverly embeds all his programming knowledge into these examples, which is really entertaining.

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Data structure (Python language description)

Author: [US] Kenneth A. Lambert (Lambert)

In computer science, data structure is an advanced course, the concept is abstract, and it is difficult. Python syntax is simple and interactive. It is easier and clearer to explain topics such as data structures in Python than in C.

Chapter 1 of this book briefly introduces the basic knowledge and characteristics of Python language. The second chapter introduces abstract data type, data structure, complexity analysis, array and linear linked list structure in detail to the fourth chapter. The fifth chapter and the sixth chapter focus on the relevant knowledge of object-oriented design. Chapter 5 covers the main differences between interfaces and implementation, polymorphism, and information hiding. Chapter 6 mainly explains the relevant knowledge of inheritance. Chapters 7 to 9, represented by stacks, queues, and lists, introduce the relevant knowledge of linear collections. Chapter 10 introduces various tree structures, Chapter 11 explains the relevant content of sets and dictionaries, and Chapter 12 introduces graphs and graph processing algorithms. At the end of each chapter, review questions and case studies are given to help readers consolidate and think.

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Think Python Like a Computer Scientist

Author: [US] Alan B. Downey

This book teaches Python language programming with the idea of ​​training readers to think like computer scientists. The main body of the book is how to think, design and develop. The specific programming language is just a medium to provide a convenient introduction to specific scenarios. Not a book that introduces languages, but a book that introduces programming ideas. Unlike other programming language books, it does not stick to language details, but tries to guide readers to become better from the perspective of beginners with vivid examples and rich exercises.

Python Advanced Edition

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Advanced Python Programming (Second Edition)

Authors: [Poland] Michal Jaworski (Jaworski), [France] Tarek Ziade (Ryder)

This book is based on the Python edition. Through 13 chapters, it deeply reveals the advanced skills of Python programming. This book starts with the introduction of the Python language and its community, and discusses important topics such as Python syntax, naming rules, Python package writing, code deployment, extension program development, code management, document writing, test development, code optimization, concurrent programming, and design patterns. A comprehensive and systematic explanation was given.

This book is suitable for readers who want to further improve their Python programming skills, and for readers who are interested in Python programming. Combined with typical and practical development cases, this book can help readers create high-performance, reliable and maintainable Python applications.

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Python high-performance programming

Author: [US] Gorelick (Micha Gorelick), Ozswald (Ian Ozsvald)

This book has 12 chapters in total, focusing on how to optimize the code and speed up the running speed of practical applications. The book covers the following topics: background knowledge of computer internals, lists and tuples, dictionaries and sets, iterators and generators, matrix and vector computation, concurrency, clusters and work queues, and more. Finally, through a series of real cases, the problems that need to be paid attention to in the application scenarios are shown.

This book is suitable for beginners and intermediate Python programmers, readers who have a certain Python language foundation and want to advance and improve.

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Python Geek Project Programming

Author: [US] Mahesh Venkitachalam

Python is an interpreted, object-oriented, high-level programming language with dynamic data types. Through Python programming, we can solve many tasks in real life.

This book helps and encourages readers to explore the world of Python programming through 14 interesting projects. The book consists of 14 chapters, each introducing some interesting projects implemented with Python programming, including analyzing iTunes playlists, simulating artificial life, creating ASCII code art pictures, photo stitching, generating 3D images, creating particle-simulated fireworks fountain effects, realizing Stereo ray casting algorithm, and electronic projects using Python combined with hardware such as Arduino, Raspberry Pi. This book does not introduce the basics of the Python language, but shows how to use Python to solve various practical problems through a series of non-simple projects, and how to use some popular Python libraries.

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Python Core Programming (Third Edition)

Author: [US] Wesley Chun (Wesley Chun)

This book is a new and upgraded version of the classic bestseller "Python Core Programming" (Second Edition), which is divided into three parts. Part 1 explains some general applications of Python, including regular expressions, network programming, Internet client programming, multi-thread programming, GUI programming, database programming, Microsoft Office programming, extended Python, etc. Part 2 explains topics related to Web development, including Web clients and servers, Web programming related to CGI and WSGI, the Diago Web framework, cloud computing, and advanced Web services. The third part is a supplementary/experimental chapter, including some content such as text processing.

This book is suitable for Python developers with certain experience to read.

Pythonartificial intelligence

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Python machine learning - the core algorithm of predictive analysis

Author: [US] Michael Bowles (Bowles)

When learning and researching machine learning, novices in machine learning often don't know the dazzling algorithms.

at a loss. This book helps readers understand machine learning from the perspective of algorithms and Python language implementation.

The book focuses on two core "algorithm families", penalized linear regression and ensemble methods, and uses code examples

Demonstrate the rationale for using the algorithm in question. The book is divided into seven chapters, discussing in detail the two core algorithms of the forecasting model, the construction of the forecasting model, the specific application and realization of the penalty linear regression and the integral method.

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A Practical Guide to Machine Learning with Python

Author: [US] Alexander Combs

Machine learning is a field that has become more and more popular in recent years, and the Python language has gradually developed over a period of time.

Become one of the mainstream programming languages. This book combines two popular fields of machine learning and Python language, and uses two core machine learning algorithms to maximize the advantages of Python language in data analysis.

This book has 10 chapters. The first chapter expounds the ecosystem of Python machine learning, and the remaining nine chapters introduce many algorithms related to machine learning, including various classification algorithms, data visualization techniques, recommendation engines, etc. , which mainly includes the application of machine learning in apartments, air tickets, IPO market, news feed, content promotion, stock market, pictures, chatbots, recommendation engines, etc.

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Proficient in Python Natural Language Processing

Authors: [India] Deepti Chopra, Nisheeth Joshi, Iti Mathur

Natural language processing is one of the fields in computational linguistics and artificial intelligence related to human-computer interaction.

This book is a comprehensive study guide to learning natural language processing. It describes how to implement various NLP tasks in Python to help readers create projects based on real-life applications. The book has 10 chapters covering topics such as string manipulation, statistical language modeling, morphology, part-of-speech tagging, syntax analysis, semantic analysis, sentiment analysis, information retrieval, discourse analysis, and evaluation of natural language processing systems.

This book is suitable for readers who are familiar with the Python language and have a certain understanding and interest in the development of natural language processing.

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Python Design Patterns (Second Edition)

Author: [India] Chetan Giridhar

Design patterns are one of the most powerful ways to structure large software systems. Optimizing software architecture and design has gradually become an important topic in the process of software development and maintenance.

Through 11 chapters, this book fully reveals the content of design patterns, and uses Python language for example analysis. The book includes various design patterns such as Singleton Design Pattern, Factory Pattern, Facade Pattern, Proxy Pattern, Observer Pattern, Command Pattern, Template Method Pattern, Composite Pattern, State Design Pattern, and Antipatterns.

This book is suitable for readers who focus on software design principles and hope to apply excellent design patterns to Python programming, and it is also suitable for ordinary software engineers and architects.

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NLTK Basics Tutorial - Building Machine Learning Applications with NLTK and Python Libraries

Author: [India] Nitin Hadeniya

The NLTK library is one of the most popular and widely used libraries in the field of natural language processing (NLP), and Python has gradually become one of the mainstream programming languages.

This book mainly introduces how to combine the NLTK library with some Python libraries to implement complex NLP tasks and machine learning applications. The book is divided into 10 chapters. Chapter 1 briefly introduces natural language processing. The second, third, and fourth chapters mainly introduce some general preprocessing techniques, preprocessing techniques for natural language processing and named entity recognition techniques. The content after Chapter 5 focuses on how to build some NLP applications, involving text classification, data science and data processing, social media mining and large-scale text mining.

This book is suitable for NLP and machine learning enthusiasts, readers interested in text processing, advanced Python programmers who want to quickly learn NLTK, and machine learning researchers.

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A Guide to Data Science in Python

Author: [India] Gopi Subramanian (Sabra Manian)

60+ practical development skills to help you explore Python and its powerful data science capabilities

As a high-level programming language, Python has become a highly respected language in the programming field because of its simplicity, readability, and scalability, and it has become one of the first choices of data scientists.

This book provides a detailed introduction to the application of Python in data science, covering topics such as data exploration, data analysis and mining, machine learning, and large-scale machine learning. Each chapter provides readers with sufficient mathematical knowledge and code examples to allow readers to understand algorithm functions of different depths and help readers better grasp each knowledge point.

Clearly structured and full of examples, this book will benefit both newcomers to data science and experienced data scientists.

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Write a web crawler in Python

Author: Richard Lawson (Australia)

This book explains how to use Python to write a web crawler program, including the introduction of web crawlers, three methods of grabbing data from pages, extracting data from caches, using multithreading and processes to grab content in dynamic pages, and forms Interact, deal with captcha issues on the page, and use Scarpy and Portia to grab data. Finally, several real websites are crawled using the data crawling technology introduced in this book, aiming to help readers learn and live.

This book is suitable for readers who have some experience in Python programming and are interested in crawler technology.

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Bayesian Thinking: A Pythonic Approach to Statistical Modeling

Author: [US] Alan B. Downey

This book helps those who want to use mathematical tools to solve practical problems. The only requirement may be some knowledge of probability and programming. Bayesian method is a commonly used mathematical method to solve uncertain problems by using probability knowledge. For computer professionals, it should be familiar with its application to common computer problems, such as machine translation, speech recognition, spam detection, etc.

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Python natural language processing

Authors: Steven Bird, Ivan Klein, Edward Loper

Natural language processing is an important direction in the field of computer science and artificial intelligence. It studies various theories and methods for effective communication between humans and computers using natural language, involving all operations of computers on natural language.

Python Natural Language Processing (Python Natural Language Processing) is a practical introductory guide to the field of natural language processing, designed to help readers learn how to write programs to analyze written language. Python Natural Language Processing is based on the Python programming language and a natural language toolkit open source library called NLTK, but does not require readers to have Python programming experience. The book consists of 11 chapters, arranged in order of difficulty. Chapters 1 to 3 introduce the basics of language processing, and describe how to use small Python programs to analyze interesting text information. Chapter 4 discusses structured programming to reinforce the programming points introduced in the previous chapters. Chapters 5 through 7 introduce the fundamentals of language processing, including tagging, classification, and information extraction. Chapters 8 to 10 introduce the methods of sentence analysis, syntactic structure identification and sentence meaning expression. Chapter 11 describes how to effectively manage linguistic data. The concluding section briefly discusses the past and future of natural language processing.

The book is very practical and includes hundreds of practical examples and graded exercises. It can be used for self-study by readers, textbooks for natural language processing or computational linguistics, and supplementary readings for courses such as artificial intelligence, text mining, and corpus linguistics.

Application of Python in various fields

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A Beginner's Guide to Physical Modeling in Python

Author: [US] Jesse M. Kinder, Philip Nelson

#A practical guide to solving scientific problems with Python, recommended by many world-renowned university professors#

This book is designed to help Python learners acquire sufficient Python programming skills for physical modeling. The book is divided into 8 chapters and 5 appendices, including Python basic knowledge, data structure and program control, data input and output, advanced Python knowledge and advanced technology, etc. , running through three physical modeling experiments in different directions and with different difficulties. Appendices describe Python installation, error messages, version differences, and topics for further study.

This book is suitable for Python beginners, especially for readers who want to use Python for scientific computing and physical modeling.

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Python financial practice

Author: [US] Yan Yuxing (Yan Yuxing)

This book introduces the application of Python in the financial field through 12 chapters, from the installation of Python, basic grammar to a series of simple programming examples. This book guides readers to learn Python step by step. At the same time, this book also reveals the application skills of Python in the financial industry by combining various modules of Python, option prices, financial graph drawing, time series, option pricing models, option pricing, etc.

This book is suitable for university teachers and students majoring in finance and accounting. It is also suitable for researchers and practitioners in the financial field to learn from. This book is also a good reference book for readers who have a certain foundation in computer programming but want to work in the financial industry.

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Python geographic data processing

Author: [US] Chris Gerrard (Garrard)

Python, as a high-level programming language, is becoming a highly respected language in the programming field due to its simplicity, readability, and extensibility. As the scripting language of ArcGIS, Python will greatly improve the efficiency of geographic data processing.

This book has 13 chapters, which introduce spatial data, Python foundation, OGR library, vector data, data screening and selection, operation details of geometric objects, spatial relationship, spatial reference system, GDAL library, raster data, supervised and unsupervised techniques, and the use of related modules and libraries in Python. By reading this book, readers will learn more about the specific application of Python language in the field of geographic data processing.

With detailed explanations and rich examples, this book is suitable for any reader who wants to learn to use geospatial data. Readers new to the field of spatial analysis will also benefit from this book.

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A Guide to Geospatial Analysis with Python (Second Edition)

Author: [US] Joel Lawhead (Lehard)

★A practical guide to complete GIS development and remote sensing analysis with Python3, which can efficiently handle various geographical analysis problems.

Python, as a high-level programming language, is becoming one of the most respected languages ​​in the programming world due to its simplicity, readability, and extensibility.

Based on geospatial analysis, this book introduces the application skills of Python in geographic information processing. The book is divided into 10 chapters, respectively introducing Python and geospatial analysis, geospatial data, geospatial technology, Python's geospatial analysis tools, Python and geographic information system, Python and remote sensing, Python and elevation data, Python and advanced geospatial Modeling, real-time data, comprehensive applications, etc.

This book structure is clear, example problem is complete. It is suitable for readers who want to understand the digitization and analysis of surveying and mapping, and developers and researchers who want to use Python for spatial geographic analysis, modeling and GIS analysis.

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ArcGIS-Based Python Programming Skills (Second Edition)

Author: [US] Eric Peppler (Piper)

Python, as a high-level programming language, is becoming a highly respected language in the programming field due to its simplicity, readability, and extensibility. Using Python as a scripting language for GIS development will greatly improve the efficiency of ArcGIS data processing.

This book will show you how to use Python in the desktop ArcGIS environment to create geoprocessing scripts, manage map documents and layers, find and fix missing data links, edit data in feature classes and tables, and more. , thereby improving the productivity of GIS developers.

This book has a clear structure and complete examples. It is not only suitable for professionals engaged in GIS development, but also suitable for readers who intend to contact or engage in Python programming.

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Python data analysis

Author: Ivan Idris [Indonesia]

Python is a multi-paradigm programming language, suitable for both object-oriented application development and functional design patterns. Python has become the programming language of choice for data scientists for data analysis, visualization, and machine learning. Can help you quickly improve work efficiency.

This book will guide novices through all aspects of data analysis in Python, from data retrieval, cleaning, manipulation, visualization, and storage to advanced analysis and modeling. At the same time, the book highlights a series of open source Python modules, such as NumPy, SciPy, matplotlib, pandas, IPython, Cython, scikit-learn, and NLTK. Additionally, the book covers topics such as data visualization, signal processing, time series analysis, databases, predictive analytics, and machine learning. By reading this book, you will become a master of data analysis.

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Python and HDF 5 Big Data Applications

Author: [US] Andrew Colette (Colette)

With the expansion of Python application fields, more and more people use Python to process large numerical data sets, and it is becoming more and more important to use standard formats for data storage and communication. HDFS is rapidly becoming the choice for people to store scientific data.

This book introduces anyone with a fundamental background in data analysis in Python to using HDF5 in Python. This book will focus on HDF5's native feature set rather than Python's high-level abstractions. Readers who are familiar with Python and NumPy will have an easier time reading and mastering the contents of this book.

This book is suitable for Python developers with a certain foundation, especially for readers who want to use Python to develop related applications such as data storage and processing.

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Python financial big data analysis

Author: [Germany] Yves Hilpi Sko

The only professional book that explains in detail how to use Python to analyze and process financial big data; a must-read for practitioners in the field of financial application development.

With its simplicity, readability, scalability, and large and active scientific computing community, Python has been widely and rapidly used in the financial industry that needs to analyze and process large amounts of data, and has become the preferred programming language for developing core applications in this industry . Python financial big data analysis provides skills and tools for data analysis and development of related applications using Python.

Python financial big data analysis is divided into three parts, a total of 19 chapters. The first part introduces the use of Python in finance, covering the reasons why Python is used in the financial industry, its infrastructure and tools, and some specific introductory examples of Python in econometric finance. Part 2 introduces the most important Python libraries, techniques, and methods in financial analysis and application development, including Python data types and structures, matplotlib for data visualization, financial time series data processing, high-performance input/output operations, high-performance Python technology and libraries, various mathematical tools needed in finance, random number generation and random process simulation, Python statistical applications, integration of Python and Excel, Python object-oriented programming and GUI development, integration of Python and Web technology, Web-based development of applications and web services. The third part focuses on the development of Monte Carlo simulation options and derivatives pricing, covering the introduction of valuation framework, financial model simulation, derivatives valuation, portfolio valuation, volatility options and other knowledge.

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A Quick Start to Game Programming with Python (4th Edition)

Author: [US] Al Svegat (Svegat)

This book teaches Python programming by writing small and interesting games, directly shows the source code of the game, and explains the programming principles through examples. Whole book has 21 chapters in all, 12 game programs and examples run through it. Introduces the basics of Python, data types, functions, process control, program debugging, flowchart design, string operations, lists and dictionaries, Cartesian coordinate system, cryptography basics, game AI simulation, animated graphics, collision detection, sound and Graphics and other aspects of programming knowledge. This book can help readers master the basic skills of Python game programming in an easy and interesting process.

This book is suitable for beginners of Python programming of different ages and levels.

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Selenium automated testing - based on Python language [pre-sales]

Author: [India] Gundicha.u (Unmesh Gundecha)

【Estimated time to market: January 10th】

Selenium is a collection of tools, mainly used for automated testing of web applications, and has been widely used in the industry. This book introduces how to use the Python language to call the Selenium WebDriver interface for automatic testing. The main contents are: Introduction of Python-based Selenium WebDriver, the first Selenium Python script, writing unit tests with unittest, generating test reports in HTML format, element positioning, introduction of Selenium Python API, element waiting mechanism, cross-browser testing, mobile testing , writing an iOS test script, writing an Android test script, page objects and black-box testing, advanced features of Selenium WebDriver, integration of third-party tools and frameworks and other core technologies.

This book is suitable for any software tester to read, and it is also suitable as a study book for teachers and students in colleges and universities and as a teaching material for training schools.