Informatyka
Python Interviews. Discussions with Python Experts
Michael Driscoll
Each of these twenty Python Interviews can inspire and refresh your relationship with Python and the people who make Python what it is today. Let these interviews spark your own creativity, and discover how you also have the ability to make your mark on a thriving tech community. This book invites you to immerse in the Python landscape, and let these remarkable programmers show you how you too can connect and share with Python programmers around the world. Learn from their opinions, enjoy their stories, and use their tech tips.• Brett Cannon - former director of the PSF, Python core developer, led the migration to Python 3.• Steve Holden - tireless Python promoter and former chairman and director of the PSF.• Carol Willing - former director of the PSF and Python core developer, Project Jupyter Steering Council member.• Nick Coghlan - founding member of the PSF's Packaging Working Group and Python core developer.• Jessica McKellar - former director of the PSF and Python activist.• Marc-André Lemburg - Python core developer and founding member of the PSF.• Glyph Lefkowitz - founder of Twisted and fellow of the PSF• Doug Hellmann - fellow of the PSF, creator of the Python Module of the Week blog, Python community member since 1998.• Massimo Di Pierro - fellow of the PSF, data scientist and the inventor of web2py. • Alex Martelli - fellow of the PSF and co-author of Python in a Nutshell.• Barry Warsaw - fellow of the PSF, Python core developer since 1995, and original member of PythonLabs.• Tarek Ziadé - founder of Afpy and author of Expert Python Programming.• Sebastian Raschka - data scientist and author of Python Machine Learning.• Wesley Chun - fellow of the PSF and author of the Core Python Programming books.• Steven Lott - Python blogger and author of Python for Secret Agents.• Oliver Schoenborn - author of Pypubsub and wxPython mailing list contributor.• Al Sweigart - bestselling author of Automate the Boring Stuff with Python and creator of the Python modules Pyperclip and PyAutoGUI.• Luciano Ramalho - fellow of the PSF and the author of Fluent Python.• Mike Bayer - fellow of the PSF, creator of open source libraries including SQLAlchemy.• Jake Vanderplas - data scientist and author of Python Data Science Handbook.
Python. Leksykon kieszonkowy. Wydanie IV
Mark Lutz
Jakie możliwości kryją standardowe moduły biblioteczne? Jak wykonywać operacje na plikach? Jak stworzyć graficzny interfejs użytkownika? Python to wyjątkowo praktyczny język programowania, idealnie nadający się do szybkiego rozwiązywania niecodziennych problemów, z którymi często borykają się koderzy. Nie wymusza on stosowania jednego stylu programowania, co pozwala na dużo większą elastyczność w trakcie pisania kodu. Umożliwia programowanie obiektowe, strukturalne i funkcyjne, a ponadto udostępnia zaawansowane mechanizmy zarządzania pamięcią, zapewnia dynamiczne sprawdzanie typów oraz czytelną składnię. Te wszystkie zalety powodują, że Python ma grupę swoich wiernych fanów. Niniejsza książka należy do popularnej serii "Leksykon kieszonkowy", dzięki której zawsze i wszędzie możesz przypomnieć sobie wybrane zagadnienia, związane z różną tematyką. Pozycja, którą właśnie trzymasz w rękach, została poświęcona językowi Python. W trakcie jej lektury zapoznasz się z takimi zagadnieniami, jak sterowanie przepływem programu, wykorzystanie pętli, list, słowników oraz operacje na plikach. Ponadto w każdej chwili będziesz mógł sprawdzić składnię oraz sposoby wykorzystania funkcji i wyjątków wbudowanych. Książka stanowi znakomite kompendium wiedzy na temat języka Python. Sprawdzi się ona w rękach początkującego użytkownika - jako przewodnik, a w rękach zaawansowanego programisty - jako pomocnik. Wbudowane typy i operatory Działania na liczbach Operacje na łańcuchach znaków Wykorzystanie Unicode w Pythonie Obsługa list oraz słowników Operacje na zbiorach i plikach Sterowanie przepływem programu Konwersja typów Obsługa wyjątków Wykorzystanie przestrzeni nazw Zasięgi zmiennych Przeciążanie operatorów Standardowe moduły biblioteczne Zastosowanie wyrażeń regularnych Tworzenie graficznego interfejsu użytkownika Wyciśnij jeszcze więcej z języka Python!
Alexander Combs, Saurabh Chhajed, Michael Roman
Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks.By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects.
Yuxi (Hayden) Liu
The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML.Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way.With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more.By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.
Python Machine Learning By Example. The easiest way to get into machine learning
Yuxi (Hayden) Liu, Ivan Idris
Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.
Sebastian Raschka, Jared Huffman, Vahid Mirjalili, Ryan...
Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities.If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.
Python Made Easy. A First Course in Computer Programming Using Python
Kevin Wilson
Python Made Easy is designed to transform beginners into proficient Python programmers. The journey begins with an introduction to Python, covering basic concepts and syntax that lay the foundation for your coding skills. As you progress, you'll dive into essential programming constructs like data structures, functions, and file handling.In the second phase of the course, you'll explore more complex topics such as object-oriented programming, modules, and libraries. These sections will give you the tools to write efficient, reusable, and modular code. You'll also learn how to handle exceptions, ensuring your programs are robust and error-resistant. Special attention is given to graphical user interfaces (GUIs) and game development, making your Python skills applicable to a wide range of projects.The final part of the course covers advanced topics like debugging, testing, and deploying Python applications. You'll also delve into web development, where you'll learn to build and deploy web applications using Python. The course concludes with valuable video resources to reinforce your learning and provide additional insights. By the end of this course, you'll have a solid understanding of Python and be ready to tackle real-world programming challenges.
Python Microservices Development. Build, test, deploy, and scale microservices in Python
Tarek Ziadé
We often deploy our web applications into the cloud, and our code needs to interact with many third-party services. An efficient way to build applications to do this is through microservices architecture. But, in practice, it's hard to get this right due to the complexity of all the pieces interacting with each other.This book will teach you how to overcome these issues and craft applications that are built as small standard units, using all the proven best practices and avoiding the usual traps. It's a practical book: you’ll build everything using Python 3 and its amazing tooling ecosystem. You will understand the principles of TDD and apply them. You will use Flask, Tox, and other tools to build your services using best practices. You will learn how to secure connections between services, and how to script Nginx using Lua to build web application firewall features such as rate limiting. You will also familiarize yourself with Docker’s role in microservices, and use Docker containers, CoreOS, and Amazon Web Services to deploy your services.This book will take you on a journey, ending with the creation of a complete Python application based on microservices. By the end of the book, you will be well versed with the fundamentals of building, designing, testing, and deploying your Python microservices.