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!

Python. Leksykon kieszonkowy. Wydanie V

Mark Lutz

Język Python obecny jest na rynku od ponad 20 lat. Opracowany został na początku lat dziewięćdziesiątych XX wieku i błyskawicznie zaczął zdobywać uznanie programistów na całym świecie. Python sprawdza się doskonale w pisaniu skryptów oraz narzędzi, a w dużym projekcie także nie zawiedzie oczekiwań. Język ten korzysta z automatycznego zarządzania pamięcią oraz umożliwia obiektowe i funkcyjne podejście do tworzonego programu. Jednym z jego najważniejszych atutów jest bardzo silna społeczność programistów, wymieniająca się na bieżąco informacjami na temat praktycznych zastosowań tego języka. Dzięki temu uzyskanie odpowiedzi na trapiące Cię pytania nie powinno stanowić problemu. Jeżeli jednak chcesz mieć zawsze pod ręką sprawdzone źródło informacji, które pozwoli Ci w każdej sytuacji rozwiać wątpliwości, to trafiłeś na doskonałą pozycję. Należy ona do serii Leksykon kieszonkowy i charakteryzuje się niezwykle zwięzłym, przejrzystym układem najważniejszych treści oraz poręczną formą. Znajdziesz tu szczegółowe informacje na temat typów wbudowanych, wyjątków, programowania obiektowego oraz przetwarzania nazw i reguł zasięgu. Kolejne wydanie tej książki zostało ulepszone i zaktualizowane o mnóstwo nowych informacji, takich jak wykorzystanie Python Launcher w systemie Windows czy formalne reguły dziedziczenia. To doskonałe źródło informacji na temat języka Python! Dzięki tej książce: poznasz podstawy Pythona zapoznasz się z zasadami programowania w tym języku poznasz typy wbudowane wykorzystasz standardowe moduły będziesz mieć zawsze pod ręką solidne źródło informacji o Pythonie Najlepsze rozwiązania typowych problemów!

Python Machine Learning Blueprints. Put your machine learning concepts to the test by developing real-world smart projects - Second Edition

Alexander Combs, 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.

Python Machine Learning By Example. Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn - Third Edition

Yuxi (Hayden) Liu

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.

Python Machine Learning By Example. Implement machine learning algorithms and techniques to build intelligent systems - Second Edition

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

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.

Python Machine Learning By Example. Unlock machine learning best practices with real-world use cases - Fourth Edition

Yuxi (Hayden) Liu

The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts.Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine.This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.

Python Machine Learning Cookbook. 100 recipes that teach you how to perform various machine learning tasks in the real world

Prateek Joshi

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

Python Machine Learning Cookbook. Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets - Second Edition

Giuseppe Ciaburro, Prateek Joshi

This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks.With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning.By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.

Python. Machine learning i deep learning. Biblioteki scikit-learn i TensorFlow 2. Wydanie III

Sebastian Raschka, Vahid Mirjalili

Uczenie maszynowe jest jedną z najbardziej fascynujących technologii naszych czasów - rozwojem jego najróżniejszych zastosowań zajmują się tacy giganci jak Google, Facebook, Apple, Amazon czy IBM. Uczenie maszynowe otwiera zupełnie nowe możliwości i powoli staje się nieodzowne: wystarczy wymienić asystenty głosowe w smartfonach, chatboty ułatwiające klientom wybór produktu, a także sieci ułatwiające podejmowanie decyzji o inwestycjach giełdowych, filtrujące niechciane wiadomości e-mail czy wspomagające diagnostykę medyczną. Oto obszerny przewodnik po uczeniu maszynowym i uczeniu głębokim w Pythonie. Zawiera dokładne omówienie najważniejszych technik uczenia maszynowego oraz staranne wyjaśnienie zasad rządzących tą technologią. Poszczególne zagadnienia zilustrowano mnóstwem wyjaśnień, wizualizacji i przykładów, co znakomicie ułatwia zrozumienie materiału i sprawne rozpoczęcie samodzielnego budowania aplikacji i modeli, takich jak te służące do klasyfikacji obrazów, odkrywania ukrytych wzorców czy wydobywania dodatkowych informacji z danych. Wydanie trzecie zostało zaktualizowane - znalazł się w nim opis biblioteki TensorFlow 2 i najnowszych dodatków do biblioteki scikit-learn. Dodano również wprowadzenie do dwóch nowatorskich technik: uczenia przez wzmacnianie i budowy generatywnych sieci przeciwstawnych (GAN). W książce między innymi: platformy, modele i techniki uczenia maszynowego wykorzystywanie biblioteki scikit-learn i TensorFlow sieci neuronowe, sieci GAN i inne przygotowywanie danych dla modeli uczenia maszynowego ocena i strojenie modeli analizy: regresyjna, skupień i sentymentów Uczenie głębokie z Pythonem: zrozum i zastosuj!

Python Machine Learning. Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorial

Sebastian Raschka

Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.

Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Second Edition

Sebastian Raschka, Vahid Mirjalili

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 Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - Third Edition

Sebastian Raschka, Vahid Mirjalili

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

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: Master the Art of Design Patterns. Click here to enter text

Dusty Phillips, Chetan Giridhar, Sakis Kasampalis

Python is an object-oriented scripting language that is used in everything from data science to web development. Known for its simplicity, Python increases productivity and minimizes development time. Through applying essential software engineering design patterns to Python, Python code becomes even more efficient and reusable from project to project. This learning path takes you through every traditional and advanced design pattern best applied to Python code, building your skills in writing exceptional Python. Divided into three distinct modules, you’ll go from foundational to advanced concepts by following a series of practical tutorials.Start with the bedrock of Python programming – the object-oriented paradigm. Rethink the way you work with Python as you work through the Python data structures and object-oriented techniques essential to modern Python programming. Build your confidence as you learn Python syntax, and how to use OOP principles with Python tools such as Django and Kivy.In the second module, run through the most common and most useful design patterns from a Python perspective. Progress through Singleton patterns, Factory patterns, Façade patterns and more all with detailed hands-on guidance. Enhance your professional abilities in in software architecture, design, and development.In the final module, run through the more complex and less common design patterns, discovering how to apply them to Python coding with the help of real-world examples. Get to grips with the best practices of writing Python, as well as creating systems architecture and troubleshooting issues.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:? Python 3 Object-Oriented Programming - Second Edition by Dusty Phillips? Learning Python Design Patterns - Second Edition by Chetan Giridhar? Mastering Python Design Patterns by Sakis Kasampalis

Python Microservices Development. Build efficient and lightweight microservices using the Python tooling ecosystem - Second Edition

Simon Fraser, Tarek Ziadé

The small scope and self-contained nature of microservices make them faster, cleaner, and more scalable than code-heavy monolithic applications. However, building microservices architecture that is efficient as well as lightweight into your applications can be challenging due to the complexity of all the interacting pieces.Python Microservices Development, Second Edition will teach you how to overcome these issues and craft applications that are built as small standard units using proven best practices and avoiding common pitfalls. Through hands-on examples, this book will help you to build efficient microservices using Quart, SQLAlchemy, and other modern Python toolsIn this updated edition, 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. Python Microservices Development, Second Edition describes how to use containers and AWS to deploy your services. By the end of the book, you’ll have created a complete Python application based on microservices.

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.

Python Multimedia. Learn how to develop Multimedia applications using Python with this practical step-by-step guide

Ninad Sathaye

Multimedia applications are used by a range of industries to enhance the visual appeal of a product. This book will teach the reader how to perform multimedia processing using Python.This step-by-step guide gives you hands-on experience for developing exciting multimedia applications using Python. This book will help you to build applications for processing images, creating 2D animations and processing audio and video.Writing applications that work with images, videos, and other sensory effects is great. Not every application gets to make full use of audio/visual effects, but a certain amount of multimedia makes any application a lot more appealing. There are numerous multimedia libraries for which Python bindings are available. These libraries enable working with different kinds of media, such as images, audio, video, games, and so on. This book introduces the reader to the most widely used open source libraries through several exciting, real world projects. Popular multimedia frameworks and libraries such as GStreamer,Pyglet, QT Phonon, and Python Imaging library are used to develop various multimedia applications.

Python na maturze. Rozwiązania i analiza wybranych zadań programistycznych

Roland Zimek

Matura z Pythonem to nic trudnego! Ucz się! Analizuj! Programuj! Nauka programowania na dobre zadomowiła się w szkołach, a umiejętności informatyczne są od lat sprawdzane na egzaminach maturalnych. Jej adepci zapewne zdają sobie sprawę, jakim wzięciem na rynku cieszą się osoby, które mogą się pochwalić znajomością Pythona. To język, który dzięki swoim możliwościom i wszechstronności znajduje zastosowanie w najróżniejszych dziedzinach nauki, przemysłu i biznesu. Książka Python na maturze. Rozwiązania i analiza wybranych zadań programistycznych pozwoli Ci zapoznać się z prawdziwymi zadaniami maturalnymi z kilku ostatnich lat. Będziesz mógł je przeanalizować, a także opracować prawidłowe rozwiązania w języku Python. W efekcie nie tylko zdobędziesz i utrwalisz umiejętności programistyczne, lecz również - co nie mniej ważne - rozwiniesz zdolności w zakresie analizy problemów i wyboru właściwych rozwiązań. Treść zadań maturalnych Analiza problemów Praktyczne rozwiązania Uzasadnienie wybranych technik Typowe pułapki w zadaniach Składnia i instrukcje Pythona Zastosowanie języka w praktyce Naucz się Pythona i zdaj maturę jak prymus!

Python na start! Programowanie dla nastolatków

Michał Wiszniewski

Zacznij od skryptów, a skończysz na szczycie! Pierwsze kroki, czyli od czego zacząć przygodę z Pythonem Niezbędne gadżety, czyli z czego i jak budować programy w Pythonie Wędrówka przez style, czyli jak programować strukturalnie, obiektowo i funkcyjnie Czy kiedykolwiek zastanawiałeś się nad tym, dlaczego na świecie istnieje aż tyle języków programowania i czym różnią się one od siebie? I których z tych języków warto się nauczyć? Na pewno co najmniej kilku, ale na Twojej krótkiej liście nie może zabraknąć Pythona! Ten język ma dwie potężne zalety: po pierwsze, jest znacznie łatwiejszy w nauce i bardziej intuicyjny niż choćby C czy C++, a po drugie, umożliwia tworzenie skryptów, czyli funkcjonalnych programów, przydających się na co dzień programistom wszelkiej maści, administratorom sieci i wielu innym ludziom. Python to także fantastyczne narzędzie do tworzenia rozbudowanych projektów. Zresztą, możesz się o tym szybko przekonać. W tej książce znadziesz omówienie wszystkich najważniejszych funkcji i możliwości Pythona. Zobaczysz, jak pracować w środowisku tego języka, jak używać zmiennych, instrukcji warunkowych, funkcji i typów danych. Zobaczysz, w jakich sytuacjach warto wykorzystywać pętle i moduły, jak wykonywać operacje na plikach i jak obsługiwać wyjątki. Sprawdzisz, do czego przydaje się programowanie strukturalne, a do czego obiektowe, i zobaczysz, jak łatwo połączyć je w Pythonie! Krótko mówiąc, zanim się obejrzysz, zaczniesz samodzielnie pisać zaawansowane skrypty. Sprawdź, programuj i baw się dobrze! Pierwszy program, czyli jak zostałem programistą Zmienne są niezbędne... a zmienne znakowe jeszcze bardziej Operacje wejścia – przekaż skryptowi dowolne informacje Instrukcje warunkowe – początek prawdziwego programowania Funkcje – to jest to! Zasięg zmiennych i nieco więcej o instrukcjach warunkowych – trzeba uważać Pętle – czyli jak ułatwić sobie życie Zaawansowane typy danych – to tylko groźnie brzmi! Operacje na plikach – w końcu ciekawy rozdział! Moduły – one są wspaniałe! Wyjątki – czyli błędy w języku Python Obiekty, klasy i metody – czyli programowanie obiektowe Zaklinaj węża, czyli programuj w Pythonie!

Python Natural Language Processing. Advanced machine learning and deep learning techniques for natural language processing

Jalaj Thanaki

This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them. During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis. You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data.By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world.

Python Natural Language Processing Cookbook. Over 50 recipes to understand, analyze, and generate text for implementing language processing tasks

Zhenya Antić

Python is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization.Starting with an overview of NLP, the book presents recipes for dividing text into sentences, stemming and lemmatization, removing stopwords, and parts of speech tagging to help you to prepare your data. You’ll then learn ways of extracting and representing grammatical information, such as dependency parsing and anaphora resolution, discover different ways of representing the semantics using bag-of-words, TF-IDF, word embeddings, and BERT, and develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you’ll also see how to extract information from text, implement unsupervised and supervised techniques for topic modeling, and perform topic modeling of short texts, such as tweets. Additionally, the book shows you how to develop chatbots using NLTK and Rasa and visualize text data.By the end of this NLP book, you’ll have developed the skills to use a powerful set of tools for text processing.

Python Natural Language Processing Cookbook. Over 60 recipes for building powerful NLP solutions using Python and LLM libraries - Second Edition

Zhenya Antić, Saurabh Chakravarty, Edward A. Fox

Harness the power of Natural Language Processing (NLP) to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess.You’ll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you’ll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You’ll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs.This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust in your NLP models.By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.

Python Network Programming. Conquer all your networking challenges with the powerful Python language

Abhishek Ratan, Eric Chou, Pradeeban Kathiravelu, Dr. M. O. Faruque Sarker

This Learning Path highlights major aspects of Python network programming such as writing simple networking clients, creating and deploying SDN and NFV systems, and extending your network with Mininet. You’ll also learn how to automate legacy and the latest network devices. As you progress through the chapters, you’ll use Python for DevOps and open source tools to test, secure, and analyze your network. Toward the end, you'll develop client-side applications, such as web API clients, email clients, SSH, and FTP, using socket programming.By the end of this Learning Path, you will have learned how to analyze a network's security vulnerabilities using advanced network packet capture and analysis techniques. This Learning Path includes content from the following Packt products:• Practical Network Automation by Abhishek Ratan • Mastering Python Networking by Eric Chou • Python Network Programming Cookbook, Second Edition by Pradeeban Kathiravelu, Dr. M. O. Faruque Sarker