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Python in a Nutshell
by Alex MartelliThis book offers Python programmers one place to look when they need help remembering or deciphering the syntax of this open source language and its many powerful but scantily documented modules. This comprehensive reference guide makes it easy to look up the most frequently needed information--not just about the Python language itself, but also the most frequently used parts of the standard library and the most important third-party extensions. Ask any Python aficionado and you'll hear that Python programmers have it all: an elegant object-oriented language with readable and maintainable syntax, that allows for easy integration with components in C, C++, Java, or C#, and an enormous collection of precoded standard library and third-party extension modules. Moreover, Python is easy to learn, yet powerful enough to take on the most ambitious programming challenges. But what Python programmers used to lack is a concise and clear reference resource, with the appropriate measure of guidance in how best to use Python's great power. Python in a Nutshell fills this need. Python in a Nutshell, Second Edition covers more than the language itself; it also deals with the most frequently used parts of the standard library, and the most popular and important third party extensions. Revised and expanded for Python 2.5, this book now contains the gory details of Python's new subprocess module and breaking news about Microsoft's new IronPython project. Our "Nutshell" format fits Python perfectly by presenting the highlights of the most important modules and functions in its standard library, which cover over 90% of your practical programming needs. This book includes: A fast-paced tutorial on the syntax of the Python language An explanation of object-oriented programming in Python Coverage of iterators, generators, exceptions, modules, packages, strings, and regular expressions A quick reference for Python's built-in types and functions and key modules Reference material on important third-party extensions, such as Numeric and Tkinter Information about extending and embedding Python Python in a Nutshell provides a solid, no-nonsense quick reference to information that programmers rely on the most. This book will immediately earn its place in any Python programmer's library. Praise for the First Edition: "In a nutshell, Python in a Nutshell serves one primary goal: to act as an immediately accessible goal for the Python language. True, you can get most of the same core information that is presented within the covers of this volume online, but this will invariably be broken into multiple files, and in all likelihood lacking the examples or the exact syntax description necessary to truly understand a command." --Richard Cobbett, Linux Format "O'Reilly has several good books, of which Python in a Nutshell by Alex Martelli is probably the best for giving you some idea of what Python is about and how to do useful things with it." --Jerry Pournelle, Byte Magazine
Python in a Nutshell
by Alex MartelliIn the tradition of O'Reilly's "In a Nutshell" series, Python in a Nutshelloffers Python programmers one place to look when they need help remembering or deciphering the syntax of this open source language and its many modules. This comprehensive reference guide makes it easy to look up all the most frequently needed information--not just about the Python language itself, but also the most frequently used parts of the standard library and the most important third-party extensions.
Python in a Nutshell: A Desktop Quick Reference
by Alex Martelli Anna Ravenscroft Steve HoldenUseful in many roles, from design and prototyping to testing, deployment, and maintenance, Python is consistently ranked among today’s most popular programming languages. The third edition of this practical book provides a quick reference to the language—including Python 3.5, 2.7, and highlights of 3.6—commonly used areas of its vast standard library, and some of the most useful third-party modules and packages.Ideal for programmers with some Python experience, and those coming to Python from other programming languages, this book covers a wide range of application areas, including web and network programming, XML handling, database interactions, and high-speed numeric computing. Discover how Python provides a unique mix of elegance, simplicity, practicality, and sheer power.This edition covers:Python syntax, Object-Oriented Python, standard library modules, and third-party Python packagesPython’s support for file and text operations, persistence and databases, concurrent execution, and numeric computationsNetworking basics, event-driven programming, and client-side network protocol modulesPython extension modules, and tools for packaging and distributing extensions, modules, and applications
Python in a Nutshell: A Desktop Quick Reference
by Alex Martelli Anna Martelli Ravenscroft Steve Holden Paul McGuirePython was recently ranked as today's most popular programming language on the TIOBE index, thanks to its broad applicability to design and prototyping to testing, deployment, and maintenance. With this updated fourth edition, you'll learn how to get the most out of Python, whether you're a professional programmer or someone who needs this language to solve problems in a particular field.Carefully curated by recognized experts in Python, this new edition focuses on version 3.10, bringing this seminal work on the Python language fully up to date on five version releases, including preview coverage of upcoming 3.11 features.This handy guide will help you:Learn how Python represents data and program as objectsUnderstand the value and uses of type annotationsExamine which language features appeared in which recent versionsDiscover how to use modern Python idiomaticallyLearn ways to structure Python projects appropriatelyUnderstand how to debug Python code
Python in Easy Steps (G - Reference, Information and Interdisciplinary Subjects Ser.)
by Mike McGrathPython in easy steps, 2nd edition instructs you how to program in the powerful Python language, giving complete examples that illustrate each aspect with colourized source code. Python in easy steps, 2nd edition begins by explaining how to install the free Python interpreter so you can quickly begin to create your own executable programs by copying the book's examples. It demonstrates all the Python language basics before moving on to provide examples of Object Oriented Programming (OOP) and CGI scripting to handle web form data. The book concludes by demonstrating how you can use your acquired knowledge to create and deploy graphical windowed applications.
Python Interviews: Discussions with Python Experts
by Michael DriscollMike Driscoll takes you on a journey talking to a hall-of-fame list of truly remarkable Python experts. You’ll be inspired every time by their passion for the Python language, as they share with you their experiences, contributions, and careers in Python. Key Features Hear from these key Python thinkers about the current status of Python, and where it's heading in the future Listen to their close thoughts on significant Python topics, such as Python's role in scientific computing, and machine learning Understand the direction of Python, and what needs to change for Python 4 Book Description 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 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 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. What you will learn How successful programmers think The history of Python Insights into the minds of the Python core team Trends in Python programmingWho this book is for Python programmers and students interested in the way that Python is used – past and present – with useful anecdotes. It will also be of interest to those looking to gain insights from top programmers.
Python. Leksykon kieszonkowy. Wydanie V
by Mark LutzPodr?czny przewodnik po j?zyku Python!J?zyk Python obecny jest na rynku od ponad 20 lat. Opracowany zosta? na pocz?tku lat dziewi??dziesi?tych XX wieku i b?yskawicznie zacz?? zdobywa? uznanie programistów na ca?ym ?wiecie. Python sprawdza si? doskonale w pisaniu skryptów oraz narz?dzi, a w du?ym projekcie tak?e nie zawiedzie oczekiwa?. J?zyk ten korzysta z automatycznego zarz?dzania pami?ci? oraz umo?liwia obiektowe i funkcyjne podej?cie do tworzonego programu. Jednym z jego najwa?niejszych atutów jest bardzo silna spo?eczno?? programistów, wymieniaj?ca si? na bie??co informacjami na temat praktycznych zastosowa? tego j?zyka. Dzi?ki temu uzyskanie odpowiedzi na trapi?ce Ci? pytania nie powinno stanowi? problemu.Je?eli jednak chcesz mie? zawsze pod r?k? sprawdzone ?ród?o informacji, które pozwoli Ci w ka?dej sytuacji rozwia? w?tpliwo?ci, to trafi?e? na doskona?? pozycj?. Nale?y ona do serii „Leksykon kieszonkowy” i charakteryzuje si? niezwykle zwi?z?ym, przejrzystym uk?adem najwa?niejszych tre?ci oraz por?czn? form?. Znajdziesz tu szczegó?owe informacje na temat typów wbudowanych, wyj?tków, programowania obiektowego oraz przetwarzania nazw i regu? zasi?gu. Kolejne wydanie tej ksi??ki zosta?o ulepszone i zaktualizowane o mnóstwo nowych informacji, takich jak wykorzystanie Python Launcher w systemie Windows czy formalne regu?y dziedziczenia. To doskona?e ?ród?o informacji na temat j?zyka Python!Dzi?ki tej ksi??ce: poznasz podstawy Pythona zapoznasz si? z zasadami programowania w tym j?zyku poznasz typy wbudowane wykorzystasz standardowe modu?y b?dziesz mie? zawsze pod r?k? solidne ?ród?o informacji o PythonieNajlepsze rozwi?zania typowych problemów!
Python lernen in abgeschlossenen Lerneinheiten: Programmieren für Einsteiger mit vielen Beispielen
by Sebastian DörnEin leicht verständliches Buch, um einfach und schnell Python zu lernen Sebastian Dörns Buch „Python lernen in abgeschlossenen Lerneinheiten“ bringt Einsteigern anhand in sich abgeschlossener Lerneinheiten die Grundlagen von und das Programmieren mit Python bei. Zum Inhalt des Buchs gehören folgende Kapitel:· Erste Schritte in Python· Variablen, Ausdrücke und Operatoren · Bedingte Auswahlanweisungen · Iterationen und Schleifen · Funktionen · Reguläre Ausdrücke · Einfache Dateiverarbeitung · Objektorientierte Programmierung Es zeigt Ihnen den Entwurf von effizienten Daten- und Ablaufstrukturen und versetzt Sie dadurch in die Lage, algorithmische Konzepte zu verstehen und in Programmcode umzusetzen. Begreifbare, leicht nachvollziehbare Konzepte und viele anschauliche Programmierbeispiele Das Buch „Python lernen in abgeschlossenen Lerneinheiten“ richtet sich in erster Linie an:a) Studierende und Dozenten b) Schüler und Lehrer Genauso spricht es aber auch alle Programmieranfänger an, die einen schnellen Einstieg in die Programmierung mit Python suchen. Das Werk behandelt die strukturelle Programmierung, die Funktionsweise von Algorithmen, die Grundprinzipien der Objektorientierung und das Verarbeiten von Dateien. Zahlreiche Programmierbeispiele machen die einzelnen Konzepte begreifbar und leicht nachvollziehbar. Die zentralen Lernziele des Buchs „Python lernen in abgeschlossenen Lerneinheiten“ sind das Verstehen der Abstraktionskonzepte moderner Programmiersprachen und das Erlernen des logischen und algorithmischen Denkens. Mit diesem Wissen können Sie im Anschluss selbstständig eigene Computerprogramme implementieren, um damit praxisrelevante Aufgaben schnell und sicher zu bearbeiten.
Python lernen in abgeschlossenen Lerneinheiten: Programmieren für Einsteiger mit vielen Beispielen
by Sebastian DörnEin leicht verständliches Buch, um einfach und schnell Python zu lernenSebastian Dörns Buch „Python lernen in abgeschlossenen Lerneinheiten“ bringt Einsteigern anhand in sich abgeschlossener Lerneinheiten die Grundlagen von und das Programmieren mit Python bei. Zum Inhalt des Buchs gehören folgende Kapitel:Erste Schritte in PythonVariablen, Ausdrücke und Operatoren Bedingte Auswahlanweisungen Iterationen und Schleifen Funktionen Reguläre Ausdrücke Einfache Dateiverarbeitung Objektorientierte Programmierung Es zeigt Ihnen den Entwurf von effizienten Daten- und Ablaufstrukturen und versetzt Sie dadurch in die Lage, algorithmische Konzepte zu verstehen und in Programmcode umzusetzen.Begreifbare, leicht nachvollziehbare Konzepte und viele anschauliche ProgrammierbeispieleDas Buch „Python lernen in abgeschlossenen Lerneinheiten“ richtet sich in erster Linie an:a) Studierende und Dozenten b) Schüler und Lehrer Genauso spricht es aber auch alle Programmieranfänger an, die einen schnellen Einstieg in die Programmierung mit Python suchen. Das Werk behandelt die strukturelle Programmierung, die Funktionsweise von Algorithmen, die Grundprinzipien der Objektorientierung und das Verarbeiten von Dateien. Zahlreiche Programmierbeispiele machen die einzelnen Konzepte begreifbar und leicht nachvollziehbar. Die zentralen Lernziele des Buchs „Python lernen in abgeschlossenen Lerneinheiten“ sind das Verstehen der Abstraktionskonzepte moderner Programmiersprachen und das Erlernen des logischen und algorithmischen Denkens. Mit diesem Wissen können Sie im Anschluss selbstständig eigene Computerprogramme implementieren, um damit praxisrelevante Aufgaben schnell und sicher zu bearbeiten.
Python Machine Learning
by Wei-Meng LeePython makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart—it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand. • Python data science—manipulating data and data visualization • Data cleansing • Understanding Machine learning algorithms • Supervised learning algorithms • Unsupervised learning algorithms • Deploying machine learning models Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level.
Python Machine Learning
by Sebastian RaschkaUnlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book * Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization * Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms * Ask - and answer - tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn * Explore how to use different machine learning models to ask different questions of your data * Learn how to build neural networks using Pylearn 2 and Theano * Find out how to write clean and elegant Python code that will optimize the strength of your algorithms * Discover how to embed your machine learning model in a web application for increased accessibility * Predict continuous target outcomes using regression analysis * Uncover hidden patterns and structures in data with clustering * Organize data using effective pre-processing techniques * Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail 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 Pylearn2, 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. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
by Sebastian Raschka Vahid MirjaliliApplied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.Key FeaturesThird edition of the bestselling, widely acclaimed Python machine learning bookClear and intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practicesBook DescriptionPython 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.What you will learnMaster the frameworks, models, and techniques that enable machines to 'learn' from dataUse scikit-learn for machine learning and TensorFlow for deep learningApply machine learning to image classification, sentiment analysis, intelligent web applications, and moreBuild and train neural networks, GANs, and other modelsDiscover best practices for evaluating and tuning modelsPredict continuous target outcomes using regression analysisDig deeper into textual and social media data using sentiment analysisWho This Book Is ForIf you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.
Python Machine Learning Blueprints: Intuitive data projects you can relate to
by Alexander T. CombsAn approachable guide to applying advanced machine learning methods to everyday problems About This Book * Put machine learning principles into practice to solve real-world problems * Get to grips with Python's impressive range of Machine Learning libraries and frameworks * From retrieving data from APIs to cleaning and visualization, become more confident at tackling every stage of the data pipeline Who This Book Is For Python programmers and data scientists - put your skills to the test with this practical guide dedicated to real-world machine learning that makes a real impact. What You Will Learn * Explore and use Python's impressive machine learning ecosystem * Successfully evaluate and apply the most effective models to problems * Learn the fundamentals of NLP - and put them into practice * Visualize data for maximum impact and clarity * Deploy machine learning models using third party APIs * Get to grips with feature engineering In Detail Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it? Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the development of some awesome machine learning applications and algorithms with real-world examples that demonstrate how to put concepts into practice. You'll learn how to use cluster techniques to discover bargain air fares, and apply linear regression to find yourself a cheap apartment - and much more. Everything you learn is backed by a real-world example, whether its data manipulation or statistical modelling. That way you're never left floundering in theory - you'll be simply collecting and analyzing data in a way that makes a real impact. Style and approach Packed with real-world projects, this book takes you beyond the theory to demonstrate how to apply machine learning techniques to real problems.
Python Machine Learning Blueprints - Second Edition: Put your machine learning concepts to the test by developing real-world smart projects, 2nd Edition
by Alexander CombsThis book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. Implement libraries from Python ecosystem to build a range of projects addressing various machine learning domains. Knowledge of Python programming language and machine learning concepts are recommended.
Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases
by null Yuxi (Hayden) LiuAuthor Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandasKey FeaturesDiscover new and updated content on NLP transformers, PyTorch, and computer vision modelingIncludes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutionsImplement ML models, such as neural networks and linear and logistic regression, from scratchPurchase of the print or Kindle book includes a free PDF copyBook DescriptionThe 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.What you will learnFollow machine learning best practices throughout data preparation and model developmentBuild and improve image classifiers using convolutional neural networks (CNNs) and transfer learningDevelop and fine-tune neural networks using TensorFlow and PyTorchAnalyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIPBuild classifiers using support vector machines (SVMs) and boost performance with PCAAvoid overfitting using regularization, feature selection, and moreWho this book is forThis expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.
Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition
by Yuxi (Hayden) LiuA comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniquesKey FeaturesDive into machine learning algorithms to solve the complex challenges faced by data scientists todayExplore cutting edge content reflecting deep learning and reinforcement learning developmentsUse updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-endBook DescriptionPython 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 Naive 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.What you will learnUnderstand the important concepts in ML and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text analysis and NLP using Python libraries such NLTK and GensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learnWho this book is forIf you're a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you.Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.
Python Machine Learning By Example: Implement Machine Learning Algorithms And Techniques To Build Intelligent Systems, 2nd Edition
by Yuxi Hayden LiuTake tiny steps to enter the big world of data science through this interesting guide About This Book • Learn the fundamentals of machine learning and build your own intelligent applications • Master the art of building your own machine learning systems with this example-based practical guide • Work with important classification and regression algorithms and other machine learning techniques Who This Book Is For This book is for anyone interested in entering the data science stream with machine learning. Basic familiarity with Python is assumed. What You Will Learn • Exploit the power of Python to handle data extraction, manipulation, and exploration techniques • Use Python to visualize data spread across multiple dimensions and extract useful features • Dive deep into the world of analytics to predict situations correctly • Implement machine learning classification and regression algorithms from scratch in Python • Be amazed to see the algorithms in action • Evaluate the performance of a machine learning model and optimize it • Solve interesting real-world problems using machine learning and Python as the journey unfolds In Detail 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. Style and approach This book is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem, and involves hands-on work—giving you a deep insight into the world of machine learning. With simple yet rich language—Python—you will understand and be able to implement the examples with ease.
Python Machine Learning By Example - Second Edition: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition
by Yuxi (Hayden) LiuThis book is for Machine Learning Aspirants, Data Analysts, Data Engineers who are highly passionate about Machine Learning and wants to start getting employed in Machine Learning assignments. Prior knowledge of python coding is assumed and basic familiarity with the statistical concept is beneficial although not a mandate
Python Machine Learning Case Studies
by Danish HaroonEmbrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources. Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You'll see machine learning techniques that you can use to support your products and services. Moreover you'll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs. By taking a step-by-step approach to coding in Python you'll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems. What You Will Learn Gain insights into machine learning concepts Work on real-world applications of machine learning Learn concepts of model selection and optimization Get a hands-on overview of Python from a machine learning point of view Who This Book Is For Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.
Python Machine Learning Cookbook
by Prateek JoshiThis book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code.
Python Machine Learning Cookbook: Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets, 2nd Edition
by Prateek Joshi Giuseppe CiaburroDiscover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch Key Features Learn and implement machine learning algorithms in a variety of real-life scenarios Cover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques Find easy-to-follow code solutions for tackling common and not-so-common challenges Book Description 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. What you will learn Use predictive modeling and apply it to real-world problems Explore data visualization techniques to interact with your data Learn how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Get well versed with reinforcement learning, automated ML, and transfer learning Work with image data and build systems for image recognition and biometric face recognition Use deep neural networks to build an optical character recognition system Who this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful.
Python Machine Learning for Beginners: Learn Machine Learning from scratch with Python
by AI Sciences OUThis course lays the foundations for both a theoretical and practical understanding of machine learning and artificial intelligence, utilizing Python as a beginner-friendly introduction and invitation to further studyKey FeaturesA crash course in Python programmingInteractive, guided practice through a series of machine learning exercisesInstant access to PDFs, Python codes, and exercises from the publisher's website at no extra costBook DescriptionMachine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that's right. Based on a significant amount of data and evidence, it's obvious that ML and AI are here to stay. Consider any industry today. The practical applications of ML are really driving business results. Whether it's healthcare, e-commerce, government, transportation, social media sites, financial services, manufacturing, oil and gas, marketing, and sales. You name it. The list goes on. There's no doubt that ML is going to play a decisive role in every domain in the future. But what does a machine learning professional do? A machine learning specialist develops intelligent algorithms that learn from data and also adapt to the data quickly. Then, these high-end algorithms make accurate predictions. Python Machine Learning for Beginners presents you with a hands-on approach to learn ML fast. You'll learn about data analysis and visualization in great detail in the first half of the book. Then, in the second half, you'll learn about machine learning and statistical models for data science. Each chapter presents you with the theoretical framework behind the different data science and machine learning techniques, and practical examples illustrate the working of these techniques. By the end of this course, you will have a firm grasp on the theoretical foundations of machine learning and artificial intelligence as well as having explored and practiced various real-world applications through Python. The code bundle for this course is available at https://www.aispublishing.net/nlp-crash-course1603576259757What you will learnGet up to speed with Python programmingExplore Python NumPy and Pandas libraries for data analysisPractice data visualization via Matplotlib, Seaborn, and Pandas librariesSolve regression problems in ML using Sklearn librarySolve classification problems in ML using Sklearn libraryStudy data clustering with ML using Sklearn libraryCover deep learning with Python TensorFlow 2.0Perform dimensionality reduction with PCA and LDA using SklearnWho this book is forThis course is specifically designed for those students interested in studying machine learning from its theoretical foundations to advanced applications with Python. No prior experience is required.
Python Machine Learning - Second Edition
by Sebastian Raschka Vahid MirjaliliUnlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book • Second edition of the bestselling book on Machine Learning • A practical approach to key frameworks in data science, machine learning, and deep learning • Use the most powerful Python libraries to implement machine learning and deep learning • Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn • Understand the key frameworks in data science, machine learning, and deep learning • Harness the power of the latest Python open source libraries in machine learning • Explore machine learning techniques using challenging real-world data • Master deep neural network implementation using the TensorFlow library • Learn the mechanics of classification algorithms to implement the best tool for the job • Predict continuous target outcomes using regression analysis • Uncover hidden patterns and structures in data with clustering • Delve deeper into textual and social media data using sentiment analysis In Detail 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. Thoroughly updated using the latest Python 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 deep learning library. The scikit-learn code has also been fully updated to include recent 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 in today's world. If you've read the first edition of this book, you'll be delighted to find a new 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 more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn. Style and Approach Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python.
Python Machine Learning Second Edition
by Sebastian Raschka Vahid MirjaliliUnlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book Second edition of the bestselling book on Machine Learning A practical approach to key frameworks in data science, machine learning, and deep learning Use the most powerful Python libraries to implement machine learning and deep learning Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis In Detail 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. Thoroughly updated using the latest Python 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 deep learning library. The scikit-learn code has also been fully updated to include recent 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 in today's world. If you've read the first edition of this book, you'll be delighted to find a new 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 more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn. Style and Approach Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python.
Python Machine Learning Workbook for Beginners: 10 Machine Learning Projects Explained from Scratch
by AI Sciences OUA practical guide to machine learning with Python through the presentation and guided completion of ten real-world projectsKey FeaturesStep-by-step roadmap to data science and machine learningA Python crash course in machine learning10 machine learning and data science projects for practical studyBook DescriptionMachine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to better understand potential risks. The ever-expanding data, cost-effective data storage, and competitively priced powerful processing continue to drive the growth of ML. This is the best time you could enter the exciting machine learning universe. Industries are reinventing themselves constantly by developing more advanced data analysis models. These models analyze larger and more complex data than ever while delivering instantaneous and more accurate results on enormous scales. In this backdrop, it is evident that hands-on practice is everything in machine learning. Tons of theory will amount to nothing if you don't have enough hands-on practice. Textbooks and online classes mislead you into a false sense of mastery. The easy availability of learning resources tricks you and you become overconfident. But when you try to apply the theoretical concepts you have learned, you realize it's not that simple. This is where projects play a crucial role in your learning journey. Projects are doubtless the best investment of your time. You'll not only enjoy learning but you'll also make quick progress. And unlike studying boring theoretical concepts, you'll find that working on projects is easier to stay motivated. The projects in this book cover ten different interesting topics. Each project will help you refine your ML skills and apply them in the real world. These projects also present you with an opportunity to enrich your portfolio, making it simpler to find a great job, explore interesting career paths, and even negotiate a higher pay package. Overall, this learning-by-doing book will help you accomplish your machine learning career goals faster. The code bundle for this course is available at https://www.aispublishing.net/ai-sciences-bookWhat you will learnHouse price prediction using linear regressionFiltering spam email messages using Naive Bayes algorithmPredicting used car sale price using Feedforward Artificial Neural NetworksPredicting stock market trends with RNN (LSTM)Language translation using Seq2Seq encoder-decoder LSTMClassifying cats and dogs images using Convolutional Neural NetworksMovie recommender system using item-based collaborative filteringFace detection with OpenCV in PythonHandwritten English character recognition with CNNCustomer segmentation based on income and spendingWho this book is forThe scripts, images, and graphs are clear and provide visuals to the text description. If you're new to ML and self-study is your only option, then this book is a must.