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Auteur Corey WADE |
Documents disponibles écrits par cet auteur (2)
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The Python Workshop : Write Python code to solve challenging real-world problems / Corey WADE / PACKT PUBLISHING (2022)
Titre : The Python Workshop : Write Python code to solve challenging real-world problems Type de document : e-book Auteurs : Corey WADE Editeur : PACKT PUBLISHING Année de publication : 2022 ISBN/ISSN/EAN : 9781804610619 Note générale : copyrighted Langues : Anglais (eng) Résumé : Gain proficiency, productivity, and power by working on projects and kick-starting your career in Python with this comprehensive, hands-on guide.Key FeaturesUnderstand and utilize Python syntax, objects, methods, and best practicesExplore Python's many features and libraries through real-world problems and big dataUse your newly acquired Python skills in machine learning as well as web and software developmentBook DescriptionPython is among the most popular programming languages in the world. It's ideal for beginners because it's easy to read and write, and for developers, because it's widely available with a strong support community, extensive documentation, and phenomenal libraries – both built-in and user-contributed.This project-based course has been designed by a team of expert authors to get you up and running with Python. You'll work though engaging projects that'll enable you to leverage your newfound Python skills efficiently in technical jobs, personal projects, and job interviews. The book will help you gain an edge in data science, web development, and software development, preparing you to tackle real-world challenges in Python and pursue advanced topics on your own. Throughout the chapters, each component has been explicitly designed to engage and stimulate different parts of the brain so that you can retain and apply what you learn in the practical context with maximum impact.By completing the course from start to finish, you'll walk away feeling capable of tackling any real-world Python development problem.What you will learnWrite efficient and concise functions using core Python methods and librariesBuild classes to address different business needsCreate visual graphs to communicate key data insightsOrganize big data and use machine learning to make regression and classification predictionsDevelop web pages and programs with Python tools and packagesAutomate essential tasks using Python scripts in real-time executionWho this book is forThis book is for professionals, students, and hobbyists who want to learn Python and apply it to solve challenging real-world problems. Although this is a beginner's course, you'll learn more easily if you already have an understanding of standard programming topics like variables, if-else statements, and functions. Experience with another object-oriented program, though not essential, will also be beneficial. If Python is your first attempt at computer programming, this book will help you understand the basics with adequate detail for a motivated student. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88937889 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=562793
Titre : Hands-On Gradient Boosting with XGBoost and scikit-learn Type de document : e-book Auteurs : Corey WADE Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781839218354 Note générale : copyrighted Langues : Anglais (eng) Résumé : Get to grips with building robust XGBoost models using Python and scikit-learn for deploymentKey FeaturesGet up and running with machine learning and understand how to boost models with XGBoost in no timeBuild real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal resultsDiscover tips and tricks and gain innovative insights from XGBoost Kaggle winnersBook DescriptionXGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently.The book introduces machine learning and XGBoost in scikit-learn before building up to the theory behind gradient boosting. You'll cover decision trees and analyze bagging in the machine learning context, learning hyperparameters that extend to XGBoost along the way. You'll build gradient boosting models from scratch and extend gradient boosting to big data while recognizing speed limitations using timers. Details in XGBoost are explored with a focus on speed enhancements and deriving parameters mathematically. With the help of detailed case studies, you'll practice building and fine-tuning XGBoost classifiers and regressors using scikit-learn and the original Python API. You'll leverage XGBoost hyperparameters to improve scores, correct missing values, scale imbalanced datasets, and fine-tune alternative base learners. Finally, you'll apply advanced XGBoost techniques like building non-correlated ensembles, stacking models, and preparing models for industry deployment using sparse matrices, customized transformers, and pipelines.By the end of the book, you'll be able to build high-performing machine learning models using XGBoost with minimal errors and maximum speed.What you will learnBuild gradient boosting models from scratchDevelop XGBoost regressors and classifiers with accuracy and speedAnalyze variance and bias in terms of fine-tuning XGBoost hyperparametersAutomatically correct missing values and scale imbalanced dataApply alternative base learners like dart, linear models, and XGBoost random forestsCustomize transformers and pipelines to deploy XGBoost modelsBuild non-correlated ensembles and stack XGBoost models to increase accuracyWho this book is forThis book is for data science professionals and enthusiasts, data analysts, and developers who want to build fast and accurate machine learning models that scale with big data. Proficiency in Python, along with a basic understanding of linear algebra, will help you to get the most out of this book. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88906437 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=526029
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