Practical Gradient Boosting
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Practical Gradient Boosting
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Author : Guillaume Saupin
language : en
Publisher: guillaume saupin
Release Date : 2022-11-10
Practical Gradient Boosting written by Guillaume Saupin and has been published by guillaume saupin this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-10 with Computers categories.
This book on Gradient Boosting methods is intended for students, academics, engineers, and data scientists who wish to discover in depth the functioning of this Machine Learning technique used to build decision tree ensembles. All the concepts are illustrated by examples of application code. They allow the reader to rebuild from scratch his own training library of Gradient Boosting methods. In parallel, the book presents the best practices of Data Science and provides the reader with a solid technical background to build Machine Learning models. After a presentation of the principles of Gradient Boosting citing the application cases, advantages and limitations, the reader is introduced to the details of the mathematical theory. A simple implementation is given to illustrate how it works. The reader is then armed to tackle the application and configuration of these methods. Data preparation, training, explanation of a model, management of Hyper Parameter Tuning and use of objective functions are covered in detail! The last chapters of the book extend the subject to the application of Gradient Boosting for time series, the presentation of the emblematic libraries XGBoost, CatBoost and LightGBM as well as the concept of multi-resolution models.
Hands On Gradient Boosting With Python
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Author : Dr Adrian Devlin
language : en
Publisher: Independently Published
Release Date : 2025-12-11
Hands On Gradient Boosting With Python written by Dr Adrian Devlin and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-11 with Computers categories.
Are you curious about machine learning but feel overwhelmed by math, jargon, and complex tutorials? If words like XGBoost, LightGBM, and gradient boosting sound exciting but intimidating, this book is your friendly guide through the noise. Hands-On Gradient Boosting with Python: A Practical Introduction to XGBoost, LightGBM, and the Scikit-Learn Ecosystem is written for complete beginners and self-taught developers who want a clear, step-by-step path into modern Python machine learning-without needing a PhD or years of coding experience. You'll start with the basics of Python, scikit-learn, and tabular data, then gently build up to powerful boosting models used in real-world projects and Kaggle competitions. Every chapter walks you through code line by line, explains why each step matters, and shows you how to avoid common mistakes. Inside, you'll learn how to: Set up your Python machine learning environment with confidence Understand core concepts like decision trees, ensembles, and gradient boosting in plain English Build practical models with scikit-learn, XGBoost, and LightGBM for regression and classification Work on real-world projects such as house price prediction and credit risk scoring Tune hyperparameters, handle imbalanced data, and evaluate models with metrics like AUC, F1, and RMSE Use SHAP and LIME for model explainability so you can trust your predictions Save, load, and deploy your models so they are ready for real applications Throughout the book, you're treated like a learner-not a walking error message. Mistakes are normalized, experiments are encouraged, and every "small win" is celebrated: Clear explanations before any code Gradual progression from simple to advanced models Gentle reminders that confusion is part of learning Practical tips for debugging, improving, and reusing your work Whether you're a student, an aspiring data scientist, or a developer stepping into Python machine learning for the first time, this book becomes your supportive companion-one that makes gradient boosting feel approachable, understandable, and genuinely fun. If you're ready to stop scrolling tutorials and start building real models that actually work, open this book and begin your hands-on journey into gradient boosting with Python today.
Mastering Gradient Boosting
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Author : Dr Benjamin Neudorf
language : en
Publisher: Independently Published
Release Date : 2025-09-16
Mastering Gradient Boosting written by Dr Benjamin Neudorf and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-16 with Computers categories.
Unlock the Power of Modern Machine Learning-No Experience Required Are you fascinated by the buzz around machine learning but feel overwhelmed by the jargon, math, or where to even start? Maybe you've seen words like CatBoost, LightGBM, or XGBoost in tutorials and forums, but every explanation seems written for experts. You're not alone-and you don't need a computer science degree to master these powerful tools. Mastering Gradient Boosting is your friendly, step-by-step guide to conquering three of today's most essential machine learning libraries. Whether you're an absolute beginner or a curious professional, this book welcomes you with open arms-demystifying complex concepts and turning technical obstacles into practical victories. What Makes This Book Different? Instead of intimidating you with formulas or skipping key steps, this book gently guides you from the basics to hands-on mastery: Zero Prerequisites: No advanced math or coding experience required. Every chapter explains terms, breaks down code, and celebrates your progress. Learn by Doing: Build real projects from scratch using Python and today's most in-demand libraries-CatBoost, LightGBM, and XGBoost. Confidence-Building Approach: Each section is designed to reduce anxiety, normalize mistakes, and transform uncertainty into "aha!" moments. Complete Practical Coverage: Install and set up your environment with ease Understand gradient boosting, decision trees, and ensemble learning Train, tune, and evaluate powerful models with clear, bite-sized code Explore real-world case studies in finance, healthcare, and customer analytics Interpret results and deploy models for real impact Key Takeaways You'll Gain: Build high-performance ML models for tabular data-even as a beginner Master model evaluation, hyperparameter tuning, and interpretability (SHAP, LIME, etc.) Develop a robust workflow you can use in Kaggle competitions, job interviews, or your own data projects Gain skills trusted by data scientists, analysts, and tech teams worldwide A Supportive Guide for Lifelong Learners Learning machine learning should be empowering-not intimidating. This book meets you where you are, encourages your curiosity, and helps you turn small wins into big breakthroughs. Each chapter ends with tips, encouragement, and next steps, making the journey enjoyable at every turn. Perfect For: Beginners, students, and career-changers Self-learners eager to build job-ready skills Anyone seeking a supportive introduction to CatBoost, LightGBM, and XGBoost Ready to unlock your potential and master the most in-demand machine learning skills? Start your journey with Mastering Gradient Boosting-and see just how far you can go.
Practical Xgboost
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Author : Everett Higgins
language : en
Publisher: Independently Published
Release Date : 2025-10-25
Practical Xgboost written by Everett Higgins and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-25 with Computers categories.
Practical XGBoost is a hands-on, step-by-step guide for mastering one of the most powerful machine learning algorithms available today. This book shows how to build, tune, and deploy gradient boosting models using Python, making complex concepts approachable and actionable for anyone working with data. Inside, you'll discover how to transform tabular data into predictive models that deliver real results. From handling missing values to engineering meaningful features, optimizing hyperparameters, and scaling models for large datasets, every concept is paired with runnable Python examples that bring learning directly into practice. What this book helps you achieve: Build accurate classification and regression models efficiently using XGBoost. Engineer features and interpret model predictions for clear, trustworthy results. Tune hyperparameters, implement distributed training, and leverage GPU acceleration. Deploy models in real-world pipelines using FastAPI, MLflow, and cloud platforms. Explore practical case studies in finance, healthcare, and energy to see XGBoost applied to real problems. This book stands out by focusing on practical, end-to-end implementation. Every step is explained clearly, with actionable tips and code that works out of the box, bridging the gap between understanding the algorithm and applying it in production-ready systems. Take control of your machine learning projects. Start building faster, smarter, and more accurate models with Practical XGBoost.
Hands On Gradient Boosting With Xgboost And Scikit Learn
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Author : Corey Wade
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-10-16
Hands On Gradient Boosting With Xgboost And Scikit Learn written by Corey Wade and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-16 with Computers categories.
Get to grips with building robust XGBoost models using Python and scikit-learn for deployment Key Features Get up and running with machine learning and understand how to boost models with XGBoost in no time Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results Discover tips and tricks and gain innovative insights from XGBoost Kaggle winners Book Description XGBoost 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 learn Build gradient boosting models from scratch Develop XGBoost regressors and classifiers with accuracy and speed Analyze variance and bias in terms of fine-tuning XGBoost hyperparameters Automatically correct missing values and scale imbalanced data Apply alternative base learners like dart, linear models, and XGBoost random forests Customize transformers and pipelines to deploy XGBoost models Build non-correlated ensembles and stack XGBoost models to increase accuracy Who this book is for This 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.
Practical Xgboost
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Author : Everett Higgins
language : en
Publisher: Independently Published
Release Date : 2025-10-25
Practical Xgboost written by Everett Higgins and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-25 with Computers categories.
Practical XGBoost is a hands-on, step-by-step guide for mastering one of the most powerful machine learning algorithms available today. This book shows how to build, tune, and deploy gradient boosting models using Python, making complex concepts approachable and actionable for anyone working with data. Inside, you'll discover how to transform tabular data into predictive models that deliver real results. From handling missing values to engineering meaningful features, optimizing hyperparameters, and scaling models for large datasets, every concept is paired with runnable Python examples that bring learning directly into practice. What this book helps you achieve: Build accurate classification and regression models efficiently using XGBoost. Engineer features and interpret model predictions for clear, trustworthy results. Tune hyperparameters, implement distributed training, and leverage GPU acceleration. Deploy models in real-world pipelines using FastAPI, MLflow, and cloud platforms. Explore practical case studies in finance, healthcare, and energy to see XGBoost applied to real problems. This book stands out by focusing on practical, end-to-end implementation. Every step is explained clearly, with actionable tips and code that works out of the box, bridging the gap between understanding the algorithm and applying it in production-ready systems. Take control of your machine learning projects. Start building faster, smarter, and more accurate models with Practical XGBoost.
The Gradient Boosting Guidebook
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Author : Haider Koele
language : en
Publisher: Independently Published
Release Date : 2025-11-10
The Gradient Boosting Guidebook written by Haider Koele and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-10 with Computers categories.
Unlock the Power of Machine Learning-No Experience Needed! Are you curious about machine learning, but feel overwhelmed by jargon, complicated code, or fear that it's only for "experts"? The Gradient Boosting Guidebook is your friendly, step-by-step companion, crafted especially for beginners who want to confidently build real-world models using Python's most powerful tools-XGBoost, LightGBM, and Scikit-Learn. Imagine moving from confusion to clarity as you master gradient boosting, one of today's most important and in-demand techniques for data science and AI. Whether you dream of winning a Kaggle competition, landing a data science job, or simply understanding how modern predictions work, this book meets you exactly where you are-no prior programming or math background required. Inside, you'll discover: Crystal-Clear Explanations: Complex concepts like ensemble learning and model tuning are broken down into simple, friendly language anyone can understand. Hands-On Projects: Build practical machine learning solutions step by step, from data preparation and feature engineering to model deployment-perfect for portfolio-building or classroom use. Beginner-Friendly Python Tutorials: Get started fast, with easy instructions for installing and using the core Python ML libraries, even if you've never coded before. Real-World Applications: Work through guided projects that mirror real business and analytics challenges-like credit risk analysis, price prediction, and more. Troubleshooting and Cheat Sheets: Find quick help for common errors and reference guides to speed up your learning, reduce frustration, and celebrate every breakthrough. Supportive Tone: You'll find encouragement at every turn, with stories, tips, and "personal insight" that normalize mistakes and show you that learning is about growth, not perfection. Key Takeaways: Learn how to use gradient boosting to solve real problems with confidence Gain practical experience with XGBoost, LightGBM, and Scikit-Learn Master data cleaning, feature engineering, and hyperparameter tuning Build models that you can explain, deploy, and trust Embrace mistakes as part of the journey and celebrate each small win This isn't just a technical manual-it's your launchpad into the world of data science. If you've ever thought "I'm not technical enough," this guide is here to prove you wrong and show you just how capable you are. Every chapter builds your skills and confidence, guiding you from your very first model to deploying machine learning solutions you'll be proud of. Ready to turn uncertainty into expertise and make your mark in data science? Start your journey with The Gradient Boosting Guidebook and discover how approachable, practical, and empowering machine learning can be!
Artificial Intelligence In Surgery Understanding The Role Of Ai In Surgical Practice
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Author : Daniel A. Hashimoto
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-03-08
Artificial Intelligence In Surgery Understanding The Role Of Ai In Surgical Practice written by Daniel A. Hashimoto and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-08 with Medical categories.
Build a solid foundation in surgical AI with this engaging, comprehensive guide for AI novices Machine learning, neural networks, and computer vision in surgical education, practice, and research will soon be de rigueur. Written for surgeons without a background in math or computer science, Artificial Intelligence in Surgery provides everything you need to evaluate new technologies and make the right decisions about bringing AI into your practice. Comprehensive and easy to understand, this first-of-its-kind resource illustrates the use of AI in surgery through real-life examples. It covers the issues most relevant to your practice, including: Neural Networks and Deep Learning Natural Language Processing Computer Vision Surgical Education and Simulation Preoperative Risk Stratification Intraoperative Video Analysis OR Black Box and Tracking of Intraoperative Events Artificial Intelligence and Robotic Surgery Natural Language Processing for Clinical Documentation Leveraging Artificial Intelligence in the EMR Ethical Implications of Artificial Intelligence in Surgery Artificial Intelligence and Health Policy Assessing Strengths and Weaknesses of Artificial Intelligence Research Finally, the appendix includes a detailed glossary of terms and important learning resources and techniques―all of which helps you interpret claims made by studies or companies using AI.
Manual Of British Water Engineering Practice
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Author : William Oswald Skeat
language : en
Publisher:
Release Date : 1961
Manual Of British Water Engineering Practice written by William Oswald Skeat and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1961 with Sanitary engineering categories.
Practical Machine Learning With Lightgbm And Python
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Author : ROSHAN. BHAVE
language : en
Publisher:
Release Date : 2021-08
Practical Machine Learning With Lightgbm And Python written by ROSHAN. BHAVE and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08 with Machine learning categories.