Learn Lightgbm
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Learn Lightgbm
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Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2025-05-08
Learn Lightgbm written by Diego Rodrigues and has been published by StudioD21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-08 with Business & Economics categories.
LEARN LIGHTGBM Build Accurate Models with Scalable Machine Learning This book is ideal for students, data scientists, machine learning engineers, and analysts who want to master LightGBM with practical application in corporate environments. You will learn how to prepare data, optimize hyperparameters, and integrate models with leading market tools such as AWS, Azure, Google Cloud, MLflow, Optuna, and Docker. Explore concepts like boosting, leaf-wise tree growth, GPU acceleration, automated tuning, and cross-platform deployment. Includes: • Installation and configuration of LightGBM on Windows, Linux, and cloud environments • Dataset preparation using Pandas, NumPy, and integration with Spark • Advanced hyperparameter optimization with Optuna and Hyperopt • Experiment tracking and monitoring with MLflow • Production deployment using Flask, FastAPI, Docker, and CI/CD pipelines on AWS and Azure By the end, you will be equipped to build high-performance machine learning models ready to run in enterprise and global cloud solutions. lightgbm, aws, azure, google cloud, mlflow, optuna, docker, ci/cd pipelines, gpu acceleration, machine learning in production
Scikit Learn Xgboost And Lightgbm For Beginners
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Author : Haider Koele
language : en
Publisher: Independently Published
Release Date : 2025-09-04
Scikit Learn Xgboost And Lightgbm For Beginners 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 2025-09-04 with Computers categories.
Unlock the World of Machine Learning-No Experience Required! Do you feel overwhelmed by complex jargon and endless lines of code every time you think about learning machine learning? Have you wondered if you're "tech-savvy enough" to break into AI, or wished for a resource that makes the journey friendly, practical, and genuinely enjoyable? This is the beginner's book you've been searching for. Inside Scikit-Learn, XGBoost, and LightGBM for Beginners, you'll discover a warm, step-by-step guide that demystifies machine learning from the ground up-designed especially for readers with no prior experience. You don't need a computer science degree, advanced math skills, or a background in programming. All you need is curiosity, patience, and the desire to learn by doing. What Makes This Book Different? Gentle, Encouraging Approach: Every chapter is crafted to nurture your confidence and curiosity. Mistakes are normalized, progress is celebrated, and learning feels like an adventure-not a test. Step-by-Step Learning: Follow clear explanations and practical examples, from your very first line of Python code to building real machine learning projects with Scikit-Learn, XGBoost, and LightGBM. Hands-On Projects: Apply what you learn to real-world datasets and scenarios, so you gain skills that are both practical and relevant. Modern, In-Demand Tools: Master three of today's most popular frameworks-Scikit-Learn for accessible machine learning, XGBoost for powerful performance, and LightGBM for cutting-edge speed. No Experience Needed: Written with absolute beginners in mind, with jargon gently explained, plenty of support, and troubleshooting tips at every step. Complete Learning Journey: From setting up your environment, cleaning data, and building models to interpreting results and deploying solutions-you'll walk away with genuine confidence and tangible skills. Key Takeaways: Learn Python-based machine learning in plain language, with zero prior coding required Build, evaluate, and deploy real models using Scikit-Learn, XGBoost, and LightGBM Tackle practical projects that make your learning stick-classification, regression, pipelines, and more Overcome imposter syndrome with a nurturing, supportive tone that makes mistakes part of the process Prepare for real-world jobs, data science interviews, or personal projects with in-demand, industry-tested tools Find your place in the machine learning community-regardless of your background or experience Start Your Machine Learning Journey with Confidence! Whether your dream is to build smarter apps, break into data science, or simply understand the technology shaping our world, this book will guide you every step of the way. Embrace a hands-on, beginner-friendly approach that makes learning machine learning achievable, enjoyable, and even fun. Ready to transform confusion into confidence? Open this book, take your first step, and let your machine learning adventure begin!
Machine Learning With Lightgbm And Python
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Author : Andrich van Wyk
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-09-29
Machine Learning With Lightgbm And Python written by Andrich van Wyk 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 2023-09-29 with Computers categories.
Take your software to the next level and solve real-world data science problems by building production-ready machine learning solutions using LightGBM and Python Key Features Get started with LightGBM, a powerful gradient-boosting library for building ML solutions Apply data science processes to real-world problems through case studies Elevate your software by building machine learning solutions on scalable platforms Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMachine Learning with LightGBM and Python is a comprehensive guide to learning the basics of machine learning and progressing to building scalable machine learning systems that are ready for release. This book will get you acquainted with the high-performance gradient-boosting LightGBM framework and show you how it can be used to solve various machine-learning problems to produce highly accurate, robust, and predictive solutions. Starting with simple machine learning models in scikit-learn, you’ll explore the intricacies of gradient boosting machines and LightGBM. You’ll be guided through various case studies to better understand the data science processes and learn how to practically apply your skills to real-world problems. As you progress, you’ll elevate your software engineering skills by learning how to build and integrate scalable machine-learning pipelines to process data, train models, and deploy them to serve secure APIs using Python tools such as FastAPI. By the end of this book, you’ll be well equipped to use various -of-the-art tools that will help you build production-ready systems, including FLAML for AutoML, PostgresML for operating ML pipelines using Postgres, high-performance distributed training and serving via Dask, and creating and running models in the Cloud with AWS Sagemaker.What you will learn Get an overview of ML and working with data and models in Python using scikit-learn Explore decision trees, ensemble learning, gradient boosting, DART, and GOSS Master LightGBM and apply it to classification and regression problems Tune and train your models using AutoML with FLAML and Optuna Build ML pipelines in Python to train and deploy models with secure and performant APIs Scale your solutions to production readiness with AWS Sagemaker, PostgresML, and Dask Who this book is forThis book is for software engineers aspiring to be better machine learning engineers and data scientists unfamiliar with LightGBM, looking to gain in-depth knowledge of its libraries. Basic to intermediate Python programming knowledge is required to get started with the book. The book is also an excellent source for ML veterans, with a strong focus on ML engineering with up-to-date and thorough coverage of platforms such as AWS Sagemaker, PostgresML, and Dask.
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.
Hands On Machine Learning Techniques
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Author : Dr Benjamin Neudorf
language : en
Publisher: Independently Published
Release Date : 2025-08-24
Hands On Machine Learning Techniques 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 2025-08-24 with Computers categories.
Are you curious about machine learning but feel overwhelmed by technical jargon or afraid to take the first step? You're not alone-and you're exactly who this book was written for. Hands-On Machine Learning Techniques: Scikit-Learn, XGBoost, and LightGBM for Beginners to Advanced is your friendly, step-by-step guide to unlocking the power of data science-no prior experience or advanced math required. Whether you're a complete beginner, an aspiring data scientist, or a developer eager to master modern ML tools, this book will nurture your confidence, celebrate your progress, and empower you to create real-world solutions. What makes this book different? Welcoming, Encouraging Style: Written as a supportive companion, each chapter guides you gently through new concepts-explaining not just the "how," but the "why"-so you always feel included and understood. No Experience Needed: Start from scratch with Python, then grow into advanced machine learning using industry-standard libraries like Scikit-Learn, XGBoost, and LightGBM-all explained in clear, accessible language. Practical, Hands-On Learning: Build real projects, tackle messy data, and solve meaningful problems from the very first chapter. Mistakes are expected and embraced as part of your learning journey. Step-by-Step Examples: Follow concise, up-to-date code samples and workflows you can adapt to your own datasets, with helpful commentary to guide you at every turn. Confidence at Every Level: Move at your own pace through beginner basics, intermediate best practices, and advanced topics like model explainability, deployment, and real-world case studies. Expert Insights and Encouragement: Personal stories and honest advice help you navigate challenges, overcome self-doubt, and build the confidence to keep going-even when technology feels intimidating. Inside, you'll discover: The essential building blocks of machine learning with Python How to prepare, clean, and understand real-world data Powerful modeling techniques using Scikit-Learn, XGBoost, and LightGBM Practical guidance for data preprocessing, feature engineering, and hyperparameter tuning Strategies for interpreting models, addressing bias, and making results explainable How to build complete, end-to-end machine learning pipelines ready for production Deployment tips-share your models with the world using web apps and cloud services Inspiring real-world projects in finance, healthcare, and e-commerce Resources, checklists, and a troubleshooting guide for ongoing support Every chapter is designed to help you succeed-normalizing mistakes, celebrating small wins, and building momentum with each lesson. If you've ever felt left behind or anxious about learning machine learning, this book will be your steady guide. With warmth, clarity, and encouragement, you'll gain not just technical skills but the confidence to use them. Start your hands-on machine learning journey today. Let this book be your companion as you transform curiosity into real-world expertise-one approachable step at a time.
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.
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!
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.
Next Generation Machine Learning With Spark
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Author : Butch Quinto
language : en
Publisher: Apress
Release Date : 2020-04-02
Next Generation Machine Learning With Spark written by Butch Quinto and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-02 with Computers categories.
Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry. Next-Generation Machine Learning with Spark provides a gentle introduction to Spark and Spark MLlib and advances to more powerful, third-party machine learning algorithms and libraries beyond what is available in the standard Spark MLlib library. By the end of this book, you will be able to apply your knowledge to real-world use cases through dozens of practical examples and insightful explanations. What You Will Learn Be introduced to machine learning, Spark, and Spark MLlib 2.4.x Achieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM libraries Detect anomalies with the Isolation Forest algorithm for Spark Use the Spark NLP and Stanford CoreNLP libraries that support multiple languages Optimize your ML workload with the Alluxio in-memory data accelerator for Spark Use GraphX and GraphFrames for Graph Analysis Perform image recognition using convolutional neural networks Utilize the Keras framework and distributed deep learning libraries with Spark Who This Book Is For Data scientists and machine learning engineers who want to take their knowledge to the next level and use Spark and more powerful, next-generation algorithms and libraries beyond what is available in the standard Spark MLlib library; also serves as a primer for aspiring data scientists and engineers who need an introduction to machine learning, Spark, and Spark MLlib.
Lightgbm Techniques And Applications
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-08-19
Lightgbm Techniques And Applications written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-19 with Computers categories.
"LightGBM Techniques and Applications" "LightGBM Techniques and Applications" offers a comprehensive and authoritative exploration of one of the most impactful gradient boosting libraries in modern machine learning. The book opens with a rigorous examination of LightGBM’s theoretical underpinnings and architectural design, delving into its core algorithms, histogram-based learning, memory optimizations, and cross-platform deployment methodologies. Readers are guided through both the foundational concepts and the advanced system integrations, providing a clear understanding of LightGBM’s functionality from the bottom up. Building on this foundation, the book presents a wealth of practical insights into algorithmic enhancements, data engineering, and robust model optimization strategies. It details advanced customization options such as Exclusive Feature Bundling (EFB), Gradient-based One-Side Sampling (GOSS), and native processing for categorical features, alongside state-of-the-art hyperparameter tuning, feature optimization, and interpretability frameworks. Extensive attention is given to distributed training architectures, deployment patterns, and monitoring pipelines, ensuring that practitioners are well-equipped for scalable real-world applications. Rounding out its coverage, the book features diverse case studies in fields such as finance, bioinformatics, IoT, and time series forecasting, illustrating LightGBM's versatility across domains. Additionally, it empowers advanced users with guidance for source code contributions, algorithmic extensions, and integration with emerging ML workflows. "LightGBM Techniques and Applications" is an indispensable resource for data scientists, engineers, and researchers seeking to master both the practical and conceptual facets of modern gradient boosting at scale.