Practical Machine Learning With Lightgbm And Python
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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.
Practical Machine Learning For Data Analysis Using Python
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Author : Abdulhamit Subasi
language : en
Publisher: Academic Press
Release Date : 2020-06-05
Practical Machine Learning For Data Analysis Using Python written by Abdulhamit Subasi and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-05 with Computers categories.
Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. - Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas - Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data - Explores important classification and regression algorithms as well as other machine learning techniques - Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features
Practical Machine Learning With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Apress
Release Date : 2017-12-20
Practical Machine Learning With Python written by Dipanjan Sarkar and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-20 with Computers categories.
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries andframeworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
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.
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!
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.
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!
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
Practical Machine Learning
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Author : Sunila Gollapudi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-01-30
Practical Machine Learning written by Sunila Gollapudi 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 2016-01-30 with Computers categories.
Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the future of machine learning Go a step further and integrate your machine learning projects with Hadoop Who This Book Is For This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. What You Will Learn Implement a wide range of algorithms and techniques for tackling complex data Get to grips with some of the most powerful languages in data science, including R, Python, and Julia Harness the capabilities of Spark and Hadoop to manage and process data successfully Apply the appropriate machine learning technique to address real-world problems Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more In Detail Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development. This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data. This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naive Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies. Style and approach A practical data science tutorial designed to give you an insight into the practical application of machine learning, this book takes you through complex concepts and tasks in an accessible way. Featuring information on a wide range of data science techniques, Practical Machine Learning is a comprehensive data science resource.
Practical Deep Learning
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Author : Ronald T. Kneusel
language : en
Publisher: No Starch Press
Release Date : 2021-03-16
Practical Deep Learning written by Ronald T. Kneusel and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-16 with Computers categories.
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about artificial intelligence and machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.