Orange Book Machine Learning by Carl Mcbride (11 results)

Paperback or Softback. Condition: New. The Orange Book of Machine Learning Green Edition: The essentials of making predictions using supervised regression and classification for tabular dat. Book.

Condition: New.

Paperback. Condition: New. Learn how to use Python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL to build, evaluate, and optimize supervised machine learning models for regression and classification tasks on tabular data.

Paperback. Condition: New. Learn how to use Python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL to build, evaluate, and optimize supervised machine learning models for regression and classification tasks on tabular data.

Paperback. Condition: new. Paperback. Learn supervised machine learning for tabular data using Python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL for regression, classification, and predictive modelingKey FeaturesExplore, clean, and prepare tabular datasets for machine learning workflowsBuild regression and classification models using modern machine learning toolsImprove predictions with calibration, conformal intervals, and optimization techniquesBook DescriptionMaster the essential tools and techniques for supervised machine learning on tabular data with this practical guide to regression and classification. Through clear explanations, code snippets, and hands-on notebooks, you'll learn how to use Python and leading machine learning libraries, including pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL, to build predictive models for real-world datasets.The book covers the complete workflow, from data exploration and cleaning to model development, evaluation, and optimization. You'll learn how to perform regression analysis for accurate point predictions and estimate uncertainty using conformal prediction intervals. For classification tasks, you'll explore probabilistic predictions and calibration techniques to improve model reliability. You'll also discover practical approaches to feature engineering, feature selection, and hyperparameter optimization to enhance model performance. In addition, the book introduces tabular foundation models and in-context learning techniques, providing insight into the latest advances in machine learning for structured data.By the end of the book, you'll have the skills and confidence to develop, evaluate, and deploy supervised machine learning models for a wide range of tabular data applications.What you will learnPerform exploratory data analysis and data cleaningApply cross-validation for reliable model evaluationBuild regression models and prediction intervalsDevelop calibrated probabilistic classification modelsOptimize models through hyperparameter tuningEngineer and select features for improved performanceUse ensemble learning methods effectivelyExplore tabular foundation models and in-context learningWho this book is forThis book is designed for motivated self-learners, university students studying applied machine learning, junior data scientists, and academic researchers looking to incorporate machine learning into their analytical workflows. Readers should have a basic familiarity with Python and data analysis concepts. Whether you're developing predictive models for business, research, or educational purposes, this book provides the practical guidance needed to apply modern machine learning techniques to structured and tabular datasets. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Learn supervised machine learning for tabular data using Python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL for regression, classification, and predictive modelingKey FeaturesExplore, clean, and prepare tabular datasets for machine learning workflowsBuild regression and classification models using modern machine learning toolsImprove predictions with calibration, conformal intervals, and optimization techniquesBook DescriptionMaster the essential tools and techniques for supervised machine learning on tabular data with this practical guide to regression and classification. Through clear explanations, code snippets, and hands-on notebooks, you'll learn how to use Python and leading machine learning libraries, including pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL, to build predictive models for real-world datasets.The book covers the complete workflow, from data exploration and cleaning to model development, evaluation, and optimization. You'll learn how to perform regression analysis for accurate point predictions and estimate uncertainty using conformal prediction intervals. For classification tasks, you'll explore probabilistic predictions and calibration techniques to improve model reliability. You'll also discover practical approaches to feature engineering, feature selection, and hyperparameter optimization to enhance model performance. In addition, the book introduces tabular foundation models and in-context learning techniques, providing insight into the latest advances in machine learning for structured data.By the end of the book, you'll have the skills and confidence to develop, evaluate, and deploy supervised machine learning models for a wide range of tabular data applications.What you will learnPerform exploratory data analysis and data cleaningApply cross-validation for reliable model evaluationBuild regression models and prediction intervalsDevelop calibrated probabilistic classification modelsOptimize models through hyperparameter tuningEngineer and select features for improved performanceUse ensemble learning methods effectivelyExplore tabular foundation models and in-context learningWho this book is forThis book is designed for motivated self-learners, university students studying applied machine learning, junior data scientists, and academic researchers looking to incorporate machine learning into their analytical workflows. Readers should have a basic familiarity with Python and data analysis concepts. Whether you're developing predictive models for business, research, or educational purposes, this book provides the practical guidance needed to apply modern machine learning techniques to structured and tabular datasets. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Learn supervised machine learning for tabular data using Python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL for regression, classification, and predictive modelingKey FeaturesExplore, clean, and prepare tabular datasets for machine learning workflowsBuild regression and classification models using modern machine learning toolsImprove predictions with calibration, conformal intervals, and optimization techniquesBook DescriptionMaster the essential tools and techniques for supervised machine learning on tabular data with this practical guide to regression and classification. Through clear explanations, code snippets, and hands-on notebooks, you'll learn how to use Python and leading machine learning libraries, including pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL, to build predictive models for real-world datasets.The book covers the complete workflow, from data exploration and cleaning to model development, evaluation, and optimization. You'll learn how to perform regression analysis for accurate point predictions and estimate uncertainty using conformal prediction intervals. For classification tasks, you'll explore probabilistic predictions and calibration techniques to improve model reliability. You'll also discover practical approaches to feature engineering, feature selection, and hyperparameter optimization to enhance model performance. In addition, the book introduces tabular foundation models and in-context learning techniques, providing insight into the latest advances in machine learning for structured data.By the end of the book, you'll have the skills and confidence to develop, evaluate, and deploy supervised machine learning models for a wide range of tabular data applications.What you will learnPerform exploratory data analysis and data cleaningApply cross-validation for reliable model evaluationBuild regression models and prediction intervalsDevelop calibrated probabilistic classification modelsOptimize models through hyperparameter tuningEngineer and select features for improved performanceUse ensemble learning methods effectivelyExplore tabular foundation models and in-context learningWho this book is forThis book is designed for motivated self-learners, university students studying applied machine learning, junior data scientists, and academic researchers looking to incorporate machine learning into their analytical workflows. Readers should have a basic familiarity with Python and data analysis concepts. Whether you're developing predictive models for business, research, or educational purposes, this book provides the practical guidance needed to apply modern machine learning techniques to structured and tabular datasets. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn how to use Python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL to build, evaluate, and optimize supervised machine learning models for regression and classification tasks on tabular data.…

Taschenbuch. Condition: Neu. The Orange Book of Machine Learning Green Edition | The essentials of making predictions using supervised regression and classification for tabular data | Carl McBride Ellis | Taschenbuch | Englisch | 2026 | Packt Publishing | EAN 9781808081316 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…