Items related to Machine Learning Vol 2 (AI and ML Reference handbooks)
Machine Learning Vol 2 (AI and ML Reference handbooks) - Softcover
Book 3 of 22: AI and ML Reference handbooks
Synopsis
Machine Learning Volume 2: Unsupervised Learning, Ensemble Methods, and Model Tuning
Machine learning extends far beyond predicting labels. Modern AI systems must uncover hidden patterns, combine multiple models for greater accuracy, and continuously optimize performance for real-world deployment.
Machine Learning Volume 2: Unsupervised Learning, Ensemble Methods, and Model Tuning builds on the foundations introduced in Volume 1 and explores the advanced techniques that enable robust, scalable, and high-performing machine learning solutions.
Designed for developers, AI engineers, data scientists, students, and technical professionals, this volume bridges the gap between introductory machine learning and production-ready model development through practical explanations, mathematical intuition, and real-world applications.
Inside this volume, you'll explore:
- Unsupervised learning fundamentals
- Clustering algorithms and applications
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Gaussian Mixture Models (GMM)
- Dimensionality reduction techniques
- Principal Component Analysis (PCA)
- t-SNE and UMAP visualization
- Association Rule Mining
- Anomaly and outlier detection
- Feature selection and extraction
- Ensemble learning concepts
- Bagging and Random Forest
- Boosting algorithms
- AdaBoost
- Gradient Boosting
- XGBoost
- LightGBM
- CatBoost
- Stacking and blending techniques
- Voting classifiers
- Hyperparameter optimization
- Grid Search
- Random Search
- Bayesian Optimization
- Automated Machine Learning (AutoML)
- Regularization techniques
- Pipeline construction
- Feature importance analysis
- Model calibration
- Performance optimization strategies
- Model comparison and selection
- Practical workflows for improving prediction accuracy
Throughout the book, concepts are explained using clear illustrations, comparison tables, workflow diagrams, mathematical intuition, and practical examples that help readers understand not only how algorithms work but also when and why they should be applied.
This volume emphasizes the critical engineering decisions involved in selecting algorithms, reducing dimensionality, combining multiple models, and systematically tuning machine learning systems for optimal performance across a wide range of applications.
Whether you're improving predictive accuracy, discovering hidden structures in data, preparing for technical interviews, or building production-grade ML solutions, this book provides the tools and methodologies used by modern AI practitioners.
Machine Learning Volume 2 is the second book in the AI/ML Reference Series, offering a comprehensive progression from machine learning fundamentals to advanced modeling techniques, deep learning, MLOps, production AI, and intelligent autonomous systems.
Unlock deeper insights from your data. Build stronger models. Optimize machine learning with confidence.
"synopsis" may belong to another edition of this title.
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Machine Learning Vol 2
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Machine Learning Vol 2 (Paperback)
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Paperback. Condition: new. Paperback. Machine Learning Volume 2: Unsupervised Learning, Ensemble Methods, and Model TuningMachine learning extends far beyond predicting labels. Modern AI systems must uncover hidden patterns, combine multiple models for greater accuracy, and continuously optimize performance for real-world deployment.Machine Learning Volume 2: Unsupervised Learning, Ensemble Methods, and Model Tuning builds on the foundations introduced in Volume 1 and explores the advanced techniques that enable robust, scalable, and high-performing machine learning solutions.Designed for developers, AI engineers, data scientists, students, and technical professionals, this volume bridges the gap between introductory machine learning and production-ready model development through practical explanations, mathematical intuition, and real-world applications.Inside this volume, you'll explore: Unsupervised learning fundamentalsClustering algorithms and applicationsK-Means ClusteringHierarchical ClusteringDBSCANGaussian Mixture Models (GMM)Dimensionality reduction techniquesPrincipal Component Analysis (PCA)t-SNE and UMAP visualizationAssociation Rule MiningAnomaly and outlier detectionFeature selection and extractionEnsemble learning conceptsBagging and Random ForestBoosting algorithmsAdaBoostGradient BoostingXGBoostLightGBMCatBoostStacking and blending techniquesVoting classifiersHyperparameter optimizationGrid SearchRandom SearchBayesian OptimizationAutomated Machine Learning (AutoML)Regularization techniquesPipeline constructionFeature importance analysisModel calibrationPerformance optimization strategiesModel comparison and selectionPractical workflows for improving prediction accuracyThroughout the book, concepts are explained using clear illustrations, comparison tables, workflow diagrams, mathematical intuition, and practical examples that help readers understand not only how algorithms work but also when and why they should be applied.This volume emphasizes the critical engineering decisions involved in selecting algorithms, reducing dimensionality, combining multiple models, and systematically tuning machine learning systems for optimal performance across a wide range of applications.Whether you're improving predictive accuracy, discovering hidden structures in data, preparing for technical interviews, or building production-grade ML solutions, this book provides the tools and methodologies used by modern AI practitioners.Machine Learning Volume 2 is the second book in the AI/ML Reference Series, offering a comprehensive progression from machine learning fundamentals to advanced modeling techniques, deep learning, MLOps, production AI, and intelligent autonomous systems.Unlock deeper insights from your data. Build stronger models. Optimize machine learning with confidence. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798187852710
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Machine Learning Vol 2 (AI and ML Reference handbooks)
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Machine Learning Vol 2
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Paperback. Condition: new. Paperback. Machine Learning Volume 2: Unsupervised Learning, Ensemble Methods, and Model TuningMachine learning extends far beyond predicting labels. Modern AI systems must uncover hidden patterns, combine multiple models for greater accuracy, and continuously optimize performance for real-world deployment.Machine Learning Volume 2: Unsupervised Learning, Ensemble Methods, and Model Tuning builds on the foundations introduced in Volume 1 and explores the advanced techniques that enable robust, scalable, and high-performing machine learning solutions.Designed for developers, AI engineers, data scientists, students, and technical professionals, this volume bridges the gap between introductory machine learning and production-ready model development through practical explanations, mathematical intuition, and real-world applications.Inside this volume, you'll explore: Unsupervised learning fundamentalsClustering algorithms and applicationsK-Means ClusteringHierarchical ClusteringDBSCANGaussian Mixture Models (GMM)Dimensionality reduction techniquesPrincipal Component Analysis (PCA)t-SNE and UMAP visualizationAssociation Rule MiningAnomaly and outlier detectionFeature selection and extractionEnsemble learning conceptsBagging and Random ForestBoosting algorithmsAdaBoostGradient BoostingXGBoostLightGBMCatBoostStacking and blending techniquesVoting classifiersHyperparameter optimizationGrid SearchRandom SearchBayesian OptimizationAutomated Machine Learning (AutoML)Regularization techniquesPipeline constructionFeature importance analysisModel calibrationPerformance optimization strategiesModel comparison and selectionPractical workflows for improving prediction accuracyThroughout the book, concepts are explained using clear illustrations, comparison tables, workflow diagrams, mathematical intuition, and practical examples that help readers understand not only how algorithms work but also when and why they should be applied.This volume emphasizes the critical engineering decisions involved in selecting algorithms, reducing dimensionality, combining multiple models, and systematically tuning machine learning systems for optimal performance across a wide range of applications.Whether you're improving predictive accuracy, discovering hidden structures in data, preparing for technical interviews, or building production-grade ML solutions, this book provides the tools and methodologies used by modern AI practitioners.Machine Learning Volume 2 is the second book in the AI/ML Reference Series, offering a comprehensive progression from machine learning fundamentals to advanced modeling techniques, deep learning, MLOps, production AI, and intelligent autonomous systems.Unlock deeper insights from your data. Build stronger models. Optimize machine learning with confidence. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798187852710
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Machine Learning Vol 2
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Taschenbuch. Condition: Neu. Neuware - Machine Learning Volume 2: Unsupervised Learning, Ensemble Methods, and Model TuningMachine learning extends far beyond predicting labels. Modern AI systems must uncover hidden patterns, combine multiple models for greater accuracy, and continuously optimize performance for real-world deployment.Machine Learning Volume 2: Unsupervised Learning, Ensemble Methods, and Model Tuning builds on the foundations introduced in Volume 1 and explores the advanced techniques that enable robust, scalable, and high-performing machine learning solutions.Designed for developers, AI engineers, data scientists, students, and technical professionals, this volume bridges the gap between introductory machine learning and production-ready model development through practical explanations, mathematical intuition, and real-world applications.Inside this volume, you'll explore: - Unsupervised learning fundamentals- Clustering algorithms and applications- K-Means Clustering- Hierarchical Clustering- DBSCAN- Gaussian Mixture Models (GMM)- Dimensionality reduction techniques- Principal Component Analysis (PCA)- t-SNE and UMAP visualization- Association Rule Mining- Anomaly and outlier detection- Feature selection and extraction- Ensemble learning concepts- Bagging and Random Forest- Boosting algorithms- AdaBoost- Gradient Boosting- XGBoost- LightGBM- CatBoost- Stacking and blending techniques- Voting classifiers- Hyperparameter optimization- Grid Search- Random Search- Bayesian Optimization- Automated Machine Learning (AutoML)- Regularization techniques- Pipeline construction- Feature importance analysis- Model calibration- Performance optimization strategies- Model comparison and selection- Practical workflows for improving prediction accuracyThroughout the book, concepts are explained using clear illustrations, comparison tables, workflow diagrams, mathematical intuition, and practical examples that help readers understand not only how algorithms work but also when and why they should be applied.This volume emphasizes the critical engineering decisions involved in selecting algorithms, reducing dimensionality, combining multiple models, and systematically tuning machine learning systems for optimal performance across a wide range of applications.Whether you're improving predictive accuracy, discovering hidden structures in data, preparing for technical interviews, or building production-grade ML solutions, this book provides the tools and methodologies used by modern AI practitioners.Machine Learning Volume 2 is the second book in the AI/ML Reference Series, offering a comprehensive progression from machine learning fundamentals to advanced modeling techniques, deep learning, MLOps, production AI, and intelligent autonomous systems.Unlock deeper insights from your data. Build stronger models. Optimize machine learning with confidence. Seller Inventory # 9798187852710
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