Machine Learning Vol by Patel Rashmi (9 results)

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

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.…

Paperback. Condition: new. Paperback. Machine Learning Volume 1: Foundations, Supervised Learning, and Model EvaluationArtificial Intelligence begins with Machine Learning. Before building deep learning models or deploying production AI systems, every practitioner needs a solid understanding of the principles that make machines learn from data.Machine Learning Volume 1: Foundations, Supervised Learning, and Model Evaluation provides a structured, practical reference for developers, data scientists, AI engineers, students, and technical professionals seeking a clear understanding of modern machine learning.Rather than overwhelming readers with unnecessary theory, this volume focuses on the concepts, algorithms, mathematics, workflows, and evaluation techniques that form the foundation of every successful ML system.Inside this volume, you'll explore: Machine Learning fundamentals and terminologyTypes of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learningEnd-to-end ML workflowData preprocessing and feature engineeringData cleaning and handling missing valuesFeature scaling, normalization, and encodingTraining, validation, and testing strategiesRegression algorithms and practical applicationsClassification algorithms and decision boundariesk-Nearest Neighbors (KNN)Naive BayesDecision TreesRandom Forest fundamentalsLinear and Logistic RegressionBias-Variance tradeoffOverfitting and underfittingCross-validation techniquesHyperparameter tuning fundamentalsPerformance metrics for regressionPerformance metrics for classificationPrecision, Recall, F1-Score, ROC, and AUCConfusion Matrix interpretationModel selection strategiesExplainability basicsReproducible ML workflowsCommon beginner mistakes and practical best practicesDesigned as both a learning resource and a long-term technical reference, this book presents complex topics through clear explanations, practical examples, comparison tables, diagrams, and concise summaries that make difficult concepts easier to understand.Whether you're preparing for interviews, building your first machine learning project, transitioning into AI engineering, or strengthening your technical foundation before moving into deep learning, this volume provides the knowledge needed to progress with confidence.Machine Learning Volume 1 is the first book in the AI/ML Reference Series, a comprehensive collection covering modern machine learning, deep learning, production AI, MLOps, Agentic AI, Generative AI, and real-world intelligent systems.Build the right foundation. Master the core principles. Create machine learning systems 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.…

Paperback. Condition: new. Paperback. Machine Learning Volume 3: Deep Learning, Generative AI, and Production Machine LearningModern Artificial Intelligence is powered by deep learning. From computer vision and natural language processing to generative AI and autonomous systems, today's intelligent applications rely on neural networks capable of learning from massive amounts of data.Machine Learning Volume 3: Deep Learning, Generative AI, and Production Machine Learning is the culmination of the Machine Learning series, taking readers beyond classical algorithms into the technologies driving the latest AI revolution.Designed for AI engineers, machine learning practitioners, software developers, data scientists, researchers, and students, this volume combines theoretical foundations with practical engineering guidance for building, deploying, and maintaining real-world AI systems.Inside this volume, you'll explore: Artificial Neural Networks (ANN)Deep Learning fundamentalsForward and BackpropagationGradient Descent optimizationActivation functionsLoss functionsWeight initializationRegularization techniquesConvolutional Neural Networks (CNN)Recurrent Neural Networks (RNN)LSTM and GRU architecturesSequence modelingAttention mechanismsTransformer architectureLarge Language Models (LLMs)Transfer LearningSelf-Supervised LearningFoundation ModelsDiffusion ModelsGenerative AI fundamentalsVision Transformers (ViT)Autoencoders and Variational Autoencoders (VAE)Generative Adversarial Networks (GANs)Embeddings and vector representationsModel compression and quantizationDistributed trainingGPU accelerationProduction machine learning pipelinesModel deployment strategiesMLOps fundamentalsModel monitoring and drift detectionExplainable AI (XAI)Responsible AI principlesAI system reliability and scalabilityFuture trends in machine learningEvery chapter combines practical workflows, architecture diagrams, comparison tables, mathematical intuition, implementation guidance, and engineering best practices to help readers understand not only how modern AI models work, but also how they are deployed and maintained in production environments.Whether you're building intelligent applications, exploring Generative AI, deploying production-scale machine learning systems, or preparing for advanced AI engineering roles, this volume provides a comprehensive technical reference that bridges research concepts with real-world implementation.Machine Learning Volume 3 completes the AI/ML Reference Series, offering a complete progression from foundational machine learning concepts to advanced deep learning, Generative AI, and production-ready intelligent systems.Master modern AI. Build scalable machine learning systems. Engineer the future 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.…