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Hardcover. Condizione: new. Hardcover. Cloud Data Science Mastery: Unlocking Snowflake's Power with Spark, R, Qubole, and PythonCloud Data Science Mastery is an advanced, comprehensive guide designed to transform the way readers understand and apply cloud-based data science. It explores the integration of powerful tools-Snowflake, Spark, R, Qubole, and Python-to build intelligent, scalable, and secure data-driven solutions.This book takes readers on a complete journey through modern cloud data science, from foundational principles to cutting-edge AI applications. It begins by unpacking the fundamentals of cloud computing and Snowflake's architecture-covering its unique separation of storage and compute, virtual warehouses, elastic scaling, and secure data sharing. Readers learn how to set up Snowflake environments, manage data pipelines, and optimize performance for high-demand analytics.In the next stage, Cloud Data Science Mastery delves into advanced analytics and machine learning. It demonstrates how to integrate Apache Spark and Qubole with Snowflake to execute large-scale data transformations, perform distributed analytics, and enable real-time insights. Using R and Python, the book guides readers through model training, feature engineering, and deployment with Snowpark ML and Model Registry-empowering them to build predictive and generative AI models within Snowflake's unified ecosystem.The book also unveils the use of Snowflake Cortex and Streamlit for building AI-driven applications, predictive models, and interactive dashboards. It provides detailed performance optimization strategies, including query tuning, resource management, and cost-efficient scaling. Additionally, readers gain practical mastery of data security, encryption, auditing, and compliance with international standards such as GDPR, HIPAA, and SOC 2.Each chapter balances theory with practical, hands-on examples and real-world case studies from industries like finance, healthcare, retail, and energy. Through these examples, readers see how cloud-based data science revolutionizes operations-enabling fraud detection, predictive healthcare diagnostics, demand forecasting, and precision agriculture.Cloud Data Science Mastery stands out for its clarity, depth, and actionable insights. It is written for data scientists, analysts, engineers, and students eager to leverage the full power of the cloud for innovation and competitive advantage.By the end, readers will have gained: Proficiency in Snowflake's cloud-native data architecture and how to integrate it with complementary tools.Mastery of data integration and transformation techniques using Spark and Qubole.Advanced skills in cloud-based machine learning and generative AI with R, Python, and Snowpark ML.Expertise in optimizing performance, scalability, and cost efficiency in large data environments.A strong command of data security, privacy, and compliance frameworks essential for enterprise-level applications.With its combination of conceptual precision, practical exercises, and visionary insight, Cloud Data Science Mastery is an indispensable resource for anyone seeking to excel in the rapidly evolving field of cloud computing and data science. It not only equips readers with the technical skills to thrive but also provides the strategic understanding to drive innovation through data intelligence in the modern digital era. A practical guide to mastering cloud-based data science using Snowflake, Spark, R, Qubole, and Python. Learn architecture, data integration, machine learning, optimization, and security to build scalable, intelligent cloud data solutions. This item is printed on dema Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condizione: new. Hardcover. Cloud Data Science Mastery: Unlocking Snowflake's Power with Spark, R, Qubole, and PythonCloud Data Science Mastery is an advanced, comprehensive guide designed to transform the way readers understand and apply cloud-based data science. It explores the integration of powerful tools-Snowflake, Spark, R, Qubole, and Python-to build intelligent, scalable, and secure data-driven solutions.This book takes readers on a complete journey through modern cloud data science, from foundational principles to cutting-edge AI applications. It begins by unpacking the fundamentals of cloud computing and Snowflake's architecture-covering its unique separation of storage and compute, virtual warehouses, elastic scaling, and secure data sharing. Readers learn how to set up Snowflake environments, manage data pipelines, and optimize performance for high-demand analytics.In the next stage, Cloud Data Science Mastery delves into advanced analytics and machine learning. It demonstrates how to integrate Apache Spark and Qubole with Snowflake to execute large-scale data transformations, perform distributed analytics, and enable real-time insights. Using R and Python, the book guides readers through model training, feature engineering, and deployment with Snowpark ML and Model Registry-empowering them to build predictive and generative AI models within Snowflake's unified ecosystem.The book also unveils the use of Snowflake Cortex and Streamlit for building AI-driven applications, predictive models, and interactive dashboards. It provides detailed performance optimization strategies, including query tuning, resource management, and cost-efficient scaling. Additionally, readers gain practical mastery of data security, encryption, auditing, and compliance with international standards such as GDPR, HIPAA, and SOC 2.Each chapter balances theory with practical, hands-on examples and real-world case studies from industries like finance, healthcare, retail, and energy. Through these examples, readers see how cloud-based data science revolutionizes operations-enabling fraud detection, predictive healthcare diagnostics, demand forecasting, and precision agriculture.Cloud Data Science Mastery stands out for its clarity, depth, and actionable insights. It is written for data scientists, analysts, engineers, and students eager to leverage the full power of the cloud for innovation and competitive advantage.By the end, readers will have gained: Proficiency in Snowflake's cloud-native data architecture and how to integrate it with complementary tools.Mastery of data integration and transformation techniques using Spark and Qubole.Advanced skills in cloud-based machine learning and generative AI with R, Python, and Snowpark ML.Expertise in optimizing performance, scalability, and cost efficiency in large data environments.A strong command of data security, privacy, and compliance frameworks essential for enterprise-level applications.With its combination of conceptual precision, practical exercises, and visionary insight, Cloud Data Science Mastery is an indispensable resource for anyone seeking to excel in the rapidly evolving field of cloud computing and data science. It not only equips readers with the technical skills to thrive but also provides the strategic understanding to drive innovation through data intelligence in the modern digital era. A practical guide to mastering cloud-based data science using Snowflake, Spark, R, Qubole, and Python. Learn architecture, data integration, machine learning, optimization, and security to build scalable, intelligent cloud data solutions. This item is pr Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Condizione: new. Hardcover. Cloud Data Science Mastery: Unlocking Snowflake's Power with Spark, R, Qubole, and PythonCloud Data Science Mastery is an advanced, comprehensive guide designed to transform the way readers understand and apply cloud-based data science. It explores the integration of powerful tools-Snowflake, Spark, R, Qubole, and Python-to build intelligent, scalable, and secure data-driven solutions.This book takes readers on a complete journey through modern cloud data science, from foundational principles to cutting-edge AI applications. It begins by unpacking the fundamentals of cloud computing and Snowflake's architecture-covering its unique separation of storage and compute, virtual warehouses, elastic scaling, and secure data sharing. Readers learn how to set up Snowflake environments, manage data pipelines, and optimize performance for high-demand analytics.In the next stage, Cloud Data Science Mastery delves into advanced analytics and machine learning. It demonstrates how to integrate Apache Spark and Qubole with Snowflake to execute large-scale data transformations, perform distributed analytics, and enable real-time insights. Using R and Python, the book guides readers through model training, feature engineering, and deployment with Snowpark ML and Model Registry-empowering them to build predictive and generative AI models within Snowflake's unified ecosystem.The book also unveils the use of Snowflake Cortex and Streamlit for building AI-driven applications, predictive models, and interactive dashboards. It provides detailed performance optimization strategies, including query tuning, resource management, and cost-efficient scaling. Additionally, readers gain practical mastery of data security, encryption, auditing, and compliance with international standards such as GDPR, HIPAA, and SOC 2.Each chapter balances theory with practical, hands-on examples and real-world case studies from industries like finance, healthcare, retail, and energy. Through these examples, readers see how cloud-based data science revolutionizes operations-enabling fraud detection, predictive healthcare diagnostics, demand forecasting, and precision agriculture.Cloud Data Science Mastery stands out for its clarity, depth, and actionable insights. It is written for data scientists, analysts, engineers, and students eager to leverage the full power of the cloud for innovation and competitive advantage.By the end, readers will have gained: Proficiency in Snowflake's cloud-native data architecture and how to integrate it with complementary tools.Mastery of data integration and transformation techniques using Spark and Qubole.Advanced skills in cloud-based machine learning and generative AI with R, Python, and Snowpark ML.Expertise in optimizing performance, scalability, and cost efficiency in large data environments.A strong command of data security, privacy, and compliance frameworks essential for enterprise-level applications.With its combination of conceptual precision, practical exercises, and visionary insight, Cloud Data Science Mastery is an indispensable resource for anyone seeking to excel in the rapidly evolving field of cloud computing and data science. It not only equips readers with the technical skills to thrive but also provides the strategic understanding to drive innovation through data intelligence in the modern digital era. A practical guide to mastering cloud-based data science using Snowflake, Spark, R, Qubole, and Python. Learn architecture, data integration, machine learning, optimization, and security to build scalable, intelligent cloud data solutions. This item is pr Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condizione: new. Hardcover. Unlock the Future of Intelligence - Where Quantum Computing Meets Machine Learning.The Power of Quantum Machine Learning by Timothy Ugbaja is a groundbreaking exploration of how quantum mechanics and artificial intelligence converge to redefine computation, prediction, optimization, and data-driven decision-making. This book demystifies one of the most revolutionary intersections in modern science-revealing how quantum algorithms can dramatically accelerate learning, pattern recognition, and complex data analysis beyond the limits of classical computing.Designed for data scientists, AI researchers, developers, and forward-thinking engineers, this comprehensive volume bridges the gap between theoretical quantum principles and practical engineering applications. It integrates detailed explanations with hands-on experimentation using today's most advanced frameworks: TensorFlow Quantum, Qiskit, PennyLane, and Amazon Braket.Readers will learn how to design, train, and deploy quantum-enhanced machine learning models using hybrid architectures that unite classical and quantum computing. Each chapter introduces real-world use cases, clear code samples, and cross-platform demonstrations that simplify the complexities of building, optimizing, and scaling quantum models.You'll explore the foundations of quantum data encoding, quantum kernels, variational circuits, and quantum neural networks, gaining the tools to build models that leverage quantum phenomena-superposition, entanglement, and interference-to unlock unprecedented computational potential.Inside this book, you'll learn to: Integrate TensorFlow Quantum with classical ML pipelines for hybrid quantum learning.Apply Qiskit to perform quantum feature encoding, kernel estimation, and classification tasks.Use PennyLane for differentiable quantum programming and deep learning synergy.Run scalable experiments on Amazon Braket's cloud-based quantum hardware.Implement practical examples in optimization, reinforcement learning, and generative models.Beyond algorithms and code, this book also explores the philosophy of quantum intelligence-how learning systems evolve when built on quantum logic instead of classical rules. Through accessible explanations and a systematic, application-driven approach, Timothy Ugbaja empowers readers to harness the next wave of computational evolution.The Power of Quantum Machine Learning is more than a technical reference-it is a visionary guide to the quantum-AI frontier, showing how today's innovators can begin building tomorrow's intelligent systems. Whether you are a Ph.D. student, researcher, data scientist, or technology strategist, this book provides the clarity, structure, and insight needed to lead in the new era of computational intelligence. The Power of Quantum Machine Learning blends theory and practice across TensorFlow Quantum, Qiskit, PennyLane, and Amazon Braket-guiding readers to build scalable AI systems powered by qubits for the next era of intelligence. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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

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

Hardcover. Condizione: new. Hardcover. Unlock the Future of Intelligence - Where Quantum Computing Meets Machine Learning.The Power of Quantum Machine Learning by Timothy Ugbaja is a groundbreaking exploration of how quantum mechanics and artificial intelligence converge to redefine computation, prediction, optimization, and data-driven decision-making. This book demystifies one of the most revolutionary intersections in modern science-revealing how quantum algorithms can dramatically accelerate learning, pattern recognition, and complex data analysis beyond the limits of classical computing.Designed for data scientists, AI researchers, developers, and forward-thinking engineers, this comprehensive volume bridges the gap between theoretical quantum principles and practical engineering applications. It integrates detailed explanations with hands-on experimentation using today's most advanced frameworks: TensorFlow Quantum, Qiskit, PennyLane, and Amazon Braket.Readers will learn how to design, train, and deploy quantum-enhanced machine learning models using hybrid architectures that unite classical and quantum computing. Each chapter introduces real-world use cases, clear code samples, and cross-platform demonstrations that simplify the complexities of building, optimizing, and scaling quantum models.You'll explore the foundations of quantum data encoding, quantum kernels, variational circuits, and quantum neural networks, gaining the tools to build models that leverage quantum phenomena-superposition, entanglement, and interference-to unlock unprecedented computational potential.Inside this book, you'll learn to: Integrate TensorFlow Quantum with classical ML pipelines for hybrid quantum learning.Apply Qiskit to perform quantum feature encoding, kernel estimation, and classification tasks.Use PennyLane for differentiable quantum programming and deep learning synergy.Run scalable experiments on Amazon Braket's cloud-based quantum hardware.Implement practical examples in optimization, reinforcement learning, and generative models.Beyond algorithms and code, this book also explores the philosophy of quantum intelligence-how learning systems evolve when built on quantum logic instead of classical rules. Through accessible explanations and a systematic, application-driven approach, Timothy Ugbaja empowers readers to harness the next wave of computational evolution.The Power of Quantum Machine Learning is more than a technical reference-it is a visionary guide to the quantum-AI frontier, showing how today's innovators can begin building tomorrow's intelligent systems. Whether you are a Ph.D. student, researcher, data scientist, or technology strategist, this book provides the clarity, structure, and insight needed to lead in the new era of computational intelligence. The Power of Quantum Machine Learning blends theory and practice across TensorFlow Quantum, Qiskit, PennyLane, and Amazon Braket-guiding readers to build scalable AI systems powered by qubits for the next era of intelligence. 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.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 292 pp. Englisch.

Hardcover. Condizione: new. Hardcover. Unlock the Future of Intelligence - Where Quantum Computing Meets Machine Learning.The Power of Quantum Machine Learning by Timothy Ugbaja is a groundbreaking exploration of how quantum mechanics and artificial intelligence converge to redefine computation, prediction, optimization, and data-driven decision-making. This book demystifies one of the most revolutionary intersections in modern science-revealing how quantum algorithms can dramatically accelerate learning, pattern recognition, and complex data analysis beyond the limits of classical computing.Designed for data scientists, AI researchers, developers, and forward-thinking engineers, this comprehensive volume bridges the gap between theoretical quantum principles and practical engineering applications. It integrates detailed explanations with hands-on experimentation using today's most advanced frameworks: TensorFlow Quantum, Qiskit, PennyLane, and Amazon Braket.Readers will learn how to design, train, and deploy quantum-enhanced machine learning models using hybrid architectures that unite classical and quantum computing. Each chapter introduces real-world use cases, clear code samples, and cross-platform demonstrations that simplify the complexities of building, optimizing, and scaling quantum models.You'll explore the foundations of quantum data encoding, quantum kernels, variational circuits, and quantum neural networks, gaining the tools to build models that leverage quantum phenomena-superposition, entanglement, and interference-to unlock unprecedented computational potential.Inside this book, you'll learn to: Integrate TensorFlow Quantum with classical ML pipelines for hybrid quantum learning.Apply Qiskit to perform quantum feature encoding, kernel estimation, and classification tasks.Use PennyLane for differentiable quantum programming and deep learning synergy.Run scalable experiments on Amazon Braket's cloud-based quantum hardware.Implement practical examples in optimization, reinforcement learning, and generative models.Beyond algorithms and code, this book also explores the philosophy of quantum intelligence-how learning systems evolve when built on quantum logic instead of classical rules. Through accessible explanations and a systematic, application-driven approach, Timothy Ugbaja empowers readers to harness the next wave of computational evolution.The Power of Quantum Machine Learning is more than a technical reference-it is a visionary guide to the quantum-AI frontier, showing how today's innovators can begin building tomorrow's intelligent systems. Whether you are a Ph.D. student, researcher, data scientist, or technology strategist, this book provides the clarity, structure, and insight needed to lead in the new era of computational intelligence. The Power of Quantum Machine Learning blends theory and practice across TensorFlow Quantum, Qiskit, PennyLane, and Amazon Braket-guiding readers to build scalable AI systems powered by qubits for the next era of intelligence. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Unlock the Future of Intelligence - Where Quantum Computing Meets Machine Learning.The Power of Quantum Machine Learning by Timothy Ugbaja is a groundbreaking exploration of how quantum mechanics and artificial intelligence converge to redefine computation, prediction, optimization, and data-driven decision-making. This book demystifies one of the most revolutionary intersections in modern science-revealing how quantum algorithms can dramatically accelerate learning, pattern recognition, and complex data analysis beyond the limits of classical computing.Designed for data scientists, AI researchers, developers, and forward-thinking engineers, this comprehensive volume bridges the gap between theoretical quantum principles and practical engineering applications. It integrates detailed explanations with hands-on experimentation using today's most advanced frameworks: TensorFlow Quantum, Qiskit, PennyLane, and Amazon Braket.Readers will learn how to design, train, and deploy quantum-enhanced machine learning models using hybrid architectures that unite classical and quantum computing. Each chapter introduces real-world use cases, clear code samples, and cross-platform demonstrations that simplify the complexities of building, optimizing, and scaling quantum models.You'll explore the foundations of quantum data encoding, quantum kernels, variational circuits, and quantum neural networks, gaining the tools to build models that leverage quantum phenomena-superposition, entanglement, and interference-to unlock unprecedented computational potential.Inside this book, you'll learn to:Integrate TensorFlow Quantum with classical ML pipelines for hybrid quantum learning.Apply Qiskit to perform quantum feature encoding, kernel estimation, and classification tasks.Use PennyLane for differentiable quantum programming and deep learning synergy.Run scalable experiments on Amazon Braket's cloud-based quantum hardware.Implement practical examples in optimization, reinforcement learning, and generative models.Beyond algorithms and code, this book also explores the philosophy of quantum intelligence-how learning systems evolve when built on quantum logic instead of classical rules. Through accessible explanations and a systematic, application-driven approach, Timothy Ugbaja empowers readers to harness the next wave of computational evolution.The Power of Quantum Machine Learning is more than a technical reference-it is a visionary guide to the quantum-AI frontier, showing how today's innovators can begin building tomorrow's intelligent systems. Whether you are a Ph.D. student, researcher, data scientist, or technology strategist, this book provides the clarity, structure, and insight needed to lead in the new era of computational intelligence.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 292 pp. Englisch.

Buch. Condizione: Neu. The Power of Quantum Machine Learning | WITH TENSORFLOW QUANTUM, QISKIT, PENNYLANE, AND AMAZON BRAKET: FROM ALGORITHMS TO APPLICATIONS | Timothy Ugbaja | Buch | Englisch | 2025 | Tilaverse Inc. | EAN 9798349653261 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Unlock the Future of Intelligence - Where Quantum Computing Meets Machine Learning.The Power of Quantum Machine Learning by Timothy Ugbaja is a groundbreaking exploration of how quantum mechanics and artificial intelligence converge to redefine computation, prediction, optimization, and data-driven decision-making. This book demystifies one of the most revolutionary intersections in modern science-revealing how quantum algorithms can dramatically accelerate learning, pattern recognition, and complex data analysis beyond the limits of classical computing.Designed for data scientists, AI researchers, developers, and forward-thinking engineers, this comprehensive volume bridges the gap between theoretical quantum principles and practical engineering applications. It integrates detailed explanations with hands-on experimentation using today's most advanced frameworks: TensorFlow Quantum, Qiskit, PennyLane, and Amazon Braket.Readers will learn how to design, train, and deploy quantum-enhanced machine learning models using hybrid architectures that unite classical and quantum computing. Each chapter introduces real-world use cases, clear code samples, and cross-platform demonstrations that simplify the complexities of building, optimizing, and scaling quantum models.You'll explore the foundations of quantum data encoding, quantum kernels, variational circuits, and quantum neural networks, gaining the tools to build models that leverage quantum phenomena-superposition, entanglement, and interference-to unlock unprecedented computational potential.Inside this book, you'll learn to:Integrate TensorFlow Quantum with classical ML pipelines for hybrid quantum learning.Apply Qiskit to perform quantum feature encoding, kernel estimation, and classification tasks.Use PennyLane for differentiable quantum programming and deep learning synergy.Run scalable experiments on Amazon Braket's cloud-based quantum hardware.Implement practical examples in optimization, reinforcement learning, and generative models.Beyond algorithms and code, this book also explores the philosophy of quantum intelligence-how learning systems evolve when built on quantum logic instead of classical rules. Through accessible explanations and a systematic, application-driven approach, Timothy Ugbaja empowers readers to harness the next wave of computational evolution.The Power of Quantum Machine Learning is more than a technical reference-it is a visionary guide to the quantum-AI frontier, showing how today's innovators can begin building tomorrow's intelligent systems. Whether you are a Ph.D. student, researcher, data scientist, or technology strategist, this book provides the clarity, structure, and insight needed to lead in the new era of computational intelligence.

Buch. Condizione: Neu. CLOUD DATA SCIENCE MASTERY | UNLOCKING SNOWFLAKE'S POWER WITH SPARK, R, QUBOLE AND PYTHON | Timothy Ugbaja | Buch | Englisch | 2025 | Tilaverse Inc. | EAN 9798349690655 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.