Isbn: 9783032043986 - classical machine learning: a practical guide using python (21 Ergebnisse)

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Hardcover. Zustand: new. Hardcover. The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel dauntingparticularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping todays AI landscape. The authors goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether youre an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey. The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardback. Zustand: New. The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel daunting-particularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping today's AI landscape. The authors' goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether you're an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey.…

Hardback. Zustand: New. The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel daunting-particularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping today's AI landscape. The authors' goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether you're an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey.…

Zustand: New.

Buch. Zustand: Neu. Neuware -The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel daunting-particularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping today's AI landscape. The authors' goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether you're an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey. 312 pp. Englisch.…

Buch. Zustand: Neu. Neuware -The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel daunting-particularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping today's AI landscape. The authors' goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether you're an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey. 312 pp. Englisch.…

Buch. Zustand: Neu. Neuware -The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel daunting-particularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping today's AI landscape. The authors' goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether you're an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey.…

Zustand: NEW.

Zustand: New.

Hardcover. Zustand: Brand New. 300 pages. 9.26x6.11x9.21 inches. In Stock.

Hardback. Zustand: New. The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel daunting-particularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping today's AI landscape. The authors' goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether you're an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey.…

Buch. Zustand: Neu. Classical Machine Learning | A Practical Guide Using Python | Sanad Aburass (u. a.) | Buch | xix | Englisch | 2026 | Springer | EAN 9783032043986 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Buch. Zustand: Neu. Neuware -The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel daunting-particularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping today's AI landscape. The authors' goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether you're an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 312 pp. Englisch.…

Buch. Zustand: Neu. Neuware - The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel daunting-particularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping today's AI landscape. The authors' goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether you're an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey.…

Hardback. Zustand: New. The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel daunting-particularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping today's AI landscape. The authors' goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether you're an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey.…

Buch. Zustand: Neu. Neuware -The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel daunting-particularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping today's AI landscape. The authors' goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether you're an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey. 332 pp. Englisch.…

Zustand: new. Questo è un articolo print on demand.

Hardcover. Zustand: Brand New. 300 pages. 9.26x6.11x9.21 inches. In Stock. This item is printed on demand.

Hardcover. Zustand: new. Hardcover. The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel dauntingparticularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping todays AI landscape. The authors goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether youre an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey. The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Zustand: new. Hardcover. The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel dauntingparticularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping todays AI landscape. The authors goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether youre an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey. The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. 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.…