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Synopsis
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.
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Classical Machine Learning (eng)
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Classical Machine Learning
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Springer Nature Switzerland, 2026
ISBN 10 : 3032043980
ISBN 13 : 9783032043986
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Classical Machine Learning (Hardcover)
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Springer Nature Switzerland AG, Cham, 2026
ISBN 10 : 3032043980
ISBN 13 : 9783032043986
Neuf
Couverture rigide
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
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Hardcover. Etat : 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. N° de réf. du vendeur 9783032043986
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Classical Machine Learning
Edité par
Springer Nature Switzerland AG, CH, 2026
ISBN 10 : 3032043980
ISBN 13 : 9783032043986
Neuf
Couverture rigide
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
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Hardback. Etat : 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. N° de réf. du vendeur LU-9783032043986
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Classical Machine Learning
Edité par
Springer Nature Switzerland AG, CH, 2026
ISBN 10 : 3032043980
ISBN 13 : 9783032043986
Neuf
Couverture rigide
Vendeur : Rarewaves USA, HEBRON, KY, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Hardback. Etat : 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. N° de réf. du vendeur LU-9783032043986
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Classical Machine Learning: A Practical Guide Using Python
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Classical Machine Learning
Edité par
Springer-Verlag Gmbh Jan 2027, 2027
ISBN 10 : 3032043980
ISBN 13 : 9783032043986
Neuf
Couverture rigide
Vendeur : Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Allemagne
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Buch. Etat : 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. N° de réf. du vendeur 9783032043986
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Classical Machine Learning
Edité par
Springer-Verlag Gmbh Jan 2027, 2027
ISBN 10 : 3032043980
ISBN 13 : 9783032043986
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : 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. N° de réf. du vendeur 9783032043986
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Classical Machine Learning: A Practical Guide Using Python
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Classical Machine Learning
Edité par
Springer-Verlag Gmbh Jan 2027, 2027
ISBN 10 : 3032043980
ISBN 13 : 9783032043986
Neuf
Couverture rigide
Vendeur : Wegmann1855, Zwiesel, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : 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. N° de réf. du vendeur 9783032043986
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