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9781804612989 - Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more di Aleksander Molak (21 risultati)
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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
Lingua: Inglese
Editore: Packt Publishing (edition ), 2023
ISBN 10: 1804612987 ISBN 13: 9781804612989
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paperback. Condizione: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!
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paperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
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Causal Inference and Discovery in Python
Lingua: Inglese
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ISBN 10: 1804612987 ISBN 13: 9781804612989
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Aggiungi al carrelloPaperback. Condizione: New. Causal Inference and Discovery in Python is a comprehensive exploration of the theory and techniques at the intersection of modern causality and machine learning. It covers fundamental concepts of Pearlian causal inference, explains the theory, and provides step-by-step code examples for both traditional and advanced causal inference and discovery techniques.
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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more (Paperback or Softback)
Lingua: Inglese
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ISBN 10: 1804612987 ISBN 13: 9781804612989
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Paperback or Softback. Condizione: New. Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more. Book.
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Causal Inference and Discovery in Python
Lingua: Inglese
Editore: Packt Publishing Limited, GB, 2023
ISBN 10: 1804612987 ISBN 13: 9781804612989
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Aggiungi al carrelloPaperback. Condizione: New. Causal Inference and Discovery in Python is a comprehensive exploration of the theory and techniques at the intersection of modern causality and machine learning. It covers fundamental concepts of Pearlian causal inference, explains the theory, and provides step-by-step code examples for both traditional and advanced causal inference and discovery techniques.
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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
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Causal Inference and Discovery in Python
Lingua: Inglese
Editore: Packt Publishing Limited, GB, 2023
ISBN 10: 1804612987 ISBN 13: 9781804612989
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Aggiungi al carrelloPaperback. Condizione: New. Causal Inference and Discovery in Python is a comprehensive exploration of the theory and techniques at the intersection of modern causality and machine learning. It covers fundamental concepts of Pearlian causal inference, explains the theory, and provides step-by-step code examples for both traditional and advanced causal inference and discovery techniques.
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Causal Inference and Discovery in Python
Lingua: Inglese
Editore: Packt Publishing Limited, GB, 2023
ISBN 10: 1804612987 ISBN 13: 9781804612989
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Aggiungi al carrelloPaperback. Condizione: New. Causal Inference and Discovery in Python is a comprehensive exploration of the theory and techniques at the intersection of modern causality and machine learning. It covers fundamental concepts of Pearlian causal inference, explains the theory, and provides step-by-step code examples for both traditional and advanced causal inference and discovery techniques.
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Causal Inference and Discovery in Python
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Causal Inference and Discovery in Python
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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental dataPurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey Features Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more Discover modern causal inference techniques for average and heterogenous treatment effect estimation Explore and leverage traditional and modern causal discovery methodsBook DescriptionCausal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.You'll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you'll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you'll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You'll further explore the mechanics of how 'causes leave traces' and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.What you will learn Master the fundamental concepts of causal inference Decipher the mysteries of structural causal models Unleash the power of the 4-step causal inference process in Python Explore advanced uplift modeling techniques Unlock the secrets of modern causal discovery using Python Use causal inference for social impact and community benefitWho this book is forThis book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who've worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.Table of Contents Causality - Hey, We Have Machine Learning, So Why Even Bother Judea Pearl and the Ladder of Causation Regression, Observations, and Interventions Graphical Models Forks, Chains, and Immoralities Nodes, Edges, and Statistical (In)dependence The Four-Step Process of Causal Inference Causal Models - Assumptions and Challenges Causal Inference and Machine Learning - from Matching to Meta- Learners Causal Inference and Machine Learning - Advanced Estimators, Experiments, Evaluations, and More Causal Inference and Machine Learning - Deep Learning, NLP, and Beyond Can I Have a Causal Graph, Please (N.B. Please use the Read Sample option to see further chapters).
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Causal Inference and Discovery in Python | Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more | Aleksander Molak | Taschenbuch | Englisch | 2023 | Packt Publishing | EAN 9781804612989 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
