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Artículo: AMZ-B0C8Z3NMT8
A Handbook of Mathematical Models with Python: Elevate your machine learning projects with NetworkX, PuLP, and linalg
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Kindle
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0.20 kg
0.20 kg
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- Master the art of mathematical modeling through practical examples, use cases, and machine learning techniquesKey FeaturesGain a profound understanding of various mathematical models that can be integrated with machine learningLearn how to implement optimization algorithms to tune machine learning modelsBuild optimal solutions for practical use casesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMathematical modeling is the art of transforming a business problem into a well-defined mathematical formulation. Its emphasis on interpretability is particularly crucial when deploying a model to support high-stake decisions in sensitive sectors like pharmaceuticals and healthcare. Through this book, you’ll gain a firm grasp of the foundational mathematics underpinning various machine learning algorithms. Equipped with this knowledge, you can modify algorithms to suit your business problem. Starting with the basic theory and concepts of mathematical modeling, you’ll explore an array of mathematical tools that will empower you to extract insights and understand the data better, which in turn will aid in making optimal, data-driven decisions. The book allows you to explore mathematical optimization and its wide range of applications, and concludes by highlighting the synergetic value derived from blending mathematical models with machine learning. Ultimately, you’ll be able to apply everything you’ve learned to choose the most fitting methodologies for the business problems you encounter.What you will learnUnderstand core concepts of mathematical models and their relevance in solving problemsExplore various approaches to modeling and learning using PythonWork with tested mathematical tools to gather meaningful insightsBlend mathematical modeling with machine learning to find optimal solutions to business problemsOptimize ML models built with business data, apply them to understand their impact on the business, and address critical questionsApply mathematical optimization for data-scarce problems where the objective and constraints are knownWho this book is forIf you are a budding data scientist seeking to augment your journey with mathematics, this book is for you. Researchers and R&D scientists will also be able to harness the concepts covered to their full potential. To make the best use of this book, a background in linear algebra, differential equations, basics of statistics, data types, data structures, and numerical algorithms will be useful.Table of ContentsIntroduction to Mathematical ModelingMachine Learning vis-à-vis Mathematical ModelingPrincipal Component AnalysisGradient DescentSupport Vector MachineGraph TheoryKalman FilterMarkov ChainExploring Optimization TechniquesOptimization Techniques for Machine Learning
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