A Handbook of Mathematical Models with Python: Elevate your machine learning projects with NetworkX, PuLP, and linalg

Master the art of mathematical modeling through practical examples, use cases, and machine learning techniques

Key Features:

• Gain a profound understanding of various mathematical models that can be integrated with machine learning
• Learn how to implement optimization algorithms to tune machine learning models
• Build optimal solutions for practical use cases
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

Mathematical 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 Learn:

• Understand core concepts of mathematical models and their relevance in solving problems
• Explore various approaches to modeling and learning using Python
• Work with tested mathematical tools to gather meaningful insights
• Blend mathematical modeling with machine learning to find optimal solutions to business problems
• Optimize ML models built with business data, apply them to understand their impact on the business, and address critical questions
• Apply mathematical optimization for data-scarce problems where the objective and constraints are known

Who this book is for:

If 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.

精通數學建模的藝術：通過實例、應用案例和機器學習技術

主要特點：
- 深入理解各種可與機器學習相結合的數學模型
- 學習如何實施優化算法以調整機器學習模型
- 構建實際應用案例的最佳解決方案
- 購買印刷版或Kindle電子書可獲得免費PDF電子書

書籍描述：
數學建模是將業務問題轉化為明確的數學形式的藝術。在藥品和醫療保健等敏感行業中，尤其重視其可解釋性，因為部署模型以支持高風險決策至關重要。

通過本書，您將深入了解支撐各種機器學習算法的基礎數學知識。憑藉這些知識，您可以修改算法以適應業務問題。從數學建模的基本理論和概念開始，您將探索一系列數學工具，這些工具將使您能夠提取洞察力並更好地理解數據，進而幫助您做出最佳的數據驅動決策。本書讓您探索數學優化及其廣泛的應用，並最終強調了將數學模型與機器學習相結合所帶來的協同價值。

最終，您將能夠應用所學的一切，選擇最適合的方法來解決遇到的業務問題。

您將學到什麼：
- 理解數學模型的核心概念及其在解決問題中的相關性
- 使用Python探索各種建模和學習方法
- 使用經過驗證的數學工具收集有意義的洞察力
- 將數學建模與機器學習相結合，找到業務問題的最佳解決方案
- 優化使用業務數據構建的機器學習模型，應用它們以了解其對業務的影響並解決關鍵問題
- 對於目標和約束已知的數據稀缺問題應用數學優化

本書適合對數據科學感興趣的初學者，研究人員和研發科學家也能充分利用所涵蓋的概念。為了最好地使用本書，具備線性代數、微分方程、統計基礎、數據類型、數據結構和數值算法的背景將會有所幫助。