دانلود کتاب راهنمای مدل های ریاضی با پایتون – پروژه های یادگیری ماشینی خود را با NetworkX، PuLP و linalg ارتقا دهید

Mathematical modeling is the art of transforming a business problem into a clear mathematical formulation. For a given problem or use case, the algorithmic implementation of a model helps optimize parameters and generate better insights and comprehension to enable decision-making. A mathematical model complements a machine learning model and supports high-stake decisions in sensitive domains such as medicine, for example.
There are three focal topics to help you understand mathematical modeling:
• Areas where a mathematical model is useful – for example, control engineering and signal processing
• Tested Python-based mathematical tools – for example, graph theory and MCMC
• Underlying algorithms of mathematical optimization
I will provide concepts of mathematical modeling and various approaches to modeling through this book. I will guide you in choosing the optimal technique and best-suited algorithm to solve a business problem using Python, based on two main sources of information:
• My experience from the past 5 years as a data scientist and application developer for businesses
• My academic research (at different stages of maturity) across science disciplines for a decade
As a data professional, I believe mathematical models (equation-driven) with objectives and constraints in a problem are as relevant as (data-driven) machine learning models. In some cases, the right combination of both yields the best solutions.
Data scientists, research and development professionals, and business scientists, in general, can gain practical insights into mathematical modeling with Python from this book.
It is assumed that you have knowledge of the following:
• Differential equations
• Linear algebra
• Basics of statistics
• Data types and data structures
• Numerical algorithms
You will learn about the relevance of mathematical models, how interpretability must be factored into models while solving a business problem, and how mathematical optimization and tuning machine learning models are important to arrive at the optimal solution. You will also learn how to choose a model, keeping in mind the cost-effectiveness and efficiency of the underlying algorithm per the business case.