E-Books →A Handbook of Mathematical Models with Python Elevate your machine learning projects with NetworkX, PuLP, and linalg

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.
Table of Contents
Introduction to Mathematical Modeling
Machine Learning vis-à-vis Mathematical Modeling
Principal Component Analysis
Gradient Descent
Support Vector Machine
Graph Theory
Kalman Filter
Markov Chain
Exploring Optimization Techniques
Optimization Techniques for Machine Learning