This is the code repository for A Handbook of Mathematical Models with Python, published (and sold) by Packt.
Elevate your machine learning projects with NetworkX, PuLP, and linalg
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy from Amazon today!
All of the code is organized into folders.
The code will look like the following:
import pandas as pd, numpy as np
from collections import Counter
import matplotlib.pyplot as plt
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapters 3-9).
You will need Python 3.0 or higher versions to run the code in respective chapters. Python libraries required to execute a particular method have been imported (compatible versions with Python 3.0) in the code, which can be installed readily in the notebook or Python environment of your system.
System requirements are mentioned in the following table:
| Software/Hardware | Operating System requirements |
|---|---|
| Python 3.0 or higher | Windows, Mac OS X, and Linux (Any) |
| Python libraries | Windows, Mac OS X, and Linux (Any) |
Dr. Ranja Sarkar is a delivery manager (data science consulting) at MathCo. She is a scientist with a focus on the intersection of data science and technology, working on a variety of projects in sectors ranging from manufacturing to healthcare to retail. Prior to this, Ranja worked as a data scientist at Shell plc, the most diversified global group of energy and petrochemical companies. Ranja has a decade of experience in academic scientific research encompassing physics, biophysics, statistical physics, and mathematics with publications in peer-reviewed internationally acclaimed journals. She has also worked at Deloitte - USI (Offices of the US) as a consultant for a year.
This book has been divided into three parts.
Part I - Mathematical Modeling: It has 2 chapters and introduces the theory and concepts of mathematical modeling and discusses how a business problem can be formulated mathematically.
Part II - Mathematical Tools: It consists of 6 chapters (all have python code examples) about the most tried and tested tools that enable mathematical modeling and how mathematical models are complementary to machine learning models. Those chapters have dedicated repositories as supplementary materials to the book. They are as follows:
-> Ch 3: PCA
Repo: https://github.com/ranja-sarkar/pca
-> Ch 4: Gradient Descent
Repo: https://github.com/ranja-sarkar/GD
-> Ch 5: SVM
Repo: https://github.com/ranja-sarkar/SVM
-> Ch 6: Graph Theory
Repo: https://github.com/ranja-sarkar/networks-graphs
-> Ch 7: Kalman Filter
Repo: https://github.com/ranja-sarkar/Kalman
-> Ch 8: Markov Chain
Repo: https://github.com/ranja-sarkar/Markov
Part III - Mathematical Optimization: It has 2 chapters (1 has python code example) and introduces the readers to optimization techniques in general, those used in machine learning and in operations research.