What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Key benefits

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

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.

Who is this book 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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Causal Inference and Discovery in Python

$39.99

A Handbook of Mathematical Models with Python

$49.99

$49.99

Total $ 139.97

Om S Sep 10, 2023

The book begins by introducing the concept of mathematical modeling and its significance in solving practical problems. It then takes readers through a journey of various mathematical models and demonstrates how Python can be used as a tool to implement and work with these models effectively.One of the book's strengths is its emphasis on hands-on learning. It provides practical examples and exercises that allow readers to apply mathematical models to real-life scenarios. This hands-on approach not only enhances understanding but also equips readers with valuable problem-solving skills.Throughout the book, readers will explore a wide range of mathematical concepts and their applications, from basic principles to more advanced topics. The book also covers optimization techniques, which are essential for finding the best solutions to complex problems.Whether you are a student looking to learn mathematical modeling or a professional seeking to apply these techniques in your work, this book provides a solid foundation. It is written in a clear and accessible manner, making it suitable for readers with various levels of mathematical and programming expertise.In conclusion, "A Handbook of Mathematical Models with Python" is a practical and user-friendly guide that demystifies mathematical modeling and demonstrates how to implement it using Python. It is a valuable resource for anyone looking to enhance their problem-solving skills and apply mathematics to real-world challenges.

Amazon Verified review

Kumar Abhishek Dec 30, 2023

This book is a concise, 100-page guide that provides a quick yet insightful overview of mathematical models and their connection to machine learning. As a senior machine learning engineer, I found this book to be a helpful introduction for those who are beginning their journey in data science or those who feel lacking in math knowledge. It effectively links fundamental mathematical concepts with Python applications in machine learning, offering practical examples and use cases. While the book is an excellent starting point for understanding the interplay between math and machine learning, it's important to note that it doesn't delve into deep technical details. For readers seeking in-depth mathematical explanations, I would recommend more mathematically intensive books. Nevertheless, for a clear, introductory understanding of how mathematics underpins machine learning algorithms, Dr. Sarkar's book is a valuable and accessible resource.

Amazon Verified review

Greg Phillips Feb 06, 2024

This is an excellent book to get you started on the path towards a better understanding of the math underpinning Machine Learning as implemented with Python. Reading is time well spent.

Amazon Verified review

Rahul Bahadur Oct 09, 2023

While most ML books delve straight into applications, this book describes, in detail, what goes on mathematically under the hood - without being overwhelming. It starts with describing some of the most common algorithms that any Data Scientist/Statistician uses (like PCA, SVM, Linear regression, neural-nets) and their applications. Later it also describes MCMC chains, Kalman filters etc. along with graph optimizations (travelling salesperson).One of the most common mistake that a data scientist does is treating everything as an ML prediction problem. This book talks about scenarios where one would apply linear programming (PuLP), network modelling etc. to come up with the correct solution.Each chapter has plenty of follow along example in python using commonly available libraries. The author also mentions a lot of great online resources if you want to delve further into any topic.All in all this is a great quick read if you are looking to refresh your understanding or even starting to explore what all methods should exists in your problem solving toolbox.

Amazon Verified review

H2N Nov 16, 2023

This book is an essential resource for data scientists and R&D professionals, offering a practical approach to mathematical modeling in Python. It's ideal for those familiar with differential equations, linear algebra, and statistics, blending machine learning with classical models. Covering PCA, Gradient Descent, SVMs, and more, it excels in explaining data classification and reduction. Additionally, it touches on Graph Theory, Kalman Filter, and Markov Chains, culminating in strategies for optimizing machine learning models. While concise, it leaves readers anticipating a more in-depth exploration in future editions.

Amazon Verified review

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

What do you get with eBook?