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A Handbook of Mathematical Models with Python

You're reading from   A Handbook of Mathematical Models with Python Elevate your machine learning projects with NetworkX, PuLP, and linalg

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781804616703
Length 144 pages
Edition 1st Edition
Languages
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Author (1):
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Ranja Sarkar Ranja Sarkar
Author Profile Icon Ranja Sarkar
Ranja Sarkar
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Toc

Table of Contents (16) Chapters Close

Preface 1. Part 1:Mathematical Modeling
2. Chapter 1: Introduction to Mathematical Modeling FREE CHAPTER 3. Chapter 2: Machine Learning vis-à-vis Mathematical Modeling 4. Part 2:Mathematical Tools
5. Chapter 3: Principal Component Analysis 6. Chapter 4: Gradient Descent 7. Chapter 5: Support Vector Machine 8. Chapter 6: Graph Theory 9. Chapter 7: Kalman Filter 10. Chapter 8: Markov Chain 11. Part 3:Mathematical Optimization
12. Chapter 9: Exploring Optimization Techniques 13. Chapter 10: Optimization Techniques for Machine Learning 14. Index 15. Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “ The hyperparameter optimization methods in the scikit-learn Python library assume good performance scores are negative values close to zero.”

A block of code is set as follows:

import pandas as pd, numpy as np
from collections import Counter
import matplotlib.pyplot as plt

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

prediction = one_class_svm.predict(X_test)
prediction = [1 if i == -1 else 0 for i in prediction] #outliers denoted by 1, inliers by 0
print(classification_report(y_test, prediction))

Tips or important notes

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