Sumathi S. Machine Learning for Decision Sciences...Python 2022

Textbook in PDF format

This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data.
Features:
Explains the basic concepts of Python and its role in machine learning.
Provides comprehensive coverage of feature engineering including real-time case studies.
Perceives the structural patterns with reference to data science and statistics and analytics.
Includes machine learning-based structured exercises.
Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning.
This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.
Introduction
Overview of Python for Machine Learning
Data Analytics Life Cycle for Machine Learning
Unsupervised Learning
Supervised Learning: Regression
Supervised Learning: Classification
Feature Engineering
Reinforcement Engineering
Case Studies for Decision Sciences Using Python
Appendix. Python Cheat Sheet for Machine Learning