Data Science and Machine Learning for Non-Programmers

ISBN-13: 9780367755386 / Twarda / 2024 / 626 str.

Dothang Truong

Opis

As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially, however, the abundance of resources can be overwhelming

Spis treści

PREFACE

 

PART I: INTRODUCTION TO DATA MINING

Chapter 1.               Introduction to Data Mining and Data Science

Chapter 2.               Data Mining Process, Methods, and Software

Chapter 3.               Data Sampling and Partitioning

Chapter 4.               Data Visualization and Exploration

Chapter 5.               Data Modification

 

PART 2: DATA MINING METHODS

 

Chapter 6.               Model Evaluation

Chapter 7.               Regression Methods

Chapter 8.               Decision Trees

Chapter 9.               Neural Networks

Chapter 10.           Ensemble Modeling

Chapter 11.           Presenting Results and Writing Data Mining Reports

Chapter 12.           Principal Component Analysis

Chapter 13.           Cluster Analysis

 

PART III: ADVANCED DATA MINING METHODS

 

Chapter 14.           Random Forest

Chapter 15.           Gradient Boosting

Chapter 16.           Bayesian Networks

 

TERM DEFINITIONS

 

REFERENCES

 

APPENDICES

 

 

O autorze

Dr. Dothang Truong is the Professor of Graduate Studies at Embry Riddle Aeronautical University, Daytona Beach, Florida. He has extensive teaching and research experience in machine learning, data analytics, air transportation management, and supply chain management. In 2022, Dr. Truong received the Frank Sorenson Award for the outstanding achievement of excellence in aviation research and scholarship.