Full Description
This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward approach guided by real-world examples in areas such as health informatics, information technology, and business analytics. The book will help readers understand the various key algorithms, major software tools, and their applications. Moreover, through examples from the healthcare and business analytics fields, it demonstrates how and when ML can help them make better decisions in their disciplines.
The book is chiefly intended for undergraduate and graduate students who are taking an introductory course in machine learning. It will also benefit data analysts and anyone interested in learning ML approaches.
Contents
1. Introduction to Machine Learning.- 2. Statistics.- 3. Overview of Machine Learning Algorithms.- 4. Data Preprocessing.- 5. Data Visualization.- 6. Linear Regression.- 7. Logistic Regression.- 8. Decision Trees.- 9. Naïve Bayes.- 10. K-Nearest Neighbors.- 11. Neural Networks.- 12. K-Means.- 13. Support Vector Machine.- 14. Voting and Bagging.- 15. Boosting and Stacking.- 16. Future Directions and Ethical Considerations.
