Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering.
Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems.
The book highlights many use cases for recommendation systems:
- - Provides a concise introduction to numerical concepts in machine learning in simple terms
- - Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables
- - Focuses on numerical examples while using small datasets for easy learning
- - Includes simple Python codes
- - Includes bibliographic references for advanced reading
The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses.
Audience: College and university level students and instructors.