Here is the listing of Best reference books on Machine Learning.
|1. “Machine Learning” by Tom Mitchell
Book Review: The book covers various concepts related to the field of machine learning which involves the development of computational models of learning processes. The major objective of this book is to provide a snapshot of machine learning through a board. The book contains 77 papers on machine learning which cover a broad range of topics including research on analogy, conceptual clustering, explanation based generalization, incremental learning, inductive inference, machine discovery, theoretical learning models and applications of machine learning methods. This book provides representative sampling of the best ongoing work in the field.
|2. “Mining the Web: Discovering Knowledge from Hypertext Data” by Soumen Chakrabarti
Book Review: This book presents techniques for knowledge production from the vast body of unstructured web data. The author examines low level machine learning techniques as they relate specifically to the challenges in the field of web mining. The later part of the book deals with the applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. The book focuses on the results, strengths and weaknesses of applications. The book details the challenges associated with semi structured and unstructured data analysis. The book also analyzes the current applications for resource discovery and social network analysis. The book is an excellent way to introduce students to vital applications of data mining and machine learning technology.
|3. “Introduction To Machine Learning” by Ethem Alpaydin
Book Review: This book explains the application of machine learning to program computers to use the past experience to solve a given problem. The systems presented in the book analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources. The book covers topics like supervised learning, Bayesian decision theory, parametric, semiparametric and nonparametric methods, hidden markov models, reinforcement learning, kernel machines, graphical models, multivariate analysis, Bayesian estimation and statistical testing. The book presents learning algorithms so that the students can move from equations to computer programs with ease. This book is very useful for graduate and undergraduate students and for professionals who are concerned with the application of machine learning methods.
Sanfoundry Global Education & Learning Series – Best Reference Books!