Machine learning and Data Mining recommendation book list

Machine learning and Data Mining recommendation book list

With these books, no longer worry about the class no sister paper should do. Take your time, learn, and uncover the mystery of machine learning and data mining.

machine learning Combat " : The first part of this book mainly introduces the basis of machine learning, and how to use the algorithm to classify, and gradually introduced a variety of classical supervised learning algorithms, such as k logistic regression algorithm, Support Vector machine, adaboost Integration method, Tree-based regression algorithms and categorical regression trees ( cart ) algorithms. The third part focuses on unsupervised learning and some of its main algorithms: k mean clustering algorithm, apriori algorithm, fp-growth algorithm. The Forth part introduces some subsidiary tools of machine learning algorithm.

The book, through carefully choreographed examples, cuts into everyday tasks, rejects academic language, and uses efficient reusable Python code to illustrate how to process statistics, analyze and visualize data. Through various examples, the reader can learn the core algorithm of machine learning, and can apply it to some strategic tasks, such as classification, prediction, recommendation. In addition, they can be used to implement some of the more advanced features, such as summarization and simplification.

I've seen a part of this book before, but the internship involves working with the data in Java code, so let's put it aside for the moment and the book that is currently being Hangyuan Li.

data mining- ( decision Trees, association rules, linear models, clusters, Bayesian networks and Neural networks ) weka weka The system has a graphical user interface for data mining, which helps to understand the model , is a useful and popular tool.

Data Mining: Concepts and Technologies: This book comprehensively describes the important knowledge and technological innovation in the field of data mining. On a fairly comprehensive basis in version 1, version 2 shows the latest research in the field, such as mining streams, timing and sequence data, and mining time space, multimedia, text, and Web data. This book serves as a must-read for teachers, researchers, and developers in data mining and knowledge Discovery .

basic data mining, inference and prediction of statistical learning: Although statistical methods are applied, the emphasis is on concepts rather than mathematics. Many examples are attached to a color map. The fundamentals of Statistical learning : Data Mining, reasoning and forecasting are extensive, ranging from guided learning (forecasting) to non-instructional learning. Topics such as neural networks, support vector machines, classification trees, and ascension are among the most comprehensive of its kind. The rapid development of computing and information technology has brought huge amounts of data in many fields such as medicine, biology, finance and marketing. Understanding these data is a challenge that has led to the development of new tools in the field of statistics and extends to new areas such as data mining, machine learning and bioinformatics.

machine Learning (Mitchell): Demonstrates the core algorithms and theories in machine learning, and illustrates the process of the algorithm's operation. Machine learning combines a number of research results, such as statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity, and cybernetics, to understand the background, algorithm, and implicit assumptions of the problem. Machine learning can be used as a textbook for undergraduates and postgraduates in computer science, as well as a reference book for researchers and teachers in related fields.

"Statistical Learning Method": This book comprehensively and systematically introduces the main methods of statistical learning, especially the supervised learning methods, including Perceptron,K -nearest neighbor, naive Bayesian method, decision tree, logistic regression and maximum entropy model, support vector machine, lifting method, em algorithm, hidden Markov model and conditional random field. In addition to chapter 1 Introduction and Final Chapter summary, each chapter introduces a method. The narrative begins with specific problems or examples, clarifies ideas, gives the necessary mathematical deduction, and makes it easy for readers to master the essence of statistical learning methods and to learn how to use them. In order to meet the needs of further study, the book also introduces some related studies, gives a few exercises, and lists the main reference documents.

Introduction to Machine learning: An introduction to the definition and application of machine learning, covering supervised learning. Bayesian decision-making theory. Parameter method, multivariate method, dimension normalization, clustering, nonparametric method, decision tree. Linear discriminant, multi-layer perceptron, local model, hidden Markov model. Classification algorithm evaluation and comparison, combination of multi-learning and enhance learning and so on.

machine learning and its application: The bookis divided into three chapters, including causal inference, manifold learning and dimensionality reduction, migration learning, class imbalance learning, evolutionary clustering, multi-marker learning, sequencing learning, semi-supervised learning and other technologies and collaborative filtering, community recommendations, Applications such as machine translation and the need for machine learning technology in Internet applications.

The second edition of Pattern classification : In addition to retaining the main content of the 1 Edition on Statistical pattern recognition and structural pattern recognition, readers will find new theories and new approaches that have been added in recent years, These include neural network, machine learning, data mining, evolutionary computation, invariant theory, hidden Markov model, statistical learning theory and support vector machine.

recommendation System Practice: A large number of codes and diagrams systematically elaborated the theoretical basis of the recommendation system, introduced the evaluation of the recommendation system of the pros and cons of various standards ( such as coverage, satisfaction ) and methods ( e.g. AB test ), which summarizes the various and recommended products and services in the Internet area today.

"Deep search engine- the compression, index and query of mass information ": Both theory and practice, the whole solution of data processing is given in a comprehensible way, including all aspects of compressing, indexing and querying. Its biggest characteristic lies in not only satisfies the need of the information retrieval theory study, but also gives the various problems which may face in the practice and the solution method.

"Probability theory and Mathematical Statistics": This book does not introduce too much, the general University of the freshman year of textbooks, only hate that did not lecture ah, is now slowly gnawing ...

Big Data: Internet large-scale data mining and distributed processing: Themain content includes Distributed File system, similarity search, search engine technology, frequent itemsets mining, clustering algorithm, advertisement management and Recommender system.

Webweb The technique of extracting and generating knowledge in data. The first chapter of the book deals with web web web indexing mechanism and keyword-based or similarity-based search mechanism, and then systematically describe the basics of web mining , focusing on hypertext-based machine learning and data mining methods, such as clustering, collaborative filtering, supervised learning, semi-supervised learning, and finally described these basic principles in web web Data Mining" provides the reader with a solid technical background and the latest knowledge.

"Top of the data": to the back of big data, put forward the current information technology development, has made China gain the advantage of the latter, China to win in the big Data era of global competition, the big data must be promoted from the technical symbols to cultural symbols, advocating data culture in the whole society.

"In-depth statistics": This book covers the knowledge points include: Information visualization, probability calculation, geometric distribution, two distribution and Poisson distribution, normal distribution, statistical sampling, confidence interval construction, hypothesis testing, chi-square distribution, correlation and regression, etc., complete coverage of the AP examination scope.

Matrix Analysis: This book from the perspective of mathematical analysis of matrix analysis of classical methods and modern methods, based on new, there is a certain depth, and give in multivariate calculus, complex analysis, differential equations, quantity optimization, approximation theory of many important applications. The main contents include: Eigenvalue, eigenvector and similarity, unitary equivalence and normal matrix, standard shape,Hermite matrix and symmetric matrix, vector norm and matrix norm, eigenvalue and estimation and perturbation, positive definite matrix, nonnegative matrix.

Machine learning and Data Mining recommendation book list