Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

Produk Detail:

  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Pages : 188 pages
  • ISBN : 0128172169
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKIntroduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning
  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Release : 15 July 2019
GET THIS BOOKIntroduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but

Machine Learning and Data Mining

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  • Publisher : Horwood Publishing
  • Release : 14 May 2007
GET THIS BOOKMachine Learning and Data Mining

Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as

Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning
  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Release : 17 June 2019
GET THIS BOOKIntroduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but

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  • Publisher : CRC Press
  • Release : 20 November 2019
GET THIS BOOKIntroduction to Data Science

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  • Publisher : MIT Press
  • Release : 29 August 2014
GET THIS BOOKIntroduction to Machine Learning

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in

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  • Publisher : Springer Nature
  • Release : 25 September 2021
GET THIS BOOKAn Introduction to Machine Learning

This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear

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  • Author : A.C. Faul
  • Publisher : CRC Press
  • Release : 23 August 2019
GET THIS BOOKA Concise Introduction to Machine Learning

The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise. This useful reference should be an essential on the bookshelves of anyone employing machine learning techniques.

Data Mining and Analysis

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  • Release : 12 May 2014
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  • Publisher : MIT Press
  • Release : 16 March 2018
GET THIS BOOKMachine Learning for Data Streams

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining

Introduction to Artificial Intelligence

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  • Publisher : Springer
  • Release : 18 January 2018
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This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning. Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources

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  • Release : 20 November 2019
GET THIS BOOKText Mining with Machine Learning

This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The

Introduction to Statistical and Machine Learning Methods for Data Science

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  • Author : Carlos Andre Reis Pinheiro,Mike Patetta
  • Publisher : SAS Institute
  • Release : 06 August 2021
GET THIS BOOKIntroduction to Statistical and Machine Learning Methods for Data Science

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book