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Published **2008**
by Wiley-Interscience in Hoboken, NJ .

Written in English

**Edition Notes**

Statement | Vladimir Cherkassky, Filip Mulier. |

Classifications | |
---|---|

LC Classifications | TK |

The Physical Object | |

Pagination | xviii, 538 p. : |

Number of Pages | 538 |

ID Numbers | |

Open Library | OL22763029M |

ISBN 10 | 9780471681823 |

Learning From Data does exactly what it sets out to do, and quite well at that. The book focuses on the mathematical theory of learning, why it's feasible, how well one can learn in theory, etc/5. As the era of Big Data rages on, mining data to gain actionable insights is a highly sought after skill. This book focuses on algorithms that have been previously used to solve key problems in data mining and which can be used on even the most gigantic of datasets. Advanced Machine Learning A Brief Introduction to Neural Networks. Jan 17, · Need I say more? Beginner or established, every data scientist should get their hands on this book. Machine Learning. Author: Tom Mitchell. Before all the hype came about, Tom Mitchell’s book on machine learning was the go-to text to understand the math behind various techniques and algorithms. Dec 06, · This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the technology that enables computational systems to adaptively improve their performance with /5().

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If you’re looking for even more learning materials, be sure to also check out an online data science course through our comprehensive courses list.. Looking for more books? Go back to our main books page.. Note that while every book here is provided for free, consider purchasing the hard copy if you find any particularly helpful. Feb 12, · The book Agile Machine Learning by Eric Carter and Matthew Hurst describes how the guiding principles of the Agile Manifesto have been used by machine learning teams in data projects. It . This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Anyone who wants to intelligently analyze complex data should own this book. Larry Wasserman, Professor, Department of Statistics and Department of Machine Learning, CMU. As a textbook for an introduction to data science through machine learning, there is much to like about ISLR.

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