Data Mining: Practical Machine Learning Tools and Techniques
Ian H. Witten Eibe Frank Mark A. Hall
Best Download [Ian H. Witten Eibe Frank Mark A. Hall] Ý Data Mining: Practical Machine Learning Tools and Techniques || [Music Book] PDF ↠
Oct 29, 2020 - 08:31 AM By Ian H. Witten Eibe Frank Mark A. Hall

Data Mining Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real world data mining situations This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to Data Mining Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real world data mining situations This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi instance Learning, plus a new version of the popular Weka machine learning software developed by the authors Witten, Frank, and Hall include both tried and true techniques of today as well as methods at the leading edge of contemporary research Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks in an updated, interactive interface Algorithms in toolkit cover data pre processing, classification, regression, clustering, association rules, visualization
  • Title: Data Mining: Practical Machine Learning Tools and Techniques
  • Author: Ian H. Witten Eibe Frank Mark A. Hall
  • ISBN: 9780123748560
  • Page: 140
  • Format: Paperback

Comments

Todd N Oct 29, 2020 - 08:31 AM
This is an excellent, but somewhat uneven, introduction to the field of machine learning, divided into three parts.Part 1 is a good overview of the types of use cases, standard data sets, and algorithms. It provides more intuitive rather than technical explanations, though there is some math to get through. Reading just this section will definitely get you through any dinner party conversations about machine learning. I read through this twice, taking careful notes in my Moleskine (natch) the se [...]
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Vhalros Oct 29, 2020 - 08:31 AM
From the perspective of a computer scientist, this book is basically totally useless, as it leaves the reader with no idea how any of the algorithms really work. It might be helpful if you want to be able to use some machine learning software while avoiding having anything more than a cursory understand of how it works.
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Derek Bridge Oct 29, 2020 - 08:31 AM
A useful compendium of data mining techniques and accompaniment to the Weka data mining tool. This book is more an overview than a detailed treatise: there are descriptions but few precise algorithms; the maths is kept to a minimum and, where there is maths, it is often left mostly unexplained; the applications seem dated - there's little on mining large-scale scientific, medical or web data, for example; and issues of handling large scale data are skirted. Nevertheless, its scope is wide and it [...]
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Joe Cole Oct 29, 2020 - 08:31 AM
This is latest edition for data mining. I like this book because if provide practical examples for machine learning.
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Kai Weber Oct 29, 2020 - 08:31 AM
This book covers data mining techniques that were developed within the study field of machine learning. It starts with explaining how to represent input and output data and then progresses from simpler, basic algorithms (e.g. naïve Bayes, decision trees, rule inference, instance-based learning, clustering) to more advances techniques (e.g. C4.5, hyperplane margins, neural networks, advanced probabilistic methods, deep learning. Along the way it also covers evaluation of what's been learned by a [...]
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JDK1962 Oct 29, 2020 - 08:31 AM
I really, really wanted to like this book more than I did. After all, it was about a topic that I have great interest in, and describes a workbench application (Weka) that I can command-line access from my favorite programming environment (R, via RWeka).The problem I was having with it is that its presentation, across the board, was incredibly wordy. They managed to make the interesting sound boring, and presented technical material with no grace whatsoever. The chapter on the Weka Explorer was [...]
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Alex Zakharov Oct 29, 2020 - 08:31 AM
I’ve been delaying picking up a proper data science book for a couple of years now and finally ran out of excuses not to do it. These days any moderately serious conversation/book about areas that I tend to follow - genetics, genomics, economic development, history, consciousness, prediction, uncertainty - requires a minimum grounding in statistics and/or machine learning. Thus, when a couple of weeks ago I had to look something up for a little work project I took the opportunity to read most [...]
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Robert Muller Oct 29, 2020 - 08:31 AM
While this book is an excellent overall summary of data mining technology, and it's an indispensable reference for using the Weka data mining software, it is not detailed enough, nor does it have enough examples, for an otherwise inexperienced novice data miner to be effective. If you come at it knowing a lot about statistics, probability, and modeling, you can get your knowledge rounded out with techniques and ideas you may not have experienced but make sense to you. If you don't bring such kno [...]
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Kid Oct 29, 2020 - 08:31 AM
Best introductory book on Data Mining in terms of concepts and practice. Not too academically but goal-driven and data-driven, which makes readers understand it easier.WEKA is a great tool, although its part in this book is a little bit too much.For those who needs more on theory perspective, I recommend the book "Introduction to Data Mining" (Pang-Ning Tan, Michael Steinbach, Vipin Kumar). But if you want to apply it on business without knowing a lot of mathematical backgrounds, you can look fo [...]
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John Orman Oct 29, 2020 - 08:31 AM
This big book has many sections that I used for my current Machine Learning online class: Applications, Knowledge Representation, Algorithms, Linear/Logistic Regression, Prediction, Classification, Clustering, and Cost Calculation. It also introduced me to the WEKA machine learning workbench, a set of free software tools that can be downloaded to implement many of the algorithms used in machine learning.
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Avinash K Oct 29, 2020 - 08:31 AM
Very good first book and a very good reference book. This is very rare achievement especially for something that should be accessible for persons with minimal background in data mining.Pedagogy is just right and writing style is very lucid.Excellent book and like Prof. Witten's lectures very interactive Highly recommend it.
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Ayman Sieny Oct 29, 2020 - 08:31 AM
The book provides a good introduction to data mining algorithms including classification, clustering and association. It also provides practical hands-on exercises using an open source data mining tool developed by the authors called WEKA.
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Darin Oct 29, 2020 - 08:31 AM
This is a decent book at a high level. If you like a lot of theory, this isn't the book for you. The authors are also the authors of the machine learning tool Weka, which is briefly covered in this book.
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Brett Dargan Oct 29, 2020 - 08:31 AM
Loved this book. Although some parts were too slow, especially the first few chapters. Took a long time to explain concepts that could have been reduced a lot.It is well worth sticking with it though; learnt some important concepts about data structures I hadn't come across before.
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Thomasreece Oct 29, 2020 - 08:31 AM
CCool book ,it makes you aware of mathematical inventions not too far fetched from wanting to incorporate only to know after that there was already such an invention. haven't completed
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Kenny Daily Oct 29, 2020 - 08:31 AM
Great reference book, with a good introduction to using the Weka suite.
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Timon Karnezos Oct 29, 2020 - 08:31 AM
Pedantic to a fault. Otherwise, it's just a bunch of algorithms with analysis and discussion.
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Randy Oct 29, 2020 - 08:31 AM
I put this book down after reading the first 1/3 or so. It didn't seem to be that well done. I can't put my finger on exactly what i didn't like, but I'm moving on to something else.
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Juliusz Gonera Oct 29, 2020 - 08:31 AM
Very hands on/practical intro to the subject. For readers who want to start using ML techniques quickly and worry about theoretical considerations later.
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Nitin Oct 29, 2020 - 08:31 AM
Explains various ML schemes very well but limits only to WEKA.
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Robert Row Oct 29, 2020 - 08:31 AM
Did not perform any of the weka related exercises
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Chris Oct 29, 2020 - 08:31 AM
I was looking for something not so theoretical, which is totally what it was to me. Practical to me means something with code
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Soren Macbeth Oct 29, 2020 - 08:31 AM
A good medium level introduction to data mining. Written by the authors of WEKA which is used to apply the concepts in the book.
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Alon Gutman Oct 29, 2020 - 08:31 AM
Love the tool(Weka) the book is bad.
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Ayoola Adegbite Oct 29, 2020 - 08:31 AM
recommended , used for data mining course at uni quite practical
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Bill Hayes Oct 29, 2020 - 08:31 AM
I like his stated approach to give readers a good feel for the different techniques of Machine Learning and what they can be used for.
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Theresamvitolo Oct 29, 2020 - 08:31 AM
Sketchyes WEKASeems like the author is just copying from other sources
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Data Mining: Practical Machine Learning Tools and Techniques By Ian H. Witten Eibe Frank Mark A. Hall Data Mining Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real world data mining situations This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to Data Mining Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real world data mining situations This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi instance Learning, plus a new version of the popular Weka machine learning software developed by the authors Witten, Frank, and Hall include both tried and true techniques of today as well as methods at the leading edge of contemporary research Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks in an updated, interactive interface Algorithms in toolkit cover data pre processing, classification, regression, clustering, association rules, visualization

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  • Best Download [Ian H. Witten Eibe Frank Mark A. Hall] Ý Data Mining: Practical Machine Learning Tools and Techniques || [Music Book] PDF ↠
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    Published :2020-07-22T08:31:47+00:00