Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale
Machine online high quality Learning for Text outlet online sale_top

Description

Product Description

Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories:

- Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.

- Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 

- Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection.

 This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop).

 This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching.

Review

“The book discusses many key technologies used today in social media, such as opinion mining or event detection. One of the most promising new technologies, deep learning, is discussed as well. This book is an excellent resource for programmers and graduate students interested in becoming experts in the text mining field. … Summing Up: Recommended. Graduate students, researchers, and professionals.” (J. Brzezinski, Choice, Vol. 56 (04), December, 2018)

From the Back Cover

Text analytics is a field that lies on the interface of information retrieval, machine learning,

and natural language processing. This book carefully covers a coherently organized framework

drawn from these intersecting topics. The chapters of this book span three broad categories:

 

1. Basic algorithms: Chapters 1 through 8 discuss the classical algorithms for text analytics

such as preprocessing, similarity computation, topic modeling, matrix factorization,

clustering, classification, regression, and ensemble analysis.

 

2. Domain-sensitive learning: Chapters 8 and 9 discuss learning models in heterogeneous

settings such as a combination of text with multimedia or Web links. The problem of

information retrieval and Web search is also discussed in the context of its relationship

with ranking and machine learning methods.

 

3. Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and

natural language applications, such as feature engineering, neural language models,

deep learning, text summarization, information extraction, opinion mining, text segmentation,

and event detection.

 

This book covers text analytics and machine learning topics from the simple to the advanced.

Since the coverage is extensive, multiple courses can be offered from the same book,

depending on course level.

About the Author

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. from the Massachusetts Institute of Technology in 1996. He has worked extensively in the field of data mining. He has published more than 350 papers in refereed conferences and journals and authored over 80 patents. He is the author or editor of 17 books, including textbooks on data mining, recommender systems, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, and a recipient of two IBM Outstanding Technical Achievement Awards (2009, 2015) for his work on data streams/high-dimensional data. He received the EDBT 2014 Test of Time Award for his work on condensation-based privacy-preserving data mining. He is also a recipient of the IEEE ICDM Research Contributions Award (2015), which is one of the two highest awards for influential research contributions in the field of data mining. He has served as the general co-chair of the IEEE Big Data Conference (2014) and as the program co-chair of the ACM CIKM Conference (2015), the IEEE ICDM Conference (2015), and the ACM KDD Conference (2016). He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008. He is an associate editor of the IEEE Transactions on Big Data, an action editor of the Data Mining and Knowledge Discovery Journal, and an associate editor of the Knowledge and Information Systems Journal. He has served as editor-in-chief of the ACM SIGKDD Explorations (2014–2017) and is currently an editor-in-chief of the ACM Transactions on Knowledge Discovery from Data. He serves on the advisory board of the Lecture Notes on Social Networks, a publication by Springer. He has served as the vice-president of the SIAM Activity Group on Data Mining and is a member of the SIAM industry committee. He is a fellow of the SIAM, ACM, and the IEEE, for “contributions to knowledge discovery and data mining algorithms.”

Product information

Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Videos

Help others learn more about this product by uploading a video!
Upload video
Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Customers who bought this item also bought

Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.
Sponsored

Customer reviews

4.6 out of 54.6 out of 5
25 global ratings

Top reviews from the United States

mur81
5.0 out of 5 starsVerified Purchase
This is an excellent textbook for academia and industry alike
Reviewed in the United States on July 17, 2018
This is an excellent textbook for academia and industry alike, although the style leans towards academia. The book introduces various machine learning methods in detail like matrix factorization, PLSA, LDA, SVD, clustering, classification, and deep learning. The exposition... See more
This is an excellent textbook for academia and industry alike, although the style leans towards academia. The book introduces various machine learning methods in detail like matrix factorization, PLSA, LDA, SVD, clustering, classification, and deep learning. The exposition is clear, intuitive, and certainly not dry, which can sometimes be a risk for mathematically oriented books. Coverage of deep learning includes some unique perspectives on word2vec, RNNs, and LSTMs. Beautiful presentation of word2vec is provided together with its relationship to matrix factorization. Even though word2vec is also covered in Jurafsky (Chapter 16) and in some other books, the details here are far greater than any other book I have seen.

The insights and connections in the book are pretty amazing. Word2vec is connected to matrix factorization, SVMs/RFs are connected to nearest neighbors, NMF is connected to PLSA, and so on. Insights are provide on why kernels truly work and the procedure for systematic kernelization of arbitrary problems is provided. Other covered topics include opinion mining, summarization, text segmentation, and information extraction.
Examples and pseudocodes are given in many chapters.

The book covers all the three aspects of machine learning (deep focus), information retrieval, (light focus), and sequence-centric topics like information extraction/summarization. The style and quality of writing is somewhat similar to "An Introduction to Information Retrieval" by Manning although the content and coverage are quite different (and more extensive). Manning focuses on IR and touches on machine learning, whereas this book focuses on machine learning and touches on IR. The book contains about 500 pages, although the amount of content is larger than other books of comparable size because of the smaller font used.

This book is clearly not a programming or implementation book, and it seems to be targeted to university classrooms, with a presentation that
is independent of specific programming frameworks (like Python). This is on par for a university textbook, and preferable for someone from academia. Nevertheless, industry can benefit too, because the pseudocodes and detailed explanations are sufficient for a mathematically and algorithmically competent engineer to implement what they want. The author also provides bibliographic summaries with pointers to software
resources.
12 people found this helpful
Helpful
Report
Colbert Philippe
5.0 out of 5 starsVerified Purchase
Another wonderful book on the subject of Deep Learning....this narrowed down to Text!
Reviewed in the United States on November 1, 2019
I love this book! It occupies a privileged place in my library. I am a professional computer programmer and Deep Learning programmer. Deep Learning can be used on images and Text. I find that my clients have Deep Learning needs that are related to text documents, text... See more
I love this book! It occupies a privileged place in my library. I am a professional computer programmer and Deep Learning programmer. Deep Learning can be used on images and Text. I find that my clients have Deep Learning needs that are related to text documents, text processing. So for me, using Deep Learning on text documents is first priority. This book is university level on the topic. It has enough material for 4 university classes. So I keep it as a reference that I can go back to all the time. I recommend this book for professionals like me who want take advantage of the booming market.
2 people found this helpful
Helpful
Report
MS
5.0 out of 5 starsVerified Purchase
Deep coverage with lots of intuition and insight
Reviewed in the United States on July 21, 2018
Great book with extensive coverage coupled with lots of intuition and insight. As one reviewer mentions, it is certainly not a programming book and is written as a university textbook. The writing is clear and precise, but it is certainly not a programming... See more
Great book with extensive coverage coupled with lots of intuition
and insight. As one reviewer mentions, it is certainly not a programming
book and is written as a university textbook. The writing is clear
and precise, but it is certainly not a programming recipe or cookbook either.
I couple this book with other similar books like Manning''s books and
Jurafsky''s book to get a full picture of the field. I must say that
the coverage is heads and shoulders above other books in depth and breadth.
How many books discuss IR, general machine learning, deep learning,
search engines, and information extraction all at one place?
5 people found this helpful
Helpful
Report
Pranab Ghosh
5.0 out of 5 starsVerified Purchase
Must read for anyone interested in NLP
Reviewed in the United States on May 13, 2019
It''s an excellent book with the right balance between math and intuitive explanations. Any one interested in the nexus between NLP and Machine Learning should read this book. My only negative comment is that all topics are not covered uniformly e.g. there is... See more
It''s an excellent book with the right balance between math and intuitive explanations. Any one interested in the nexus between NLP and Machine Learning should read this book. My only negative comment is that all topics are not covered uniformly e.g. there is disproportionately higher coverage of text classification, compared to other topics.
Helpful
Report
Nabeel Shirazi
5.0 out of 5 starsVerified Purchase
Great book
Reviewed in the United States on March 22, 2019
Very well written and easy to read. Covers the principles and builds on those.
One person found this helpful
Helpful
Report
Olaf
1.0 out of 5 starsVerified Purchase
too vague to be useful
Reviewed in the United States on July 10, 2018
very vague
3 people found this helpful
Helpful
Report
mlgeek
5.0 out of 5 stars
Very lucid exposition of text machine learning
Reviewed in the United States on July 13, 2018
Provides a detailed treatment of the text machine learning. Will appeal to professors, students and researchers. The book is officially classified as a textbook, and is intended for classroom teaching in universities. The nice writing style also makes it... See more
Provides a detailed treatment of the text machine learning.
Will appeal to professors, students and researchers.
The book is officially classified as a textbook, and is intended
for classroom teaching in universities.
The nice writing style also makes it accessible to practitioners.
A lot of explanations are given in order to explain very difficult
mathematical concepts. The book makes a special effort to
explain the reasoning for different methods. The mathematics
and algorithms are described extremely well, and exercises
are available for class room teaching. The number of topics
covered is impressive, beginning from traditional machine
learning with bag of words, and including
deep learning/processing of text as sequences. The topic modeling
and matrix factorization discussions are presented in a very
integrated way, so that the advantages and disadvantages of different
methods become very clear. The lucidity of the book is
very high, and the concepts are easy to follow.
I strongly recommend the book, especially if you are looking for
gaining a better conceptual understanding.
4 people found this helpful
Helpful
Report
veemo
5.0 out of 5 stars
Excellent Book
Reviewed in the United States on March 20, 2019
As with every other book written by Charu, this is an excellent book - well researched, detailed & takes you through everything from the basic 123s of Text Analytics to the cutting edge research work of today. If you like this writing style & content organization, highly... See more
As with every other book written by Charu, this is an excellent book - well researched, detailed & takes you through everything from the basic 123s of Text Analytics to the cutting edge research work of today. If you like this writing style & content organization, highly recommend his other books - Recommender Systems, NNs & Deep Learning etc.
2 people found this helpful
Helpful
Report

Top reviews from other countries

Vinay
5.0 out of 5 starsVerified Purchase
Good book, received in good condn
Reviewed in India on October 10, 2019
Very informative and intuition based book. It is not some a book which one can breeze through, rather it should be worked through from page to page.
Report
See all reviews
Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Customers who viewed this item also viewed

Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Explore similar books

Tags that will help you discover similar books. 8 tags
Results for: 
Where do clickable book tags come from?
Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.
Sponsored

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale

Machine online high quality Learning for Text outlet online sale