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Tag: Statistical
Foundations of Statistical Natural Language Processing by Christopher Manning
Foundations of Statistical Natural Language Processing by Christopher Manning
Price :10.49– 8.39
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Foundations of Statistical Natural Language Processing by Christopher Manning is a comprehensive guide to the fundamental concepts and techniques of natural language processing (NLP). With a focus on statistical methods, this book provides a solid foundation for understanding how to analyze and process human language using computational tools.In this book, Manning covers a wide range of topics, including language modeling, part-of-speech tagging, parsing, and information retrieval. He also discusses the use of machine learning algorithms and statistical techniques to build NLP systems that can understand and generate human language.
With its clear and concise explanations, Foundations of Statistical Natural Language Processing is an essential resource for students, researchers, and practitioners in the field of NLP. Whether you are new to the subject or looking to deepen your knowledge, this book offers valuable insights and practical advice for building effective NLP systems.
Overall, Foundations of Statistical Natural Language Processing is a must-read for anyone interested in the fascinating field of computational linguistics and NLP. Christopher Manning’s expertise and engaging writing style make this book an invaluable resource for understanding the foundations of statistical NLP.
#Foundations #Statistical #Natural #Language #Processing #Christopher #Manning
Statistical Machine Translation – Hardcover, by Koehn Philipp – Very Good
Statistical Machine Translation – Hardcover, by Koehn Philipp – Very Good
Price : 13.39
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Statistical Machine Translation – Hardcover, by Koehn Philipp – Very GoodLooking for a comprehensive guide to statistical machine translation? Look no further than Koehn Philipp’s “Statistical Machine Translation” hardcover book. This highly-regarded resource offers a detailed exploration of the principles and techniques behind statistical machine translation, making it an essential read for anyone interested in this rapidly evolving field.
With a focus on practical applications and real-world examples, Koehn Philipp provides readers with a thorough understanding of the key concepts and methodologies that underpin statistical machine translation. From the basics of language processing to advanced techniques for improving translation accuracy, this book covers it all in a clear and accessible manner.
Whether you’re a seasoned professional in the field or a newcomer looking to expand your knowledge, “Statistical Machine Translation” is sure to become a valuable addition to your library. With its engaging writing style and wealth of information, this book is a must-read for anyone interested in the fascinating world of machine translation.
#Statistical #Machine #Translation #Hardcover #Koehn #Philipp #Good
Statistical Machine Translation by Philipp Koehn (2009, Hardcover)
Statistical Machine Translation by Philipp Koehn (2009, Hardcover)
Price : 29.99
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Statistical Machine Translation: A Comprehensive Guide by Philipp Koehn (2009, Hardcover)If you’re interested in delving into the world of machine translation, then Philipp Koehn’s comprehensive guide is a must-read. In this groundbreaking book, Koehn provides a detailed overview of statistical machine translation, covering both the theoretical foundations and practical applications of this cutting-edge technology.
With a focus on statistical methods and algorithms, Koehn explains how machine translation systems can be trained on large amounts of bilingual text to generate accurate translations. He also discusses the challenges and limitations of current machine translation systems, as well as potential future developments in the field.
Whether you’re a researcher, student, or industry professional, this book offers valuable insights and practical guidance on statistical machine translation. So, if you want to stay ahead of the curve in the rapidly evolving field of machine translation, be sure to add this book to your reading list.
#Statistical #Machine #Translation #Philipp #Koehn #Hardcover
SAS and R : Data Management, Statistical Analysis, and Graphics, Hardcover by…
SAS and R : Data Management, Statistical Analysis, and Graphics, Hardcover by…
Price : 108.06
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“SAS and R: Mastering Data Management, Statistical Analysis, and Graphics with this Comprehensive Hardcover Guide”
#SAS #Data #Management #Statistical #Analysis #Graphics #Hardcover #by.., Data Management
Diagnostic and Statistical Manual of Mental Disorders DSM-5-TR Free Shipping
Diagnostic and Statistical Manual of Mental Disorders DSM-5-TR Free Shipping
Price :38.95– 34.99
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Are you looking to expand your knowledge of mental health disorders? Look no further than the Diagnostic and Statistical Manual of Mental Disorders DSM-5-TR! This comprehensive guide is a must-have for any mental health professional or student.And the best part? We are offering free shipping on all orders of the DSM-5-TR! This is your chance to get your hands on this invaluable resource without any additional cost.
Don’t miss out on this special offer. Order your copy of the DSM-5-TR today and take your understanding of mental health to the next level.
#Diagnostic #Statistical #Manual #Mental #Disorders #DSM5TR #Free #Shipping,students and
professionals
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Price : 22.86
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The Elements of Statistical Learning: Data Mining, Inference, and PredictionIn the world of data science, statistical learning is a crucial tool for making sense of complex datasets and making accurate predictions. The Elements of Statistical Learning, written by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, is a comprehensive guide to understanding the key concepts and techniques in statistical learning.
Data mining is the process of extracting patterns and knowledge from large datasets. This is done through various techniques such as clustering, classification, and regression. Inference involves making predictions and drawing conclusions based on the data at hand. Prediction, on the other hand, is the act of using statistical models to forecast future outcomes.
The Elements of Statistical Learning covers a wide range of topics, including linear models, tree-based methods, support vector machines, and neural networks. It also delves into the principles of model selection, regularization, and cross-validation.
Whether you are a beginner in the field of data science or a seasoned professional, The Elements of Statistical Learning is a must-read for anyone looking to deepen their understanding of data mining, inference, and prediction. With its clear explanations, practical examples, and insightful discussions, this book is sure to become a valuable resource in your statistical learning journey.
#Elements #Statistical #Learning #Data #Mining #Inference #Prediction
Introduction to Statistical and Machine Learning Methods for Data Science
Price: $46.54
(as of Dec 25,2024 22:37:08 UTC – Details)
ASIN : B09C2DVZHR
Publisher : SAS Institute (August 6, 2021)
Publication date : August 6, 2021
Language : English
File size : 5957 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 172 pages
Page numbers source ISBN : 1953329608
Data science is a rapidly growing field that combines statistics, computer science, and domain knowledge to extract valuable insights and information from data. Statistical and machine learning methods are essential tools for data scientists to analyze and interpret data, make predictions, and uncover patterns and trends.In this post, we will provide an introduction to statistical and machine learning methods commonly used in data science. Statistical methods involve using mathematical models to analyze and interpret data, while machine learning methods involve algorithms that can learn from and make predictions based on data.
Some commonly used statistical methods in data science include:
1. Descriptive statistics: This involves summarizing and describing the main features of a dataset, such as mean, median, mode, variance, and standard deviation.
2. Inferential statistics: This involves making inferences and predictions about a population based on a sample of data, using techniques such as hypothesis testing and confidence intervals.
3. Regression analysis: This involves modeling the relationship between a dependent variable and one or more independent variables, to make predictions and understand the impact of the independent variables on the dependent variable.
Machine learning methods, on the other hand, involve using algorithms to learn patterns and make predictions from data. Some commonly used machine learning methods in data science include:
1. Supervised learning: This involves training a model on labeled data (data with known outcomes) to make predictions on new, unseen data. Examples of supervised learning algorithms include linear regression, logistic regression, decision trees, and support vector machines.
2. Unsupervised learning: This involves training a model on unlabeled data (data without known outcomes) to discover patterns and relationships in the data. Examples of unsupervised learning algorithms include clustering algorithms like k-means and hierarchical clustering.
3. Deep learning: This involves using artificial neural networks to learn complex patterns and relationships in data. Deep learning has been particularly successful in tasks such as image and speech recognition.
By understanding and applying statistical and machine learning methods, data scientists can uncover valuable insights and make informed decisions based on data. In future posts, we will dive deeper into specific statistical and machine learning methods and their applications in data science. Stay tuned!
#Introduction #Statistical #Machine #Learning #Methods #Data #Science
Foundations of Statistical Natural Language Processing – Hardcover – VERY GOOD
Foundations of Statistical Natural Language Processing – Hardcover – VERY GOOD
Price : 14.60
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Foundations of Statistical Natural Language Processing – Hardcover – VERY GOODLooking to dive into the world of natural language processing? Look no further than this very good condition hardcover book on the foundations of statistical NLP.
This comprehensive guide covers all the essential concepts and techniques in the field, from basic linguistic principles to advanced machine learning algorithms. Whether you’re a beginner or an experienced practitioner, this book has something for everyone.
With clear explanations, practical examples, and insightful discussions, this book is a must-have for anyone interested in NLP. Don’t miss out on this opportunity to enhance your skills and knowledge in this exciting and rapidly growing field.
Get your hands on this very good condition hardcover copy of Foundations of Statistical Natural Language Processing today!
#Foundations #Statistical #Natural #Language #Processing #Hardcover #GOOD
The Elements of Statistical Learning: Data Mining, Inference, and Prediction…
The Elements of Statistical Learning: Data Mining, Inference, and Prediction…
Price : 29.99
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The Elements of Statistical Learning: Data Mining, Inference, and PredictionIn the world of data science and machine learning, understanding the principles behind statistical learning is crucial for making accurate predictions and drawing meaningful insights from data. The Elements of Statistical Learning, a renowned textbook by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, provides a comprehensive overview of the key concepts and techniques in this field.
Data mining, the process of uncovering patterns and relationships in large datasets, is a fundamental aspect of statistical learning. By utilizing various algorithms and statistical methods, data miners can extract valuable information from raw data and use it to make informed decisions.
Inference, on the other hand, involves drawing conclusions and making predictions based on data analysis. Statistical inference techniques allow researchers to make inferences about populations based on sample data, enabling them to make reliable predictions about future events or trends.
Prediction, the final element of statistical learning, involves using data to forecast future outcomes. By building predictive models and analyzing data patterns, researchers can make accurate predictions about future events, trends, or behaviors.
Overall, The Elements of Statistical Learning provides a comprehensive overview of these key elements, offering insights into the principles and techniques that underpin data mining, inference, and prediction. Whether you’re a seasoned data scientist or a newcomer to the field, this textbook is a valuable resource for understanding the fundamentals of statistical learning and applying them to real-world problems.
#Elements #Statistical #Learning #Data #Mining #Inference #Prediction..
Foundations of Statistical Natural Language Processing
Price:$125.00– $69.89
(as of Dec 24,2024 11:29:41 UTC – Details)
Publisher : The MIT Press; 1st edition (June 18, 1999)
Language : English
Hardcover : 620 pages
ISBN-10 : 0262133601
ISBN-13 : 978-0262133609
Reading age : 18 years and up
Grade level : 12 and up
Item Weight : 2.89 pounds
Dimensions : 9.46 x 8.04 x 1.25 inches
Foundations of Statistical Natural Language ProcessingStatistical Natural Language Processing (NLP) is a subfield of artificial intelligence and linguistics that focuses on the development of algorithms and models to process and understand human language. It combines techniques from computer science, machine learning, and linguistics to enable computers to analyze and generate human language text.
The foundations of Statistical NLP lie in the following key concepts:
1. Probability theory: Statistical NLP relies heavily on the principles of probability theory to model uncertainties and make predictions about language data. Techniques such as n-gram models, hidden Markov models, and probabilistic context-free grammars are commonly used to estimate the likelihood of different sequences of words in a given language.
2. Machine learning: Machine learning algorithms play a crucial role in Statistical NLP by enabling computers to learn patterns and relationships in language data. Supervised learning techniques, such as support vector machines and neural networks, are commonly used for tasks such as text classification, named entity recognition, and sentiment analysis.
3. Linguistic theory: Linguistic theories provide a theoretical framework for understanding the structure and meaning of human language. Concepts such as syntax, semantics, and pragmatics are essential for developing NLP models that can accurately analyze and generate natural language text.
4. Computational linguistics: Computational linguistics is the interdisciplinary field that combines linguistics and computer science to study the computational aspects of human language. Techniques such as part-of-speech tagging, parsing, and machine translation are essential for building NLP systems that can effectively process and understand language data.
Overall, the foundations of Statistical NLP encompass a diverse range of techniques and theories from mathematics, computer science, and linguistics. By combining these disciplines, researchers and practitioners can develop advanced NLP systems that can perform a wide range of tasks, from language translation to sentiment analysis, with a high degree of accuracy and efficiency.
#Foundations #Statistical #Natural #Language #Processing
