Machine Learning for Email

Machine Learning for Email
Title Machine Learning for Email PDF eBook
Author Drew Conway
Publisher "O'Reilly Media, Inc."
Pages 145
Release 2011-10-25
Genre Computers
ISBN 1449320708

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If you’re an experienced programmer willing to crunch data, this concise guide will show you how to use machine learning to work with email. You’ll learn how to write algorithms that automatically sort and redirect email based on statistical patterns. Authors Drew Conway and John Myles White approach the process in a practical fashion, using a case-study driven approach rather than a traditional math-heavy presentation. This book also includes a short tutorial on using the popular R language to manipulate and analyze data. You’ll get clear examples for analyzing sample data and writing machine learning programs with R. Mine email content with R functions, using a collection of sample files Analyze the data and use the results to write a Bayesian spam classifier Rank email by importance, using factors such as thread activity Use your email ranking analysis to write a priority inbox program Test your classifier and priority inbox with a separate email sample set

Machine Learning for Hackers

Machine Learning for Hackers
Title Machine Learning for Hackers PDF eBook
Author Drew Conway
Publisher "O'Reilly Media, Inc."
Pages 323
Release 2012-02-13
Genre Computers
ISBN 1449330533

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If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data

A Machine-Learning Approach to Phishing Detection and Defense

A Machine-Learning Approach to Phishing Detection and Defense
Title A Machine-Learning Approach to Phishing Detection and Defense PDF eBook
Author O.A. Akanbi
Publisher Syngress
Pages 101
Release 2014-12-05
Genre Computers
ISBN 0128029463

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Phishing is one of the most widely-perpetrated forms of cyber attack, used to gather sensitive information such as credit card numbers, bank account numbers, and user logins and passwords, as well as other information entered via a web site. The authors of A Machine-Learning Approach to Phishing Detetion and Defense have conducted research to demonstrate how a machine learning algorithm can be used as an effective and efficient tool in detecting phishing websites and designating them as information security threats. This methodology can prove useful to a wide variety of businesses and organizations who are seeking solutions to this long-standing threat. A Machine-Learning Approach to Phishing Detetion and Defense also provides information security researchers with a starting point for leveraging the machine algorithm approach as a solution to other information security threats. - Discover novel research into the uses of machine-learning principles and algorithms to detect and prevent phishing attacks - Help your business or organization avoid costly damage from phishing sources - Gain insight into machine-learning strategies for facing a variety of information security threats

Machine Learning: ECML 2004

Machine Learning: ECML 2004
Title Machine Learning: ECML 2004 PDF eBook
Author Jean-Francois Boulicaut
Publisher Springer
Pages 597
Release 2004-11-05
Genre Computers
ISBN 3540301151

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The proceedings of ECML/PKDD 2004 are published in two separate, albeit - tertwined,volumes:theProceedingsofthe 15thEuropeanConferenceonMac- ne Learning (LNAI 3201) and the Proceedings of the 8th European Conferences on Principles and Practice of Knowledge Discovery in Databases (LNAI 3202). The two conferences were co-located in Pisa, Tuscany, Italy during September 20–24, 2004. It was the fourth time in a row that ECML and PKDD were co-located. - ter the successful co-locations in Freiburg (2001), Helsinki (2002), and Cavtat- Dubrovnik (2003), it became clear that researchersstrongly supported the or- nization of a major scienti?c event about machine learning and data mining in Europe. We are happy to provide some statistics about the conferences. 581 di?erent papers were submitted to ECML/PKDD (about a 75% increase over 2003); 280 weresubmittedtoECML2004only,194weresubmittedtoPKDD2004only,and 107weresubmitted to both.Aroundhalfofthe authorsforsubmitted papersare from outside Europe, which is a clear indicator of the increasing attractiveness of ECML/PKDD. The Program Committee members were deeply involved in what turned out to be a highly competitive selection process. We assigned each paper to 3 - viewers, deciding on the appropriate PC for papers submitted to both ECML and PKDD. As a result, ECML PC members reviewed 312 papers and PKDD PC members reviewed 269 papers. We accepted for publication regular papers (45 for ECML 2004 and 39 for PKDD 2004) and short papers that were as- ciated with poster presentations (6 for ECML 2004 and 9 for PKDD 2004). The globalacceptance ratewas14.5%for regular papers(17% if we include the short papers).

Machine Intelligence and Big Data Analytics for Cybersecurity Applications

Machine Intelligence and Big Data Analytics for Cybersecurity Applications
Title Machine Intelligence and Big Data Analytics for Cybersecurity Applications PDF eBook
Author Yassine Maleh
Publisher Springer Nature
Pages 539
Release 2020-12-14
Genre Computers
ISBN 303057024X

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This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis. Cyber-attacks have posed real and wide-ranging threats for the information society. Detecting cyber-attacks becomes a challenge, not only because of the sophistication of attacks but also because of the large scale and complex nature of today’s IT infrastructures. It discusses novel trends and achievements in machine intelligence and their role in the development of secure systems and identifies open and future research issues related to the application of machine intelligence in the cybersecurity field. Bridging an important gap between machine intelligence, big data, and cybersecurity communities, it aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in grasping its diverse facets and exploring the latest advances on machine intelligence and big data analytics for cybersecurity applications.

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Fundamentals of Machine Learning for Predictive Data Analytics, second edition
Title Fundamentals of Machine Learning for Predictive Data Analytics, second edition PDF eBook
Author John D. Kelleher
Publisher MIT Press
Pages 853
Release 2020-10-20
Genre Computers
ISBN 0262361108

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The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Mastering Email Marketing: A Step-by-Step Guide

Mastering Email Marketing: A Step-by-Step Guide
Title Mastering Email Marketing: A Step-by-Step Guide PDF eBook
Author Rudhrah Keshav
Publisher Rudhrah Gourav
Pages 26
Release 2024-07-31
Genre Business & Economics
ISBN

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Email marketing is a powerful tool in the digital marketing arsenal, offering businesses the ability to reach their target audience directly in their inbox. With the right strategy, email marketing can lead to increased brand awareness, customer loyalty, and significant revenue growth. This book aims to provide a comprehensive guide to mastering email marketing, from understanding its fundamentals to implementing advanced techniques.