Machine Learning For Email

Author : Drew Conway,John White
Publisher : "O'Reilly Media, Inc."
Page : 145 pages
File Size : 50,7 Mb
Release : 2011-10-27
Category : Computers
ISBN : 9781449314309

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Machine Learning for Email by Drew Conway,John White Pdf

This compact book explores standard tools for text classification, and teaches the reader how to use machine learning to decide whether a e-mail is spam or ham (binary classification), based on raw data from The SpamAssassin Public Corpus. Of course, sometimes the items in one class are not created equally, or we want to distinguish among them in some meaningful way. The second part of the book will look at how to not only filter spam from our email, but also placing "more important" messages at the top of the queue. This is a curated excerpt from the upcoming book "Machine Learning for Hackers."

Author : Drew Conway,John Myles White
Publisher : "O'Reilly Media, Inc."
Page : 148 pages
File Size : 51,7 Mb
Release : 2011-10-25
Category : Computers
ISBN : 9781449320706

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Machine Learning for Email by Drew Conway,John Myles White Pdf

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

Author : Drew Conway,John Myles White
Publisher : "O'Reilly Media, Inc."
Page : 324 pages
File Size : 52,9 Mb
Release : 2012-02-13
Category : Computers
ISBN : 9781449330538

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Machine Learning for Hackers by Drew Conway,John Myles White Pdf

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

Author : Drew Conway,John Myles White
Publisher : Unknown
Page : 146 pages
File Size : 46,6 Mb
Release : 2011
Category : Electrical engineering
ISBN : 144931483X

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Machine Learning for Email by Drew Conway,John Myles White Pdf

Author : Jean-Francois Boulicaut,Floriana Esposito,Fosca Giannotti,Dino Pedreschi
Publisher : Springer
Page : 582 pages
File Size : 40,9 Mb
Release : 2004-11-05
Category : Computers
ISBN : 9783540301158

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Machine Learning: ECML 2004 by Jean-Francois Boulicaut,Floriana Esposito,Fosca Giannotti,Dino Pedreschi Pdf

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).

Author : Yassine Maleh,Mohammad Shojafar,Mamoun Alazab,Youssef Baddi
Publisher : Springer Nature
Page : 539 pages
File Size : 51,8 Mb
Release : 2020-12-14
Category : Computers
ISBN : 9783030570248

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Machine Intelligence and Big Data Analytics for Cybersecurity Applications by Yassine Maleh,Mohammad Shojafar,Mamoun Alazab,Youssef Baddi Pdf

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.

Author : Soma Halder,Sinan Ozdemir
Publisher : Packt Publishing Ltd
Page : 306 pages
File Size : 42,8 Mb
Release : 2018-12-31
Category : Computers
ISBN : 9781788990967

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Hands-On Machine Learning for Cybersecurity by Soma Halder,Sinan Ozdemir Pdf

Get into the world of smart data security using machine learning algorithms and Python libraries Key FeaturesLearn machine learning algorithms and cybersecurity fundamentalsAutomate your daily workflow by applying use cases to many facets of securityImplement smart machine learning solutions to detect various cybersecurity problemsBook Description Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems What you will learnUse machine learning algorithms with complex datasets to implement cybersecurity conceptsImplement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problemsLearn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDAUnderstand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimesUse TensorFlow in the cybersecurity domain and implement real-world examplesLearn how machine learning and Python can be used in complex cyber issuesWho this book is for This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book

Author : Jean-Francois Boulicaut
Publisher : Springer Science & Business Media
Page : 597 pages
File Size : 52,7 Mb
Release : 2004-09-07
Category : Computers
ISBN : 9783540231059

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Machine Learning: ECML 2004 by Jean-Francois Boulicaut Pdf

This book constitutes the refereed proceedings of the 15th European Conference on Machine Learning, ECML 2004, held in Pisa, Italy, in September 2004, jointly with PKDD 2004. The 45 revised full papers and 6 revised short papers presented together with abstracts of 5 invited talks were carefully reviewed and selected from 280 papers submitted to ECML and 107 papers submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.

Author : Iraj Sadegh Amiri,O.A. Akanbi,E. Fazeldehkordi
Publisher : Syngress
Page : 101 pages
File Size : 49,7 Mb
Release : 2014-12-05
Category : Computers
ISBN : 9780128029466

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A Machine-Learning Approach to Phishing Detection and Defense by Iraj Sadegh Amiri,O.A. Akanbi,E. Fazeldehkordi Pdf

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

Author : Dale Lane
Publisher : No Starch Press
Page : 290 pages
File Size : 53,8 Mb
Release : 2021-01-19
Category : Computers
ISBN : 9781718500570

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Machine Learning for Kids by Dale Lane Pdf

A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based, award-winning companion website, you'll see how easy it is to add machine learning to your own projects. You don't even need to know how to code! As you work through the book you'll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve their accuracy. You'll turn your models into fun computer games and apps, and see what happens when they get confused by bad data. You'll build 13 projects step-by-step from the ground up, including: • Rock, Paper, Scissors game that recognizes your hand shapes • An app that recommends movies based on other movies that you like • A computer character that reacts to insults and compliments • An interactive virtual assistant (like Siri or Alexa) that obeys commands • An AI version of Pac-Man, with a smart character that knows how to avoid ghosts NOTE: This book includes a Scratch tutorial for beginners, and step-by-step instructions for every project. Ages 12+

Author : Mettu Srinivas,G. Sucharitha,Anjanna Matta
Publisher : John Wiley & Sons
Page : 372 pages
File Size : 48,8 Mb
Release : 2021-08-10
Category : Computers
ISBN : 9781119769248

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Machine Learning Algorithms and Applications by Mettu Srinivas,G. Sucharitha,Anjanna Matta Pdf

Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.

Author : John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Publisher : MIT Press
Page : 853 pages
File Size : 44,5 Mb
Release : 2020-10-20
Category : Computers
ISBN : 9780262361101

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Fundamentals of Machine Learning for Predictive Data Analytics, second edition by John D. Kelleher,Brian Mac Namee,Aoife D'Arcy Pdf

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.

Author : Gavriel Salvendy,Michael J. Smith
Publisher : Springer
Page : 877 pages
File Size : 47,5 Mb
Release : 2009-07-01
Category : Computers
ISBN : 3642025587

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Human Interface and the Management of Information. Information and Interaction by Gavriel Salvendy,Michael J. Smith Pdf

The 13th International Conference on Human–Computer Interaction, HCI Inter- tional 2009, was held in San Diego, California, USA, July 19–24, 2009, jointly with the Symposium on Human Interface (Japan) 2009, the 8th International Conference on Engineering Psychology and Cognitive Ergonomics, the 5th International Conference on Universal Access in Human–Computer Interaction, the Third International Conf- ence on Virtual and Mixed Reality, the Third International Conference on Internati- alization, Design and Global Development, the Third International Conference on Online Communities and Social Computing, the 5th International Conference on Augmented Cognition, the Second International Conference on Digital Human Mod- ing, and the First International Conference on Human-Centered Design. A total of 4,348 individuals from academia, research institutes, industry and gove- mental agencies from 73 countries submitted contributions, and 1,425 papers that were judged to be of high scientific quality were included in the program. These papers - dress the latest research and development efforts and highlight the human aspects of the design and use of computing systems. The papers accepted for presentation thoroughly cover the entire field of human–computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas.

Author : Cruz-Cunha, Maria Manuela,Mateus-Coelho, Nuno Ricardo
Publisher : IGI Global
Page : 753 pages
File Size : 54,6 Mb
Release : 2020-08-21
Category : Computers
ISBN : 9781799857297

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Handbook of Research on Cyber Crime and Information Privacy by Cruz-Cunha, Maria Manuela,Mateus-Coelho, Nuno Ricardo Pdf

In recent years, industries have transitioned into the digital realm, as companies and organizations are adopting certain forms of technology to assist in information storage and efficient methods of production. This dependence has significantly increased the risk of cyber crime and breaches in data security. Fortunately, research in the area of cyber security and information protection is flourishing; however, it is the responsibility of industry professionals to keep pace with the current trends within this field. The Handbook of Research on Cyber Crime and Information Privacy is a collection of innovative research on the modern methods of crime and misconduct within cyber space. It presents novel solutions to securing and preserving digital information through practical examples and case studies. While highlighting topics including virus detection, surveillance technology, and social networks, this book is ideally designed for cybersecurity professionals, researchers, developers, practitioners, programmers, computer scientists, academicians, security analysts, educators, and students seeking up-to-date research on advanced approaches and developments in cyber security and information protection.

Author : Fei Hu,Xiali Hei
Publisher : CRC Press
Page : 347 pages
File Size : 48,9 Mb
Release : 2023-06-05
Category : Computers
ISBN : 9781000878875

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AI, Machine Learning and Deep Learning by Fei Hu,Xiali Hei Pdf

Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use. While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security). Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects: This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered