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Fight Fraud with Machine Learning (MEAP v2)
Author: Ashish Ranjan Jha
Year: 2023
Format: PDF | EPUB
File size: 10.1 MB
Language: ENG



Fight Fraud with Machine Learning MEAP v2: The Evolution of Technology for Human Survival In an increasingly digital world, financial and corporate fraud have become an unfortunate reality, leaving behind a trail of digital evidence that can be used to identify and apprehend the perpetrators. With the rise of machine learning (ML) techniques, it is now possible to detect and prevent fraudulent activities with greater accuracy and speed than ever before. In "Fight Fraud with Machine Learning MEAP v2 we will delve into the process of technology evolution and its impact on human survival, as well as the need to develop a personal paradigm for understanding the technological advancements that shape our society. The book begins by exploring the fundamentals of ML and its applications in fraud detection, highlighting the importance of developing scalable and tunable models that can adapt to new threats and evolving technologies. We will examine the various types of common scams such as phishing and credit card fraud, as well as emerging threats like voice spoofing and deepfakes, and learn how to build and deploy state-of-the-art fraud detection systems using Python.
Борьба с мошенничеством с помощью машинного обучения MEAP v2: Эволюция технологий для выживания человека Во все более цифровом мире финансовое и корпоративное мошенничество стало неудачной реальностью, оставив после себя след цифровых доказательств, которые можно использовать для выявления и задержания преступников. С ростом технологий машинного обучения (ML) теперь стало возможным обнаруживать и предотвращать мошеннические действия с большей точностью и скоростью, чем когда-либо прежде. В «Борьбе с мошенничеством с помощью машинного обучения MEAP v2» мы углубимся в процесс эволюции технологий и его влияние на выживание человека, а также в необходимость разработки личной парадигмы для понимания технологических достижений, которые формируют наше общество. Книга начинается с изучения основ ML и его приложений в области обнаружения мошенничества, подчеркивая важность разработки масштабируемых и настраиваемых моделей, способных адаптироваться к новым угрозам и развивающимся технологиям. Мы рассмотрим различные типы распространенных мошеннических действий, таких как фишинг и мошенничество с кредитными картами, а также новые угрозы, такие как подделка голоса и дипфейки, и научимся создавать и развертывать самые современные системы обнаружения мошенничества с помощью Python.
Lutter contre la fraude par le machine learning MEAP v2 : L'évolution des technologies pour la survie humaine Dans un monde de plus en plus numérique, la fraude financière et d'entreprise est devenue une réalité malheureuse, laissant derrière elle une trace de preuves numériques qui peuvent être utilisées pour identifier et arrêter les criminels. Avec l'augmentation des technologies d'apprentissage automatique (ML), il est maintenant possible de détecter et de prévenir les activités frauduleuses avec plus de précision et de rapidité que jamais auparavant. Dans « Lutter contre la fraude par l'apprentissage automatique MEAP v2 », nous allons approfondir le processus d'évolution de la technologie et son impact sur la survie humaine, ainsi que la nécessité de développer un paradigme personnel pour comprendre les progrès technologiques qui façonnent notre société. livre commence par une étude des bases de ML et de ses applications dans le domaine de la détection de la fraude, soulignant l'importance de développer des modèles évolutifs et personnalisables capables de s'adapter aux nouvelles menaces et aux technologies émergentes. Nous examinerons différents types d'activités frauduleuses courantes, telles que le phishing et la fraude par carte de crédit, ainsi que de nouvelles menaces telles que la contrefaçon de voix et de diptyques, et nous apprendrons à créer et à déployer les systèmes de détection de fraude les plus modernes avec Python.
Lucha contra el fraude mediante el aprendizaje automático MEAP v2: Evolución de la tecnología para la supervivencia humana En un mundo cada vez más digital, el fraude financiero y corporativo se ha convertido en una realidad desafortunada, dejando tras de sí un rastro de pruebas digitales que pueden utilizarse para identificar y detener criminales. Con el crecimiento de las tecnologías de aprendizaje automático (ML), ahora es posible detectar y prevenir acciones fraudulentas con más precisión y velocidad que nunca. En 'La lucha contra el fraude mediante el aprendizaje automático MEAP v2'profundizaremos en el proceso de evolución de la tecnología y su impacto en la supervivencia humana, así como en la necesidad de desarrollar un paradigma personal para entender los avances tecnológicos que forman nuestra sociedad. libro comienza con un estudio de los fundamentos del ML y sus aplicaciones en el campo de la detección de fraudes, destacando la importancia de desarrollar modelos escalables y personalizables capaces de adaptarse a las nuevas amenazas y tecnologías emergentes. Revisaremos diferentes tipos de actividades fraudulentas comunes, como el phishing y el fraude con tarjeta de crédito, así como nuevas amenazas como la falsificación de voz y dipfake, y aprenderemos a crear e implementar los sistemas de detección de fraude más modernos con Python.
A luta contra a fraude no ensino de máquinas MEAP v2: A evolução da tecnologia para a sobrevivência humana Em um mundo cada vez mais digital, a fraude financeira e corporativa tornou-se uma realidade falhada, deixando um rasto de evidências digitais que podem ser usadas para identificar e prender criminosos. Com o aumento da tecnologia de aprendizagem de máquinas (ML), agora é possível detectar e prevenir fraudes com mais precisão e velocidade do que nunca. Em «Combate à fraude através do aprendizado de máquinas MEAP v2», vamos nos aprofundar no processo de evolução da tecnologia e no seu impacto na sobrevivência humana e na necessidade de desenvolver um paradigma pessoal para compreender os avanços tecnológicos que formam a nossa sociedade. O livro começa com o estudo dos fundamentos da ML e de suas aplicações na área de detecção de fraudes, enfatizando a importância de desenvolver modelos escaláveis e personalizáveis capazes de se adaptar a novas ameaças e tecnologias emergentes. Vamos considerar vários tipos de fraudes comuns, tais como phishing e fraude de cartões de crédito, e novas ameaças, como falsificação de voz e dípfacos, e aprender a criar e implementar os mais modernos sistemas de detecção de fraudes com Python.
Betrugsbekämpfung durch maschinelles rnen MEAP v2: Die Evolution der Technologie für das menschliche Überleben In einer zunehmend digitalen Welt ist Finanz- und Unternehmensbetrug zu einer unglücklichen Realität geworden und hinterlässt eine Spur digitaler Beweise, mit denen Kriminelle identifiziert und gefasst werden können. Mit dem Aufstieg von Machine arning (ML) -Technologien ist es jetzt möglich, betrügerische Aktivitäten mit größerer Genauigkeit und Geschwindigkeit als je zuvor zu erkennen und zu verhindern. In „Kampf gegen Betrug durch maschinelles rnen MEAP v2“ werden wir tiefer in den technologischen Evolutionsprozess und seine Auswirkungen auf das menschliche Überleben eintauchen und die Notwendigkeit, ein persönliches Paradigma zu entwickeln, um die technologischen Fortschritte zu verstehen, die unsere Gesellschaft prägen. Das Buch beginnt mit einer Untersuchung der Grundlagen von ML und seiner Anwendungen im Bereich der Betrugserkennung und unterstreicht die Bedeutung der Entwicklung skalierbarer und anpassbarer Modelle, die sich an neue Bedrohungen und sich entwickelnde Technologien anpassen können. Wir werden verschiedene Arten von gemeinsamen betrügerischen Aktivitäten wie Phishing und Kreditkartenbetrug sowie neue Bedrohungen wie Sprachfälschung und Deepfakes untersuchen und lernen, wie man modernste Betrugserkennungssysteme mit Python erstellt und einsetzt.
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Makine Öğrenimi ile Dolandırıcılıkla Mücadele MEAP v2: İnsanın Hayatta Kalması için Teknolojinin Evrimi Giderek dijitalleşen bir dünyada, finansal ve kurumsal dolandırıcılık talihsiz bir gerçeklik haline geldi ve suçluları tanımlamak ve yakalamak için kullanılabilecek bir dijital kanıt izi bıraktı. Makine öğrenimi (ML) teknolojilerinin yükselişiyle, dolandırıcılık faaliyetlerini her zamankinden daha fazla doğruluk ve hızla tespit etmek ve önlemek artık mümkün. "MEAP v2 Makine Öğrenimi ile Dolandırıcılıkla Mücadele'de, teknolojinin evrimini ve insan yaşamı üzerindeki etkisini ve toplumumuzu şekillendiren teknolojik gelişmeleri anlamak için kişisel bir paradigma geliştirme ihtiyacını araştırıyoruz. Kitap, ML'nin temellerini ve sahtekarlık tespitindeki uygulamalarını keşfederek, yeni tehditlere ve gelişen teknolojilere uyum sağlayabilecek ölçeklenebilir ve özelleştirilebilir modeller geliştirmenin önemini vurgulayarak başlıyor. Kimlik avı ve kredi kartı sahtekarlığı gibi farklı yaygın dolandırıcılık türlerinin yanı sıra ses kurcalama ve deepfakes gibi yeni ortaya çıkan tehditlere bakıyoruz ve Python kullanarak en gelişmiş dolandırıcılık tespit sistemlerinin nasıl oluşturulacağını ve dağıtılacağını öğreniyoruz.
مكافحة الاحتيال باستخدام التعلم الآلي MEAP v2: تطور التكنولوجيا من أجل بقاء الإنسان في عالم رقمي متزايد، أصبح الاحتيال المالي والشركات حقيقة مؤسفة، تاركًا وراءه مجموعة من الأدلة الرقمية التي يمكن استخدامها لتحديد المجرمين والقبض عليهم. مع ظهور تقنيات التعلم الآلي (ML)، أصبح من الممكن الآن اكتشاف ومنع الأنشطة الاحتيالية بدقة وسرعة أكبر من أي وقت مضى. في «مكافحة الاحتيال باستخدام التعلم الآلي MEAP v2»، نتعمق في تطور التكنولوجيا وتأثيرها على بقاء الإنسان، والحاجة إلى تطوير نموذج شخصي لفهم التطورات التكنولوجية التي تشكل مجتمعنا. يبدأ الكتاب باستكشاف أساسيات ML وتطبيقاتها في الكشف عن الاحتيال، مع التأكيد على أهمية تطوير نماذج قابلة للتطوير وقابلة للتخصيص يمكنها التكيف مع التهديدات الجديدة والتكنولوجيات المتطورة. نحن ننظر إلى أنواع مختلفة من عمليات الاحتيال الشائعة مثل التصيد الاحتيالي والاحتيال على بطاقات الائتمان، بالإضافة إلى التهديدات الناشئة مثل العبث الصوتي والتزييف العميق، ونتعلم كيفية بناء ونشر أحدث أنظمة الكشف عن الاحتيال باستخدام Python.

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