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Alferink B: Programmierung mit SCL und
dem TIA Portal

Programmierung mit SCL und dem TIA Portal

Einführung in die SPS-Programmierung mit S7-1500 und S7-1200

The asset management industry is undergoing a paradigm shift toward automation, transparency, and data-driven decision-making. Traditional tools (Excel, Bloomberg) are being replaced by programmable, scalable solutions. Yet most finance professionals lack accessible, practical training in applying Python to real portfolio problems.Python for Asset Management fills that gap. The book empowers non-programmers - portfolio managers, risk analysts, and students - to implement advanced models themselves. It responds to the growing demand for quantitative literacy in finance, especially in sustainable investing and smart beta strategies, areas of active research for both of the authors.Features<ul> <li>31 hands-on Python exercises with real data and executable code.</li> <li>Complete GitHub repository (MIT License) with all scripts, data pipelines, and results.</li> <li>Step-by-step implementation of VaR (historical, parametric, Monte Carlo), bond immunization, and factor models.</li> <li>Real-world decision tools - e.g., build a bullet/barbell/ladder bond portfolio, run Brinson-Fachler attribution, or backtest smart beta vs. index.</li> <li>Immediate applicability - every exercise produces a deliverable (e.g., optimal weights, risk report, attribution table) ready for client meetings.</li> <li>Focus on practical asset management workflows, not just theory.</li> </ul>

Python for Asset Management

Graduate to the next level of your software development career, learning the tools you need to successfully manage the complexity of modern software systems. Whether you are a developer at a small software company or one of many developers at a large enterprise, your success directly correlates to the ability of your development team to rapidly respond to change. In today’s world, developers face increasingly complex challenges when it comes to requirements, technology, solution hosting, support, and pace of change. This book will help you put on the lens of a software engineer. You will come away with an understanding of how to view the entire spectrum of the software development process, learn valuable concepts, and apply these principles through meaningful examples.
What You Will Learn
<ul>
<li>Move beyond being a programmer to being a professional software engineer

</li><li>Spend more time developing software; minimize time spent dealing with ineffective or inadequate processes

</li><li>Reduce errors in judgment and provide predictable outcomes while maintaining agility and responsiveness using Lean and Agile practices

</li><li>Identify and effectively manage the various types of complexity present in modern software development

</li><li>Know the steps you can take to ensure a shared understanding among stakeholders

</li><li>Discover tools to validate user experience early and often to minimize costly re-work

</li><li>Develop software designs and architectures that enable long-term business agility

</li><li>Implement patterns and processes that result in "falling into the pit of success" instead of into the "pit of failure"

</li><li>Adopt processes and patterns that will result in pervasive "institutionalized" quality

</li><li>Think differently about the responsibilities and accountabilities of essential technical leadership roles that will ensure team maturity and growth

</li><li>Understand what it means to be a professional engineer and how to take steps towards achieving true professionalism



Who This Book Is For
This book is for software developers and team leaders who have struggled to implement design and development best practices due to lack of team resources, in-depth knowledge, or experience, and want a book designed to provide the confidence and foundational skills needed to achieve success.
</li></ul>

Achieving Predictable Outcomes Through a System for Software Development

Lean Software Systems Engineering for Developers

This book is a hands-on guide designed to help readers understand, build, and deploy powerful AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic systems, and intelligent chatbots.

Starting with the fundamentals—LLM architecture, tokenization, APIs, and fine-tuning—the book gradually builds toward complex, integrated systems. Readers will learn to implement RAG pipelines using vector databases like FAISS and Pinecone, develop autonomous AI agents that complete multi-step tasks, and create real-world chatbots that understand and adapt to user needs. The approach is project-driven: each chapter includes visual explanations, step-by-step code walkthroughs, and deployment-ready examples. From building a personal assistant that searches your notes to creating a scheduling agent, every project reinforces both technical skills and applied understanding. It emphasizes clarity, inclusivity, and real-world relevance—helping readers move confidently from basic understanding to complex applications.

Whether you're exploring Agentic AI or looking to build production-ready systems, this book gives you the tools to turn curiosity into capability—and innovation into impact.

What you will learn:

Build intelligent chatbots and tools using LLMs like GPT, LLaMA, and Mistral with guided development steps.

Combine LLMs with vector databases like FAISS and Pinecone to create accurate, context-aware AI systems.

Design AI agents capable of planning and executing complex workflows for automation and decision-making.

Apply prompt engineering, memory, and multimodal tools to build real-world AI apps for your project portfolio.




Who this book is for:

Machine Learning engineers, data scientists, and AI professionals interested in learning how to build real-world AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and intelligent chatbots.

This book is a hands-on guide designed to help readers understand, build, and deploy powerful AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic systems, and intelligent chatbots.Starting with the fundamentals—LLM architecture, tokenization, APIs, and fine-tuning—the book gradually builds toward complex, integrated systems. Readers will learn to implement RAG pipelines using vector databases like FAISS and Pinecone, develop autonomous AI agents that complete multi-step tasks, and create real-world chatbots that understand and adapt to user needs. The approach is project-driven: each chapter includes visual explanations, step-by-step code walkthroughs, and deployment-ready examples. From building a personal assistant that searches your notes to creating a scheduling agent, every project reinforces both technical skills and applied understanding. It emphasizes clarity, inclusivity, and real-world relevance—helping readers move confidently from basic understanding to complex applications.Whether you're exploring Agentic AI or looking to build production-ready systems, this book gives you the tools to turn curiosity into capability—and innovation into impact.What you will learn:<ul><li>Build intelligent chatbots and tools using open-source LLMs like GPT, LLaMA, and Mistral with guided deployment steps.</li><li>Combine LLMs with vector databases like FAISS and Pinecone to create accurate, context-aware AI systems.</li><li>Design AI agents capable of planning and executing complex workflows for automation and decision-making.</li><li>Apply prompt engineering, memory, and multimodal tools to build real-world AI apps for your project portfolio.</li></ul>Who this book is for:Machine Learning engineers, data scientists, and AI professionals interested in learning how to build real-world AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and intelligent chatbots.

Architectures, Applications, and Real-World Deployments of Large Language Models

Mastering Large Language Models

Client Platform Engineers, device administrators, and others charged with managing large fleets of devices need to be able to wield open-source tools, conform them to their unique use cases, and get them on devices securely. This has traditionally been done with other languages, some of which have evolved since the early days of Unix - the early days of the very idea of an operating system. But the increasingly closed nature of Apple devices means that these skills need to move to Swift, and all the good and bad that comes with that.


This book is about how to build Swift applications that automate tasks on Apple devices. Things like pulling information down from a Mobile Device Management (MDM) solution to configure settings, deliver apps to devices, and gain telemetry into areas of the system that might not otherwise be available to legacy scripting tools that can't get entitlements to manage various aspects of systems. Traditional Swift books don't cover these scripty automation tasks, or how to build software that can't go on the App Store. Yet that's exactly what admins need to do. We want to make the world a more secure place, and this is one of the ways we get there.


 


What You'll Learn


 How to wield Swift for secure device automation tasks.

 How to convert your code into compiled scripts.

 How to build functional programs for device inventory.

 How to integrate with enterprise systems for seamless operation.

 How to master Mobile Device Management (MDM) with Swift.





 


Who This Book Is For


This book is for client platform engineers, device administrators, and anyone managing large fleets of Apple devices.

Client Platform Engineers, device administrators, and others charged with managing large fleets of devices need to be able to wield open-source tools, conform them to their unique use cases, and get them on devices securely. This has traditionally been done with other languages, some of which have evolved since the early days of Unix – the early days of the very idea of an operating system. But the increasingly closed nature of Apple devices means that these skills need to move to Swift, and all the good and bad that comes with that.This book is about how to build Swift applications that automate tasks on Apple devices. Things like pulling information down from a Mobile Device Management (MDM) solution to configure settings, deliver apps to devices, and gain telemetry into areas of the system that might not otherwise be available to legacy scripting tools that can’t get entitlements to manage various aspects of systems. Traditional Swift books don’t cover these scripty automation tasks, or how to build software that can’t go on the App Store. Yet that’s exactly what admins need to do. We want to make the world a more secure place, and this is one of the ways we get there.&nbsp;What You'll Learn<ul><li>How to wield Swift for secure device automation tasks.</li><li>How to&nbsp;convert your code into compiled scripts.</li><li>How to build functional programs for device inventory.</li><li>How to integrate with enterprise systems for seamless operation.</li><li>How to master Mobile Device Management (MDM) with Swift.</li></ul>&nbsp;Who This Book Is ForThis book is for client platform engineers, device administrators, and anyone managing large fleets of Apple devices.

An Introduction to Swift Scripting and Programming for Apple Admins

Mastering Swift

Ready to get cooking with ABAP? Discover shortcuts and solutions for all of your regular ABAP tasks in this practical guide. Walk step by step through important programming techniques and tools, from table management to extension implementation, following "recipes" for each task along the way. Make the most of new models and frameworks with examples built around ABAP Cloud and the ABAP RESTful application programming model. Get all the solutions you need for the most common problems in ABAP development! Highlights include:1)System fields and runtime information2)Customer data browser3)Application log4)Change documents5)Lock objects6)Number range objects7)File upload8)Background processing9)Parallelization10)Email 11)Documentation

Ready to get cooking with ABAP? Discover shortcuts and solutions for all of your regular ABAP tasks in this practical guide. Walk step by step through important programming techniques and tools, from table management to extension implementation, following "recipes" for each task along the way. Make the most of new models and frameworks with examples built around ABAP Cloud and the ABAP RESTful application programming model. Get all the solutions you need for the most common problems in ABAP development! Highlights:System fields and runtime informationCustomer data browserApplication logChange documentsLock objectsNumber range objectsFile uploadBackground processingParallelizationEmail Documentation

Practical Recipes for Modern Programming

ABAP Cookbook

Dieses Buch vermittelt einen systematischen Einstieg in die Python-Programmierung und führt Schritt für Schritt durch alle wichtigen Grundlagen bis hin zu fortgeschrittenen Konzepten. Es beginnt mit den zentralen Sprachelementen wie Datentypen, Variablen, Operatoren, Zeichenketten und numerischen Funktionen und zeigt anhand klarer Beispiele, wie Python effizient eingesetzt wird.
Aufbauend darauf behandelt das Buch strukturierte Daten wie Listen, Tupel, Dictionaries und Sets sowie Kontrollstrukturen mit Verzweigungen und Schleifen. Im weiteren Verlauf werden Funktionen, Fehlerbehandlung und objektorientierte Programmierung ausführlich erklärt. Ein eigenes Kapitel widmet sich dem Arbeiten mit Dateien, inklusive Lesen, Schreiben, CSV-Verarbeitung und grundlegenden Funktionen des Betriebssystems.
Abgerundet wird das Werk durch einen großen Übungsteil mit praxisnahen Aufgaben und vollständigen Lösungen, die das eigenständige Lernen unterstützen und den Stoff festigen. Damit eignet sich das Buch sowohl für Einsteigerinnen und Einsteiger in Informatik und Data Science als auch für Anwenderinnen und Anwender, die ein solides Fundament für weiterführende Python-Anwendungen aufbauen möchten.

Dieses Buch vermittelt einen systematischen Einstieg in die Python-Programmierung und führt Schritt für Schritt durch alle wichtigen Grundlagen bis hin zu fortgeschrittenen Konzepten. Es beginnt mit den zentralen Sprachelementen wie Datentypen, Variablen, Operatoren, Zeichenketten und numerischen Funktionen und zeigt anhand klarer Beispiele, wie Python effizient eingesetzt wird.Aufbauend darauf behandelt das Buch strukturierte Daten wie Listen, Tupel, Dictionaries und Sets sowie Kontrollstrukturen mit Verzweigungen und Schleifen. Im weiteren Verlauf werden Funktionen, Fehlerbehandlung und objektorientierte Programmierung ausführlich erklärt. Ein eigenes Kapitel widmet sich dem Arbeiten mit Dateien, inklusive Lesen, Schreiben, CSV-Verarbeitung und grundlegenden Funktionen des Betriebssystems.Abgerundet wird das Werk durch einen großen Übungsteil mit praxisnahen Aufgaben und vollständigen Lösungen, die das eigenständige Lernen unterstützen und den Stoff festigen. Damit eignet sich das Buch sowohl für Einsteigerinnen und Einsteiger in Informatik und Data Science als auch für Anwenderinnen und Anwender, die ein solides Fundament für weiterführende Python-Anwendungen aufbauen möchten.

Einfacher Einstieg für Anfänger und Quereinsteiger - mit Aufgaben und Lösungsbeispielen

Grundlagen der Python-Programmierung

As AI rapidly evolves from passive models to autonomous systems capable of setting goals, reasoning, and acting independently, engineers find themselves at the threshold of a new technological era. This book serves as a bridge—connecting the world of traditional engineering to the emerging domain of Agentic AI. It is crafted for hands-on professionals who may not have formal training in AI but are eager to build the next generation of intelligent, goal-driven systems.
The journey begins with foundational concepts: what it truly means for a system to exhibit agency, how autonomy differs from automation, and why this distinction matters in practice. Early chapters lay down the necessary groundwork in machine learning and generative AI, allowing readers to appreciate the architecture that enables agentic behavior. From there, the book dives into system design patterns, prompting strategies, and the most influential tools shaping the agentic AI landscape—from LangChain to CrewAI. Practical guidance is provided on engineering agents that are not only capable but also aligned, safe, and robust in dynamic environments. The third chapter shifts into applied engineering: readers are walked step-by-step through building their first AI agent, supported by real-world examples, feedback loop design, and deployment practices that mirror how modern autonomous systems are built.
By the final chapter, readers will not only understand agentic systems—they will be ready to build, evaluate, and evolve them. The book closes by addressing the road ahead: open challenges in ethics, unpredictability, and system alignment, along with a roadmap for engineers who want to actively contribute to the field. Whether you're building automation today or preparing for the autonomy of tomorrow, Agentic AI for Engineers equips you with the knowledge, tools, and mindset to lead in the era of intelligent agents. 
What You Will Learn

<li>A practical introduction to Machine Learning and Generative AI, tailored for engineers

</li><li>Conceptualize, design, and build autonomous AI agents from scratch—even with a minimal AI background.

</li><li>The core principles of Agentic AI, including goals, environments, actions, and feedback loops

</li><li>Understand different Agentic AI frameworks and their applications.

</li><li>Integrate agentic systems into real-world applications using hands-on coding examples

</li><li>Review strategies for ensuring safe, ethical, and auditable agent behavior in production environments




Who This Book Is For
Primary audience includes Software Engineers, DevOps, and Data Engineers curious about building intelligent, autonomous systems but who lack formal AI training; Technical Product Managers and Engineering Leaders looking to understand and implement Agentic AI in real-world systems; System Architects and Automation Engineers exploring the shift from traditional automation to intelligent agent-based architectures. 
AI/ML Enthusiasts and self-learners, Engineering and computer science students, and professionals in emerging tech domains who want to build or deploy autonomous agents will also benefit from this book.
 


</li>

As AI rapidly evolves from passive models to autonomous systems capable of setting goals, reasoning, and acting independently, engineers find themselves at the threshold of a new technological era. This book serves as a bridge—connecting the world of traditional engineering to the emerging domain of Agentic AI. It is crafted for hands-on professionals who may not have formal training in AI but are eager to build the next generation of intelligent, goal-driven systems.The journey begins with foundational concepts: what it truly means for a system to exhibit agency, how autonomy differs from automation, and why this distinction matters in practice. Early chapters lay down the necessary groundwork in machine learning and generative AI, allowing readers to appreciate the architecture that enables agentic behavior. From there, the book dives into system design patterns, prompting strategies, and the most influential tools shaping the agentic AI landscape—from LangChain to CrewAI. Practical guidance is provided on engineering agents that are not only capable but also aligned, safe, and robust in dynamic environments. The third chapter shifts into applied engineering: readers are walked step-by-step through building their first AI agent, supported by real-world examples, feedback loop design, and deployment practices that mirror how modern autonomous systems are built.
 By the final chapter, readers will not only understand agentic systems—they will be ready to build, evaluate, and evolve them. The book closes by addressing the road ahead: open challenges in ethics, unpredictability, and system alignment, along with a roadmap for engineers who want to actively contribute to the field. Whether you're building automation today or preparing for the autonomy of tomorrow, 
 Agentic AI for Engineers
 equips you with the knowledge, tools, and mindset to lead in the era of intelligent agents. &nbsp;
 What You Will Learn<ul><li>A practical introduction to Machine Learning and Generative AI, tailored for engineers</li><li>Conceptualize, design, and build autonomous AI agents from scratch—even with a minimal AI background.</li><li>The core principles of Agentic AI, including goals, environments, actions, and feedback loops</li><li>Understand different Agentic AI frameworks and their applications.</li><li>Integrate agentic systems into real-world applications using hands-on coding examples</li><li>Review strategies for ensuring safe, ethical, and auditable agent behavior in production environments</li></ul>Who This Book Is ForPrimary audience includes Software Engineers, DevOps, and Data Engineers curious about building intelligent, autonomous systems but who lack formal AI training; Technical Product Managers and Engineering Leaders looking to understand and implement Agentic AI in real-world systems; System Architects and Automation Engineers exploring the shift from traditional automation to intelligent agent-based architectures. AI/ML Enthusiasts and self-learners, Engineering and computer science students, and professionals in emerging tech domains who want to build or deploy autonomous agents will also benefit from this book.&nbsp;&nbsp;

Architecting Goal-Driven Systems

Agentic AI for Engineers

Steigen Sie ein in C#
 Angefangen bei der Installation der Entwicklungsumgebung lernen Sie alle Grundlagen der C#-Programmierung, einschließlich der objektorientierten Programmierung, und entwickeln gleich Ihr erstes Programm.
 C#-Programmierung von A bis Z
 Sie lernen die GUI-Entwicklung mit Windows Forms und Windows Presentation Foundation und erstellen Datenbankanwendungen.
 Übung macht den Meister
 Ausführliche Schritt-für-Schritt-Anleitungen, anschauliche Beispiele und viele Übungsaufgaben sichern Ihren Lernerfolg. Das Buch ist ideal zum Selbststudium geeignet.

Der leichte Einstieg in C# und die .NET-Programmierung
 
 Programmieren in C# kann ganz einfach sein – das beweist dieses einsteigergerechte Buch. Schritt für Schritt lernen Sie an anschaulichen Beispielen, wie Sie C#-Projekte in Visual Studio 2026 erstellen – 
 schnelle erste Programmiererfolge sind garantiert
 ! Alle wichtigen Themen werden erläutert, einschließlich 
 objektorientierte Programmierung
 und 
 GUI-Entwicklung mit Windows Forms und Windows Presentation Foundation
 . Ausführliche Schritt-für-Schritt-Anleitungen, regelmäßige Zusammenfassungen und viele Übungsaufgaben sichern Ihren Lernerfolg. 
 Ideal für Einsteiger zum Selbststudium
 !
 <ul><li>Alle Grundlagen zu C# 14 mit GUIs, Objektorientierung und Datenbankanwendungen</li><li>Programmieren unter .NET mit Visual Studio</li><li>Inkl. Übungen und Musterlösungen und komplettem Beispielcode</li></ul>Aus dem Inhalt:<ul><li>C#-Sprachgrundlagen</li><li>Einführung in die Windows-Programmierung</li><li>Objektorientierte Programmierung</li><li>Wichtige Klassen</li><li>Fehlerbehandlung</li><li>Datenbank-Anwendungen</li><li>Zeichnen mit GDI+</li><li>Einführung in Windows Presentation Foundation</li><li>Verteilung von Programmen</li></ul>

Ideal für Programmiereinsteiger

Einstieg in C# mit Visual Studio 2026

This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs)—from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.
Starting from the essentials, you’ll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. You’ll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU level—an essential skill for scaling real-world LLMs. You’ll also gain mastery over the phases of training that define today’s leading models:
<ul>
<li>Pretraining - Building general linguistic and semantic understanding.

</li><li>Midtraining - Expanding domain-specific capabilities and adaptability.

</li><li>Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.

</li><li>Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.



The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.
By the end of this book, you’ll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.
What You’ll Learn
<ul>
<li>How to configure and optimize your development environment using PyTorch

</li><li>The mechanics of tokenization, embeddings, normalization, and attention mechanisms.

</li><li>How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.

</li><li>How to integrate custom CUDA kernels to accelerate transformer computations.

</li><li>The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.

</li><li>Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.

</li><li>How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks.





Who this book is for:
Software developers, data scientists, machine learning engineers and AI enthusiasts looking to build their models from scratch.
</li></ul></li></ul>

Design, Train, and Deploy LLMs with PyTorch

Building Large Language Models from Scratch

Souveräne KI für Unternehmen
 
 Sie wollen generative KI gewinnbringend und sicher in die Unternehmens-IT integrieren? Dann sind Sie hier richtig. Dieses Buch zeigt Ihnen, wie Sie 
 eine komplette KI-Infrastruktur mit Open-Source-Tools aufbauen
 und dabei die volle Kontrolle über Ihre Daten behalten. Ingmar Stapel führt Sie durch einen 
 kuratierten Stack für digitale Souveränität
 : von Ollama als KI-Motor über Open WebUI als professionelles Cockpit bis hin zu RAGFlow für komplexes Firmenwissen. Mit detaillierter TCO-Analyse, Schritt-für-Schritt-Anleitungen und Integration über MCP bauen Sie Ihre eigene KI-Infrastruktur auf – 
 lokal, kontrolliert und EU AI Act-konform
 .
 <ul><li>Komplette KI-Plattform mit Ollama, Open WebUI, RAGFlow und n8n aufbauen</li><li>RAG und Fine-Tuning intelligent kombinieren für maximale Effizienz</li><li>Inklusive Kreativ-Werkstatt für Bild- und Videogenerierung mit ComfyUI</li></ul>Aus dem Inhalt:<ul><li>Hardware-Auswahl und TCO-Analyse</li><li>Basisinfrastruktur mit Docker</li><li>KI-Modelle und Lizenzen</li><li>Ollama als KI-Engine</li><li>n8n und Flowise AI</li><li>Fine-Tuning mit LLaMA Factory</li><li>ComfyUI für Bildgenerierung</li><li>EU AI Act und Compliance</li><li>Open WebUI als Cockpit</li><li>Prompt Engineering</li><li>RAG und RagFlow für Unternehmens-Know-How</li><li>Model Context Protocol (MCP)</li></ul>

Strategie und Aufbau der eigenen Open-Source-Infrastruktur

Souveräne KI

In a world flooded with digital information, the ability to automatically extract meaningful and actionable insights from global news feeds is a critical skill. Extracting Actionable Information from RSS Feeds Using Python and AI offers a hands-on guide for leveraging Python and OpenAI to transform raw RSS content—both in English and non-English languages—into structured, insightful data.

This book walks readers through building intelligent pipelines that go beyond simple feed parsing. Using advanced natural language processing and AI techniques, readers will learn how to extract vital elements from each news article, including:

Author identification

Detailed, AI-generated summaries

Assessment of global, political, and social relevance

Detection of potential threats or risks

Named entity recognition (people, places, organizations)




Whether you're building real-time threat intelligence systems, media monitoring dashboards, or conducting geopolitical analysis, this book equips you with the tools and source code to accelerate your development. Each chapter includes fully functional Python scripts that can be immediately applied or extended to meet specific needs.

Designed for developers, analysts, and technologists, this practical and forward-looking book bridges the gap between unstructured content and actionable intelligence—at the speed of the global news cycle.

What You’ll Learn:

Understand how to collect and process RSS feed data from both English and non-English sources using Python.

Apply OpenAI-powered natural language processing to extract key elements such as author, summary, relevance, and threat indicators from news articles.

Perform named entity recognition (NER) to identify and extract people, places, and organizations mentioned in each article.

Evaluate the geopolitical, social, and political relevance of news stories using AI-driven content analysis techniques.

Utilize and customize the provided Python source code to build or enhance real-time content extraction and analysis tools.




Who This Book Is for:

Primary Target Readers include:

Data Analysts and Intelligence Professionals: Professionals in government, cybersecurity, media monitoring, or corporate intelligence who need to extract and act on relevant news information in real time.

Python Developers and AI Enthusiasts: Intermediate to advanced Python programmers looking to integrate AI for real-world content analysis, especially those interested in OpenAI and natural language processing.

Journalists and Media Researchers: Those seeking to automate content curation, perform author attribution, or assess bias and relevance across diverse news sources globally.

Academics and Students in Data Science, AI, or Digital Humanities: Educators and learners looking for applied projects in NLP, multilingual processing, and AI-driven analysis.

Tech-Savvy Policy Makers and Think Tank Researchers: Readers who monitor emerging global narratives and want automated tools to help assess political, social, and security implications.

In a world flooded with digital information, the ability to automatically extract meaningful and actionable insights from global news feeds is a critical skill. Extracting Actionable Information from RSS Feeds Using Python and AI offers a hands-on guide for leveraging Python and OpenAI to transform raw RSS content—both in English and non-English languages—into structured, insightful data.This book walks readers through building intelligent pipelines that go beyond simple feed parsing. Using advanced natural language processing and AI techniques, readers will learn how to extract vital elements from each news article, including:<ul><li>Author identification</li><li>Detailed, AI-generated summaries</li><li>Assessment of global, political, and social relevance</li><li>Detection of potential threats or risks</li><li>Named entity recognition (people, places, organizations)</li></ul>Whether you're building real-time threat intelligence systems, media monitoring dashboards, or conducting geopolitical analysis, this book equips you with the tools and source code to accelerate your development. Each chapter includes fully functional Python scripts that can be immediately applied or extended to meet specific needs.Designed for developers, analysts, and technologists, this practical and forward-looking book bridges the gap between unstructured content and actionable intelligence—at the speed of the global news cycle.What You’ll Learn:<ul><li>Understand how to collect and process RSS feed data from both English and non-English sources using Python.</li><li>Apply OpenAI-powered natural language processing to extract key elements such as author, summary, relevance, and threat indicators from news articles.</li><li>Perform named entity recognition (NER) to identify and extract people, places, and organizations mentioned in each article.</li><li>Evaluate the geopolitical, social, and political relevance of news stories using AI-driven content analysis techniques.</li><li>Utilize and customize the provided Python source code to build or enhance real-time content extraction and analysis tools.</li></ul>Who This Book Is for:Primary Target Readers include:<ul><li>Data Analysts and Intelligence Professionals: Professionals in government, cybersecurity, media monitoring, or corporate intelligence who need to extract and act on relevant news information in real time.</li><li>Python Developers and AI Enthusiasts: Intermediate to advanced Python programmers looking to integrate AI for real-world content analysis, especially those interested in OpenAI and natural language processing.</li><li>Journalists and Media Researchers: Those seeking to automate content curation, perform author attribution, or assess bias and relevance across diverse news sources globally.</li><li>Academics and Students in Data Science, AI, or Digital Humanities: Educators and learners looking for applied projects in NLP, multilingual processing, and AI-driven analysis.</li><li>Tech-Savvy Policy Makers and Think Tank Researchers: Readers who monitor emerging global narratives and want automated tools to help assess political, social, and security implications.</li></ul>

From Global Headlines to Actionable Intelligence

Extracting Intelligence from RSS News Feeds Using Python and AI

The second edition of Advanced Data Science and Analytics with Python reflects the rapid transformation of artificial intelligence in recent years. While preserving its practical, modular structure, this edition significantly expands coverage of the techniques shaping modern AI practice.The deep learning chapter has been substantially broadened to include reinforcement learning and generative adversarial networks, alongside a fully developed exploration of transformer architectures. Generative AI now takes centre stage, with dedicated coverage of self-attention, BERT, GPT, large language model evaluation and API-based interaction. Emerging agentic systems are introduced as part of the evolving AI landscape. Natural language processing has been enhanced with word embeddings, contextual representations and vector search, while network analysis now includes graph representation learning and embedding techniques. The chapter on data product deployment has been strengthened with modern Core ML workflows and new coverage of on-device Foundation Models, bridging experimentation and production.Fully updated for the contemporary Python ecosystem, this edition equips practitioners with the tools and architectural understanding required to design, build and deploy intelligent systems in today's AI-driven world.Jesús Rogel-Salazar is a lead data scientist, founder of RogueLoop, working for companies such as Ortus Technologies, Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Imperial College Business School, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Advanced Data Science and Analytics with Python

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