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Hyndman R: Forecasting

Forecasting

Principles and Practice, the Pythonic Way

Python has become one of the most versatile and widely used programming languages, known for its simplicity and efficiency. Python Made Easy is designed to help beginner developers easily learn the fundamentals and intermediate concepts of Python. This book provides a structured approach to learning Python, covering essential topics from basic syntax to complex data structures and numerical computing.
The book starts with an introduction to Python, explaining its advantages, drawbacks, and the importance of style guides. It then delves into key programming concepts, including variables, lists, strings, dictionaries, tuples, and rare data structures, helping readers build a strong foundation. Readers will also explore loops, conditional statements, Boolean logic, essential mathematical operations, and NumPy's use for numerical computations. Complex topics such as object-oriented programming, inheritance, file handling, unit testing, and data visualization using Matplotlib are covered in depth. Additionally, the book introduces practical applications in numerical mathematics, including Monte Carlo integration and differential equations.&nbsp;
By the end, readers will have an understanding of Python’s core principles and practical applications, empowering them to write efficient, scalable, and well-structured code. This book is particularly helpful if you already know some other programming language and you want to kick start your Python knowledge.
What you will learn:
<ul>
<li>Understand Python fundamentals, including variables, loops, and data structures.</li>
<li>What is object-oriented programming, inheritance, and file handling.</li>
<li>Explore numerical computing with NumPy and data visualization using Matplotlib.</li>
<li>Gain hands-on experience with unit testing, Monte Carlo integration, and real-world applications.</li>
</ul>

A Beginner’s Guide to Coding, Data Structures, and Practical Applications

Python Made Easy

- Entwickeln Sie Empfehlungs- und Entscheidungssysteme Schritt für Schritt.
- Lernen Sie die Stärken und Grenzen wichtiger Algorithmen kennen und worauf es in der Praxis ankommt.
- Finden Sie den passenden Algorithmus und erhalten Sie Tipps zur Parametrierung anhand ausgewählter Small-scale-Beispiele.
- Ein Buch für Praktiker und Bachelor-Studierende im Bereich Computer Science und AI.
- Ihr exklusiver Vorteil: E-Book inklusive beim Kauf des gedruckten Buches

Entdecken Sie in diesem Buch, wie Sie maschinelles Lernen unter Anwendung von supervised /unsupervised- Learning-Techniken für die Entwicklung von Empfehlungs- und Entscheidungssystemen praktisch einsetzen - klar erklärt, anwendungsorientiert und direkt auf eigene Projekte übertragbar. Eine Sammlung anschaulicher Small-scale-Beispiele hilft dabei, schnell die passenden Methoden für Ihre Anforderungen zu finden. Ob Sie etwa ein Fraud- Erkennungssystem entwickeln möchten oder Nach einem passenden Algorithmus mit Hinweisen zur Parameterwahl suchen - dieses Buch bietet konkrete Antworten.
Ein weiterer Schwerpunkt liegt auf dem Design und der Umsetzung von Empfehlungssystemen. Dabei werden zunächst die Grundlagen erläutert. Am Beispiel von Filmempfehlungssystemen erfahren Sie dann Schritt für Schritt, wie gängige Machine-Learning-Algorithmen prinzipiell arbeiten und wie diese umgesetzt werden.
Jedes Kapitel verbindet theoretische Grundlagen mit nachvollziehbaren Pilotimplementierungen und gut dokumentierten Codebeispielen in Python unter Benutzung der scikit-learn-Bibliotheken. Zum Abschluss erhalten Sie einen Leitfaden, wie Sie ein supervised/unsupervised-Projekt in einzelne Schritte zerlegen, damit eine gute Prozessintegration möglich wird.

AUS DEM INHALT //
- Arten von maschinellem Lernen
- Vorverarbeitungsschritte für maschinelles Lernen
- Fallbeispiel 1: Vorhersage Verkauf/kein Verkauf eines Hauses (Logistische-Regression-Algorithmus)
- Fallbeispiel 2: Betrugserkennung bei Transaktionen unter Einsatz eines Klassifizierers
- Fallbeispiel 3: Rotweinqualität-Prognose (SVM, MLP)
- Fallbeispiel 4: Rotweinqualität-Mehrklassenprognose
- Empfehlungssysteme inkl. Fallbeispiele (KNN, K-Means)
- Leitfaden für die Umsetzung von Supervised-Learning-Projekten
- Möglichkeiten für den Einsatz von generativen Modellen am Beispiel einer Klassifikation

- Entwickeln Sie Empfehlungs- und Entscheidungssysteme Schritt für Schritt.
- Lernen Sie die Stärken und Grenzen wichtiger Algorithmen kennen und worauf es in der Praxis ankommt.
- Finden Sie den passenden Algorithmus und erhalten Sie Tipps zur Parametrierung anhand ausgewählter Small-scale-Beispiele.
- Ein Buch für Praktiker und Bachelor-Studierende im Bereich Computer Science und AI.
- Ihr exklusiver Vorteil: E-Book inklusive beim Kauf des gedruckten Buches

Entdecken Sie in diesem Buch, wie Sie maschinelles Lernen unter Anwendung von supervised /unsupervised- Learning-Techniken für die Entwicklung von Empfehlungs- und Entscheidungssystemen praktisch einsetzen – klar erklärt, anwendungsorientiert und direkt auf eigene Projekte übertragbar. Eine Sammlung anschaulicher Small-scale-Beispiele hilft dabei, schnell die passenden Methoden für Ihre Anforderungen zu finden. Ob Sie etwa ein Fraud- Erkennungssystem entwickeln möchten oder Nach einem passenden Algorithmus mit Hinweisen zur Parameterwahl suchen – dieses Buch bietet konkrete Antworten.
Ein weiterer Schwerpunkt liegt auf dem Design und der Umsetzung von Empfehlungssystemen. Dabei werden zunächst die Grundlagen erläutert. Am Beispiel von Filmempfehlungssystemen erfahren Sie dann Schritt für Schritt, wie gängige Machine-Learning-Algorithmen prinzipiell arbeiten und wie diese umgesetzt werden.
Jedes Kapitel verbindet theoretische Grundlagen mit nachvollziehbaren Pilotimplementierungen und gut dokumentierten Codebeispielen in Python unter Benutzung der scikit-learn-Bibliotheken. Zum Abschluss erhalten Sie einen Leitfaden, wie Sie ein supervised/unsupervised-Projekt in einzelne Schritte zerlegen, damit eine gute Prozessintegration möglich wird.

AUS DEM INHALT //
- Arten von maschinellem Lernen
- Vorverarbeitungsschritte für maschinelles Lernen
- Fallbeispiel 1: Vorhersage Verkauf/kein Verkauf eines Hauses (Logistische-Regression-Algorithmus)
- Fallbeispiel 2: Betrugserkennung bei Transaktionen unter Einsatz eines Klassifizierers
- Fallbeispiel 3: Rotweinqualität-Prognose (SVM, MLP)
- Fallbeispiel 4: Rotweinqualität-Mehrklassenprognose
- Empfehlungssysteme inkl. Fallbeispiele (KNN, K-Means)
- Leitfaden für die Umsetzung von Supervised-Learning-Projekten
- Möglichkeiten für den Einsatz von generativen Modellen am Beispiel einer Klassifikation

Eine Beispielsammlung für Empfehlungs- und Entscheidungssysteme

Maschinelles Lernen

Mastering Sector and Industry Analysis

The Art of Financial Terrain

Python: In 21 Tagen die gefragte Programmiersprache lernen, spannende Projekte realisieren und neue berufliche Perspektiven eröffnen
 Sie sind fasziniert von Codes, Programmieren und effizienter Arbeitsorganisation? Und wollen endlich selbst in die Welt der Programmiersprachen einsteigen? Und das Ganze soll dazu anfängertauglich, step-by-step und anwendungsorientiert sein? Bittesehr - hier kommt Ihr persönlicher 21-Tage-Fahrplan zu fundierten Python-Fähigkeiten für Alltag, Studium oder Büro!Wer Python-Code beherrscht und gezielt für seine Zwecke einsetzen kann, ist in der modernen Arbeitswelt absolut im Vorteil: Es gibt kaum einen Job, in dem Sie mit soliden Programmierkenntnissen Prozessabläufe nicht optimieren und effizienter gestalten können, und dazu eröffnet Ihnen die Fähigkeit völlig neue berufliche Perspektiven. Doch auch das Alltagsmanagement kommt ohne digitale Unterstützung kaum mehr aus - und wie Sie sich die genau auf Ihre Bedürfnisse zugeschnitten selbst programmieren, zeigt Ihnen dieses Buch. Es führt Sie zunächst verständlich in die Grundlagen ein, zeigt Ihnen, wie Code überhaupt funktioniert, und erweitert dann Schritt für Schritt Ihre Kenntnisse sowie praktischen Anwendungsfähigkeiten. Sie lernen grundlegende Datenstrukturen ebenso kennen wie objektorientierte Programmierung und beherrschen schließlich auch fortgeschrittene Konzepte wie Dekoratoren oder List Comprehensions. Klingt (noch) alles wie Chinesisch für Sie? Keine Sorge! Dieser Praxisratgeber richtet sich gezielt an Laien ohne Vorkenntnisse und bringt Ihnen die faszinierende Welt des Codes einfach erklärt und in genau Ihrem Tempo nahe.Tag 1-9: Einführung und Grundlagen:
 Lernen Sie die Python-Entwicklungsumgebung (IDE) kennen, machen Sie sich mit den ersten Programmierungsgrundlagen vertraut und entdecken Sie grundlegende Datenstrukturen wie Listen, Tupel oder Sets.
 Tag 10-15: OOP, Dateioperationen &amp; Module:
 Steigen Sie in die objektorientierte Programmierung mit Vererbung, Polymorphismus und Kapselung ein und erlernen Sie den Umgang mit verschiedenen Dateiformaten und Modulen.
 Tag 16-18: Fortgeschrittene Konzepte:
 Generatoren und Iteratoren, Fehlerbehandlung und Ausnahmen oder Lambda-Ausdrücke - mit fortgeschrittenen Profi-Anwendungen holen Sie das Maximum aus Python heraus.
 Tag 19-21: Spannende Praxisprojekte:
 Setzen Sie nützliche Projekte wie IBAN-Prüfer, Datei-Organizer, Zufalls-Entscheidungshelfer oder Mini-KI für Chatantworten mit klaren, präzisen Anleitungen ganz einfach in die Tat um.
 Dieses Buch ist Ihr perfekter Einstieg in das faszinierende und zukunftsweisende Universum des Programmierens und macht Sie in kürzester Zeit zum Python-Profi. Ob Alltag erleichtern, Büroaufgaben vereinfachen oder Arbeitsprozesse optimieren - hier finden Sie das Werkzeug für jedes Projekt.Also, worauf warten Sie noch?
 Klicken Sie nun auf "Jetzt kaufen mit 1-Click" und investieren Sie Ihre Zeit in eine Fähigkeit, die Sie in Beruf &amp; Karriere wirklich weiterbringt!

Das Python Buch für Einsteiger: In 21 Tagen Schritt für Schritt Python programmieren lernen ohne Vorwissen - inkl. 14 Praxisprojekte, IDE-Setup, 50 Standardbefehle uvm

The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mastering the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.
Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.
The book doesn't stop at training. It tackles the crucial "last mile" of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.
Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today.
 
What you will learn:
<ul>
<li>Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework 

</li><li>Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation 

</li><li>Build and optimize RAG pipelines with effective retrieval strategies and vector databases 

</li><li>Deploy optimized LLMs using quantization techniques and scalable inference frameworks 

</li><li>Develop multimodal and agentic AI applications with vision-language models and autonomous agents 



 
Who this book is for:

This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and start
</li></ul>

The Large Language Model Recipes book 
 is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mastering the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.
 Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.The book doesn't stop at training. It tackles the crucial "last mile" of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today.&nbsp;What you will learn:<ul><li>Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework </li><li>Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation </li><li>Build and optimize RAG pipelines with effective retrieval strategies and vector databases </li><li>Deploy optimized LLMs using quantization techniques and scalable inference frameworks </li><li>Develop multimodal and agentic AI applications with vision-language models and autonomous agents </li></ul>&nbsp;Who this book is for:This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and start

A Hands-On Guide to Fine-Tuning, Optimization, Deployment, and Real-World Applications

Large Language Model Recipes

Django Admin stands as a powerful tool for rapidly developing your business back office interfaces, offering an impressive array of built-in features that can handle many common administrative tasks out of the box. Its strength lies in its ability to quickly generate functional interfaces for data management, user administration, and &nbsp;business operations with minimal code. However developers find themselves investing significant effort to bend Django Admin to their needs, when more complex operations and tasks are required. &nbsp;The Django Admin Handbook is a comprehensive guide, readers will gain deep insights into Django Admin's capabilities, limitations, and customization potential, enabling you to decide whether it's the right tool for your specific project requirements.&nbsp;Organized into five sections, this handbook takes you on a carefully structured journey through Django Admin mastery. The Foundations section establishes core concepts and basic configurations, ensuring a solid understanding of the framework's architecture. The Essential Features section dives into practical implementations of filtering, actions, forms, and performance optimization techniques that form the backbone of any administrative interface. Advanced Customization explores sophisticated features like extending list displays, creating custom views, integrating REST APIs, and implementing background tasks with Celery. The UI/UX Enhancement section demonstrates how to transform the default interface into a modern, responsive application using popular frameworks like Bootstrap and Tailwind CSS, while incorporating dynamic dashboards and data visualizations. Finally, the Security &amp; Access Control section ensures your admin interface is production-ready with robust authentication, permission systems, and audit logging capabilities. Each chapter builds upon the previous, providing both theoretical knowledge and practical implementations you can quickly apply to your projects.By the end, you will have a full overview of Django Admin capabilities. Most importantly, you'll develop the expertise to evaluate when Django Admin is the right tool for your project and how to leverage its features to their full potential. This knowledge will dramatically reduce your development time, allowing you to deliver professional-grade administrative interfaces that perfectly align with your organization's requirements.You Will:<ul><li>Master Django Admin customization techniques to build professional backoffice interfaces in hours instead of days</li><li>Implement advanced features including custom views, REST API integration, and sophisticated query optimization</li><li>Transform the default interface into a modern, responsive application using contemporary CSS frameworks</li><li>Build powerful batch processing systems with custom admin actions and Celery integration for handling resource-intensive operations and long-running tasks</li><li>Design and implement enterprise-grade security systems with role-based access control</li></ul>

Django Admin stands as a powerful tool for rapidly developing your business back office interfaces, offering an impressive array of built-in features that can handle many common administrative tasks out of the box. Its strength lies in its ability to quickly generate functional interfaces for data management, user administration, and&nbsp; business operations with minimal code. However developers find themselves investing significant effort to bend Django Admin to their needs, when more complex operations and tasks are required.&nbsp; 
 The Django Admin Handbook
 is a comprehensive guide, readers will gain deep insights into Django Admin's capabilities, limitations, and customization potential, enabling you to decide whether it's the right tool for your specific project requirements.&nbsp;
 Organized into five sections, this handbook takes you on a carefully structured journey through Django Admin mastery. The Foundations section establishes core concepts and basic configurations, ensuring a solid understanding of the framework's architecture. The Essential Features section dives into practical implementations of filtering, actions, forms, and performance optimization techniques that form the backbone of any administrative interface. Advanced Customization explores sophisticated features like extending list displays, creating custom views, integrating REST APIs, and implementing background tasks with Celery. The UI/UX Enhancement section demonstrates how to transform the default interface into a modern, responsive application using popular frameworks like Bootstrap and Tailwind CSS, while incorporating dynamic dashboards and data visualizations. Finally, the Security &amp; Access Control section ensures your admin interface is production-ready with robust authentication, permission systems, and audit logging capabilities. Each chapter builds upon the previous, providing both theoretical knowledge and practical implementations you can quickly apply to your projects.By the end, you will have a full overview of Django Admin capabilities. Most importantly, you'll develop the expertise to evaluate when Django Admin is the right tool for your project and how to leverage its features to their full potential. This knowledge will dramatically reduce your development time, allowing you to deliver professional-grade administrative interfaces that perfectly align with your organization's requirements.You Will:<ul><li>Master Django Admin customization techniques to build professional backoffice interfaces in hours instead of days</li><li>Implement advanced features including custom views, REST API integration, and sophisticated query optimization</li><li>Transform the default interface into a modern, responsive application using contemporary CSS frameworks</li><li>Build powerful batch processing systems with custom admin actions and Celery integration for handling resource-intensive operations and long-running tasks</li><li>Design and implement enterprise-grade security systems with role-based access control</li></ul>This Book Is For<ul><li>Django developers&nbsp;</li><li>Backend engineers working on business applications&nbsp;</li><li>Full-stack developers&nbsp;</li><li>Technical leads and architects&nbsp;</li></ul>

The Ultimate Guide to Building Your Back Office in Hours, Not Days

The Django Admin Handbook

This book is a hands-on, technical guide to building and deploying generative AI models using advanced deep learning architectures like transformers, GANs, VAEs, and diffusion models. Designed for AI engineers, data scientists, and ML practitioners, it offers a practical roadmap from data ingestion to real-world deployment and evaluation.

The book starts by guiding readers on selecting the right model architecture for their application, be it text generation, image synthesis, or multimodal tasks. It then walks through essential components of model training, including dataset handling, self-supervised learning, and core optimisation techniques such as backpropagation, gradient descent, and learning rate scheduling. It also delves into large-scale training infrastructure, covering GPU/TPU usage, distributed computing frameworks, and system-level strategies for scaling performance. Practical guidance is provided on fine-tuning models with domain-specific data and applying reinforcement learning from human feedback (RLHF), model quantisation, and pruning to improve efficiency. Key challenges in generative AI—such as overfitting, bias, hallucination, and data efficiency—are addressed through proven techniques and emerging best practices. Readers will also gain insight into model interpretability and generalisation, ensuring robust and trustworthy outputs. The book demonstrates how to build scalable, production-ready generative systems across domains like media, healthcare, scientific simulation, and design through real-world examples and applied case studies.

 

By the end, readers will gain an understanding of how to architect, optimise, and apply generative models across diverse domains such as media creation, healthcare, design, scientific simulation, and beyond.

What you will learn;

Learn how to choose and implement generative models—VAEs, GANs, transformers, and diffusion models—for specific use cases.

Master training optimization techniques such as backpropagation, gradient descent, adaptive learning rates, and regularization.

Apply best practices for large-scale training using GPUs, TPUs, and distributed computing frameworks for performance scaling.

Boost model efficiency through quantization, pruning, fine-tuning, and RLHF to enhance output quality and reduce overhead.




Who this book is for:

AI Engineers and Machine Learning Practitioners looking to build and deploy generative models in real-world applications. Data Scientists working on deep learning projects involving text, vision, audio, or multimodal generation.

This book is a hands-on, technical guide to building and deploying generative AI models using advanced deep learning architectures like transformers, GANs, VAEs, and diffusion models. Designed for AI engineers, data scientists, and ML practitioners, it offers a practical roadmap from data ingestion to real-world deployment and evaluation.The book starts by guiding readers on selecting the right model architecture for their application, be it text generation, image synthesis, or multimodal tasks. It then walks through essential components of model training, including dataset handling, self-supervised learning, and core optimisation techniques such as backpropagation, gradient descent, and learning rate scheduling. It also delves into large-scale training infrastructure, covering GPU/TPU usage, distributed computing frameworks, and system-level strategies for scaling performance. Practical guidance is provided on fine-tuning models with domain-specific data and applying reinforcement learning from human feedback (RLHF), model quantisation, and pruning to improve efficiency. Key challenges in generative AI—such as overfitting, bias, hallucination, and data efficiency—are addressed through proven techniques and emerging best practices. Readers will also gain insight into model interpretability and generalisation, ensuring robust and trustworthy outputs. The book demonstrates how to build scalable, production-ready generative systems across domains like media, healthcare, scientific simulation, and design through real-world examples and applied case studies.&nbsp;By the end, readers will gain an understanding of how to architect, optimise, and apply generative models across diverse domains such as media creation, healthcare, design, scientific simulation, and beyond.What you will learn;<ul><li>Learn how to choose and implement generative models—VAEs, GANs, transformers, and diffusion models—for specific use cases.</li><li>Master training optimization techniques such as backpropagation, gradient descent, adaptive learning rates, and regularization.</li><li>Apply best practices for large-scale training using GPUs, TPUs, and distributed computing frameworks for performance scaling.</li><li>Boost model efficiency through quantization, pruning, fine-tuning, and RLHF to enhance output quality and reduce overhead.</li></ul>Who this book is for:AI Engineers and Machine Learning Practitioners looking to build and deploy generative models in real-world applications. &nbsp;Data Scientists working on deep learning projects involving text, vision, audio, or multimodal generation.

A Practical Guide to Generative AI Development and Scaling

Building and Training Generative AI Models

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

De Gruyter Textbook

This book is a textbook about calculus of one variable. It contains proofs of all the theorems, propositions etc but also Python codes in order to solve many calculus problems. To produce these codes we use AI and show to the reader how to do that.&nbsp;
This book aims to supplement the existing bibliography concerning single-variable calculus. Material including Python code for both symbolic and approximate computations. Our goal is to appropriately use artificial intelligence to generate this code and demonstrate to students how this is approximately done. This book is extremely useful for first-year science students, their professors, and others.

This book is a textbook about calculus of one variable. It contains proofs of all the theorems, propositions etc but also Python codes in order to solve many calculus problems. To produce these codes we use AI and show to the reader how to do that.&nbsp;This book aims to supplement the existing bibliography concerning single-variable calculus. Material including Python code for both symbolic and approximate computations. Our goal is to appropriately use artificial intelligence to generate this code and demonstrate to students how this is approximately done. This book is extremely useful for first-year science students, their professors, and others.

with Python and AI

Calculus of One Variable

Springer Texts in Business and Economics

A Hands-On Approach

Essentials of Financial Economics

The XIAO ESP32-C3 board combines the ultra-compact form factor of the XIAO series with the full capabilities of the ESP32-C3 chip, including integrated Wi-Fi and Bluetooth&nbsp;5.0. This book is a comprehensive, hands-on guide to getting the most out of this powerful yet affordable development board.The author takes readers from the fundamentals to advanced projects. Starting with LED blinking and dimming, the book progresses to ADC and PWM for precise sensor interfacing and PWM-controlled lighting effects before moving on to building practical, real-world applications, including:<ul><li>A Wi-Fi weather station with an OLED display and real-time online weather data</li><li>An ultrasonic parking assistant with audible feedback</li><li>A refrigerator monitoring probe with climate sensing and Deep Sleep for years of battery operation</li><li>A retro Pong game controlled by a touch-free sensor</li><li>A microphone-based party app that responds to claps</li><li>And much more: lighting control, reaction-time measurement, mains frequency analysis, soil moisture sensing, ambient LED lighting, and 7-segment displays</li>
 
 </ul>

Using MicroPython

Build Smart Projects with the XIAO ESP32-C3 Board

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

Forecasting

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