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Hyndman R: Forecasting

Forecasting

Principles and Practice, the Pythonic Way

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>

Architecting Goal-Driven Systems

Agentic AI for Engineers

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

Statistical Methods in the Atmospheric Sciences, Fifth Edition provides a structured exploration of the statistical techniques essential for analyzing atmospheric data. The book begins with foundational concepts in probability, setting the stage for more advanced topics. It then covers univariate statistics, including empirical distributions, parametric probability models, and both frequentist and Bayesian inference methods, offering tools for rigorous data analysis and interpretation. The text also addresses statistical forecasting and ensemble forecasting, along with methods for verifying forecast accuracy. In addition, time series analysis is explored in detail, enabling readers to understand temporal dependencies in atmospheric data. The book advances into multivariate statistics, presenting matrix algebra and random matrices as mathematical foundations. It discusses the multivariate normal distribution, principal component analysis (EOF), and multivariate analysis of vector pairs to handle complex, multidimensional atmospheric datasets. Techniques for discrimination, classification, and cluster analysis are also examined, providing methods for categorizing and interpreting atmospheric patterns. Supplementary materials include example data sets, probability tables, and a glossary of symbols and acronyms, along with answers to exercises that reinforce learning.

Statistical Methods in the Atmospheric Sciences

Gain a thorough grounding in marketing analytics using Python with this practical guide which balances applied analytics tools with marketing theory.

Applied Marketing Analytics Using Python

'Essential reading' Rory Sutherland, author of Alchemy 'Possibly the best-timed book ever' Merryn Somerset Webb, Bloomberg Gold (noun): A precious, yellow metal, prized for its beauty. Inert, immune to corrosion, highly malleable though with little utility. Seen as pure wealth. The definitive biography of the metal that has shaped our world - and may yet determine its fate. The Secret History of Gold tells the epic tale of the world's oldest, and most treasured, currency. From its origins in the formation of the solar system to its pivotal role in both ancient myth and modern finance, Dominic Frisby explores, with wit and brevity, how gold has defined the entire course of human civilisation. Gold has inspired men to do the most brilliant - and terrible - things. The most stolen metal in history, it has lured explorers, conquerors and thieves. It has sparked wars, it has built empires, it has empowered leaders - from Alexander the Great to Adolf Hitler - good and bad. As nations today such as China and Russia mine and hoard gold at record rates, Frisby examines its enduring significance in the digital age and its potential to influence global peace and power. This is the untold story of gold: its past, its present and its future. 'A fabulous, fascinating, fantastical tale' Matt Ridley, author of How Innovation Works 'Well-researched and razor-sharp' Al Murray, comedian and historian

Myth, Money, Politics and Power

The Secret History of Gold

Statistical skills are essential for analysing data and ensuring optimal business operation and efficiency. In this clear and concise textbook, Les Oakshott and Kathy Kotiadis guide students through key quantitative techniques, from basic maths and statistics through to advanced topics such as hypothesis testing and linear programming. Grounded in the everyday realities of business, this textbook introduces each chapter with an engaging case study to highlight how organisations operationalise these techniques, ranging from the growth of China's economy and a Covid-19 infection survey to goal scoring at Euro 2024 and the effect of global warming on the occurrence of salmonella in Singapore. New to this edition: -Links the UN Sustainable Development Goals to quantitative techniques -Greater emphasis on Excel to enable seamless transition to blended learning approaches -Walk-through videos of key processes hosted on the companion website This is the essential textbook for delivering quantitative methods and business statistics modules to students of mixed maths abilities across undergraduate and postgraduate levels. Accompanying online resources for this title can be found at bloomsbury.pub/essential-quantitative-methods-8e. These resources are designed to support teaching and learning at no extra cost.

for Business, Management and Finance

Essential Quantitative Methods

Springer Texts in Business and Economics

A Hands-On Approach

Essentials of Financial Economics

AI can write the code. You still need to know what it does. You’re already good at Excel. But you’re tired of the copy/paste, the helper columns, and the brittle formulas that break when someone adds a row. You want automation, and whether you write the code yourself or let AI generate it, you need to understand what’s actually running your business processes. Automate Excel with Python teaches you to build real workflows, step-by-step. You’ll read messy workbooks into pandas dataframes, filter and reshape data without helper columns, merge sources without silent VLOOKUP failures, and export polished results with formatting intact. A capstone chapter ties it together: import a multi-tab workbook, generate exception reports, and email the results—all from one script you run with a single click. You’ll learn how to: <ul><li>Read existing Excel files you already use into Python, even the messy ones</li><li>Replace daily copy/paste routines with reusable scripts</li><li>Merge and match data across sources with auditable results</li><li>Handle dates, times, and the edge cases that break formulas</li><li>Export from Python to Excel with column widths, number formats, and frozen panes intact</li><li>Build workflows that run daily without babysitting</li></ul> AI can write the code. This book makes sure you’re the one in control of it.

A Practical Guide for Nonprogrammers

Automate Excel with Python

This book equips AI developers, machine learning engineers, and software architects with the knowledge and tools needed to design, build, and deploy scalable autonomous agents. Beginning with foundational concepts in LLMs and agent architectures, the book transitions into hands-on design patterns and real-world implementation guidance. Readers will follow practical Python examples using cutting-edge frameworks like LangChain and AutoGen, with full reproducible code available on GitHub.
Emphasizing engineering essentials scalability, interoperability, observability, and ethics the book dives into multi-agent coordination, including emerging protocols such as MCP and A2A. It wraps up with compelling case studies from customer service bots to multi-agent analytics systems demonstrating how to build secure, stable, and trust-aligned AI agents. This guide bridges theory with practice for the modern AI practitioner.
You Will:
<ul>
<li>Discover reusable patterns such as reactors, planners, and tool-using agents that power effective autonomous systems.</li>
<li>Learn to build agents using Python with popular frameworks like LangChain and AutoGen, complete with reproducible GitHub code.</li>
<li>Dive into cutting-edge coordination mechanisms (e.g., MCP, A2A) that enable agents to collaborate and assign tasks dynamically.</li>
</ul>
&nbsp;
This book is for : Software Architects &amp; System Designers
&nbsp;

Architecting Scalable, Ethical, and Trustworthy Systems for Enterprise

Agentic AI Systems in Practice

This textbook covers essential topics in quantitative finance, including stochastic calculus, portfolio optimization (static and dynamic), and risk-neutral pricing. Combining financial theory with real-world applications, the book presents a step-by-step guide to modelling financial data in continuous time using R and Python. The side-by-side presentation of the two software languages allows readers to grasp the similarities and differences between the two codes, while guiding them through models calibrated with actual market data that illustrate the quantitative characteristics of optimal portfolios.
Reinforced with pedagogical features including accompanying online datasets and numerical exercises to understand stochastic processes, this textbook will be a valuable resource for postgraduate students on corporate finance, quantitative finance, portfolio and investment management, risk management and actuarial courses, as well as finance professionals undertaking quantitative modelling.

Numerical Applications in R and Python

Portfolio Management in Continuous Time

This book delves into contemporary business analytics techniques across sectors for critical decision-making. It combines data, mathematical and statistical models, and information technology to present alternatives for decision evaluation. Offering systematic mechanisms, it explores business contexts, factors, and relationships to foster competitiveness. Beyond managerial perspectives, it includes contributions from professionals, academics, and scholars worldwide, delivering comprehensive knowledge and skills through diverse viewpoints, cases, and applications of analytical tools. As an international business science reference, it targets professionals, academics, researchers, doctoral scholars, postgraduate students, and research organizations seeking a nuanced understanding of modern business analytics.

Data-Driven Decision Making

Anhand der Programmiersprache Python vermittelt dieses Buch die theoretischen und methodischen Grundlagen der Ökonometrie. Python ist bestens für AnfängerInnen geeignet und besitzt leistungsstarke Bibliotheken wie Pandas und Matplotlib, mit denen Datenbestände verwaltet bzw. bearbeitet und in Form aussagefähiger Grafiken dargestellt werden können. Mit der Bibliothek Statsmodels stehen effiziente Methoden für die Analyse und Auswertung von statistischen Daten als auch für die Regressionsanalyse zur Verfügung. LeserInnen benötigen keine fundierten Kenntnisse in der Programmierung, da die Kommandos in der Regel einen parametergesteuerten Zugriff auf die Methoden erlauben. Angesichts der Dominanz von Python in den Bereichen Data Science und KI ist die Wahl dieser Sprache eine zukunftssichere Entscheidung für die ökonometrische Analyse. Ergänzend zum Buch werden alle Python-Skripte und Datensätze auf SpringerLink zum Download bereitgestellt, um die direkte Umsetzung der Verfahren zu ermöglichen.Der Inhalt<ul><li>Die Grundlagen der Programmiersprache Python</li><li>Einführung in Pandas, NumPy und Matplotlib</li><li>Datenverarbeitung mit Pandas DataFrames</li><li>Datenvisualisierung mit Matplotlib</li><li>Deskriptive Statistik und statistische Grundlagen</li><li>Regressionsanalyse von Querschnittsdaten</li><li>Spezifikationstests und Modellvalidierung</li><li>Dynamische Regressionsmodelle für Zeitreihen</li><li>Regressionsanalyse von Paneldaten</li><li>Matrixbearbeitung mit Python</li><li>Kompakte Regressionsanalyse mittels Matrixalgebra</li></ul>Der AutorJürgen Malitte
 war beruflich in der IT- und Methodenberatung von Unternehmen sowie als Organisationsprogrammierer tätig und unterrichtet Mathematik/Statistik und Programmiersprachen in verschiedenen Bildungseinrichtungen.

Eine Einführung mit praktischen softwaregestützten Beispielen

Ökonometrie verstehen und anwenden mit Python

Forecasting

Autres titres sur ce thème