Skills ScholarVox : Nouveautés : Sciences, informatique et techniques http://skills.scholarvox.com <![CDATA[ Structural Steel Design Ed. 1 ]]> http://skills.scholarvox.com/catalog/book/88982325 Editeur :

Essential knowledge of steel-framed structure design is a cornerstone for architectural, civil, and structural engineers, as well as for students planning careers in structural design and construction. Structural Steel Design, Fourth Edition delivers a comprehensive understanding of structural steel design, starting with the fundamentals and progressing to the design of a complete structural system. It emphasizes not just the individual steel elements or components but their integration within the broader context of the entire structure.

By working through the chapters and corresponding design project tasks, readers will complete the design of a full steel structure, allowing them to grasp the connections between discrete components and the larger system. This approach reinforces the importance of seeing the "big picture" in structural design.

Encouraged by the American Institute for Steel Construction, this book goes beyond traditional textbook exercises by offering real-world examples, project-based exercises, and open-ended problems that challenge the reader to make decisions and navigate the iterative nature of structural design. Practical details and real-world end-of-chapter problems reflect the types of challenges encountered in professional engineering practice, making this text not just an academic resource but a practical guide for aspiring engineers.

]]> <![CDATA[ Physique Méthodes et exercices PCSI - PTSI Ed. 5 ]]> http://skills.scholarvox.com/catalog/book/88982255 Editeur :

LES METHODES 

  • Classées par thèmes du programme, 183 méthodes vous sont expliquées par étapes. 
  • Chaque méthode renvoie à plusieurs exercices d'application. 

LES EXERCICES 

  • Les exercices d'application, au nombre de 413, sont triés par difficulté.
  • Ils couvrent l'intégralité du programme de PCSI-PTSI. 
  • Des indications "pour bien démarrer" vous donnent un coup de pouce si vous avez du mal à résoudre un exercice. 
  • Tous les exercices sont corrigés, avec une rédaction complète. 

 

]]> <![CDATA[ Le cours de biochimie Ed. 3 ]]> http://skills.scholarvox.com/catalog/book/88982254 Editeur :

Cet ouvrage fait la synthèse en 200 fiches des concepts fondamentaux de la biochimie structurale et métabolique enseignés dans les premières années d’études supérieures.
La présentation est adaptée aux besoins des étudiants préparant
un examen ou un concours : fiches synthétiques pour comprendre,
QCM pour s’évaluer, sujets de synthèse pour s’entraîner.
Cette troisième édition a été actualisée avec de nouveaux focus,
fiches et QCM.
 

]]> <![CDATA[ Future-Ready Data Foundation with MongoDB : Principles for designing scalable and AI-ready data architectures ]]> http://skills.scholarvox.com/catalog/book/88982171 Editeur :

Learn how MongoDB helps organizations build scalable, secure, and AI-ready data foundations that power RAG, AI agents, and intelligent applications while simplifying performance, governance, and growth.

Key Features

  • Identify the data requirements that enable AI applications to succeed in production
  • Evaluate retrieval strategies for accurate and trustworthy AI experiences
  • Apply architectural principles for scalable, AI-ready data platforms
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

AI success depends on more than models, it requires a data foundation built for scale, intelligence, and trust. In Future-Ready Data Foundation with MongoDB, you’ll discover the principles behind building AI-ready data architectures that support modern applications, retrieval-augmented generation (RAG), and AI agents. This concise guide explores the critical role of data in AI modernization and shows how MongoDB helps organizations create a unified foundation for innovation. Through practical architectural insights, you’ll learn how retrieval strategies influence the quality and reliability of AI outcomes and how MongoDB’s document model and vector search capabilities support intelligent data access. You’ll also explore the scalability, performance, and operational patterns required to keep AI systems running efficiently, including replication, sharding, workload isolation, and search optimization. Finally, you’ll examine the governance, observability, security, and compliance considerations that help organizations deploy AI responsibly and at scale. By the end of this book, you’ll be equipped to evaluate, design, and optimize data foundations that support the next generation of AI-powered applications.

What you will learn

  • Identify the data foundations required for successful AI initiatives
  • Compare lexical, vector, and hybrid retrieval strategies
  • Understand how RAG and AI agents shape modern AI architectures
  • Use MongoDB's document model to store and manage data for AI applications
  • Explore native vector search for semantic retrieval and AI-powered search experiences
  • Assess scaling patterns using replication, sharding, and dedicated Search Nodes
  • Apply governance, security, and compliance practices for AI systems

Who this book is for

This book is for technology leaders, data architects, data engineers, platform engineers, and AI practitioners who want to build strong foundations for modern AI initiatives. It is also valuable for decision-makers evaluating how data architecture affects the scalability, performance, security, and governance of AI systems. By reading this book, you’ll gain the architectural knowledge needed to assess, design, and support AI-ready data platforms that power RAG applications, AI agents, and other intelligent solutions.

]]> <![CDATA[ Claude Code in Action : Build a Real-World AI App From Start to Finish ]]> http://skills.scholarvox.com/catalog/book/88982164 Editeur :

Build a working AI application with Claude Code from initial setup through debugging and refinement. Practice client-server planning, feature generation, export fixes, provider integration, and interface improvement.

Key Features

  • End-to-end project work across setup, architecture, generation, debugging, and interface polish
  • Real troubleshooting explains why generated applications need testing, review, and iteration
  • Hands-on provider integration shows how local and hosted AI options fit one application well

Book Description

Hands-on agentic development becomes clearer when every concept supports a working application. The opening sections prepare Node.js, Claude Code, required accounts, and project files, then introduce the shift from a coding assistant to an active development partner. Readers define the application, organize its core components, and translate frontend, backend, export, and error-handling needs into focused requests. The build then moves from generation to evidence-based troubleshooting. Readers install dependencies, run the application, diagnose the first failures, repair client-server communication, and investigate HTML and JSON exports. Additional work adds a presentation mode and compares hosted and local model providers through OpenAI and Ollama, including configuration and timeout problems that reveal how environment details affect behavior. By the end of this guide, readers can take an AI-assisted application from idea to a tested local result. They will be prepared to prompt with clearer intent, inspect generated code, resolve integration faults, and improve interface details through deliberate iteration.

What you will learn

  • Install Node.js and Claude Code for local development
  • Plan a maintainable client-server application
  • Generate frontend and backend components from clear prompts
  • Debug dependencies, exports, and client-server issues
  • Integrate OpenAI and Ollama as model providers
  • Refine interface behavior through a guided practice project

Who this book is for

Developers and technical learners who want project-based experience with Claude Code and AI application development. Basic programming and command-line familiarity are useful; the guided setup covers Node.js, application structure, dependencies, testing, and debugging.

]]> <![CDATA[ The Generative AI Career Masterplan : Navigate the future of AI with practical insights from industry pioneers at AI-first organizations ]]> http://skills.scholarvox.com/catalog/book/88982161 Editeur :

Kick-start your agentic AI career with insights from leaders at Google, Microsoft, and Liquid AI. Learn the skills, strategies, and tools to build intelligent agents, transition faster, and lead in the era of autonomous systems.

Key Features

  • Discover the only comprehensive career roadmap for the Generative AI era
  • Learn directly from top industry experts, leaders, and AI innovators
  • Build future-proof skills, from prompt engineering to ethical AI strategy
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

This book is a landmark publication, the first-ever collaborative guide authored by a powerhouse lineup of industry leaders from Google, Microsoft, IBM and Liquid AI. This all-in-one resource demystifies the Agentic AI revolution and delivers a practical, career-centered framework for every professional, from graduates to senior executives. You'll start by understanding Gen AI's impact on the job market and decoding core concepts like LLMs, transformers, and prompt engineering. With detailed role descriptions and leveling matrices to identify and build a roadmap tailored to your background. Next, dive into the Gen AI tech stack: RAG architectures, vector databases, function calling, multi-agent systems, and LLMOps. Explore essential frameworks including LangChain, LlamaIndex, Haystack, & Hugging Face, learning when and how to apply each tool effectively. Beyond technical skills, you'll cultivate the human advantages AI cannot replicate, critical thinking, adaptability, creativity, and ethical judgment. Real-world use cases span finance, healthcare, software development, and marketing, demonstrating how Gen AI transforms industries. More than a technical guide, this is a career transformation blueprint. It integrates practical frameworks for skill development, job search strategies, and personal branding in an AI-augmented world.

What you will learn

  • Understand the global impact of Generative and Agentic AI on jobs and industries
  • Master key GenAI & Agentic AI concepts for any professional background
  • Identify your best-fit AI career path and transferable skills
  • Build familiarity with LlamaIndex, Haystack, Hugging Face, and core RAG workflows
  • Build an AI-enhanced personal brand using the 5-Layer LinkedIn Strategy and Portfolio Showcase Framework
  • Apply ethical and responsible AI principles and governance in real-world practice
  • Create a lifelong learning and upskilling roadmap for sustained growth

Who this book is for

This book is for graduates, professionals, and leaders seeking to future-proof their careers and capitalize on the rise of Generative AI. Whether you’re entering the job market, transitioning from a traditional tech or non-tech role, or aiming to lead AI transformation, this guide provides the clarity, tools, and confidence to take control of your trajectory. It’s an indispensable resource for engineers, data scientists, product managers, business analysts, educators, and executives determined to stay relevant in the new world of work.

]]> <![CDATA[ Deployable AI SaaS : From Backend in Python to Stripe Integration ]]> http://skills.scholarvox.com/catalog/book/88982156 Editeur :

Develop a full-stack AI SaaS foundation with FastAPI, authentication, account features, and Stripe billing. Connect protected APIs, credit tracking, frontend flows, webhooks, and end-to-end tests.

Key Features

  • Full-stack coverage of backend services, account flows, billing logic, and application testing
  • Clear product logic for users, API keys, credits, authentication, and Stripe payment flows
  • Production-minded checks show how complete user flows and database health support SaaS quality

Book Description

A dependable AI SaaS product needs more than a model endpoint. The first unit establishes a FastAPI backend with configuration, database models, users, API keys, credits, JWT access tokens, authentication middleware, and protected routes. Readers see how account data, credential checks, authorization failures, and service logic fit together as a coherent application foundation. The next stage extends those services into frontend experiences for registration, login, account management, API keys, and credit balances. Testing connects the client and server before billing work begins. Stripe integration then introduces products, prices, checkout, payment records, event verification, webhooks, and billing pages, with attention to how data moves through the complete user flow. By the end of this guide, readers can assemble and test the core systems behind an AI SaaS application. They will understand how authentication, usage credits, protected APIs, payments, database checks, and frontend interactions work together to support a more deployable product.

What you will learn

  • Build a FastAPI backend with structured configuration
  • Model users, API keys, credits, and account data
  • Implement JWT authentication and protected endpoints
  • Create frontend flows for accounts and credit balances
  • Integrate Stripe checkout, webhooks, and billing records
  • Test complete user journeys and database health

Who this book is for

Python developers, full-stack engineers, SaaS builders, and technical learners creating authenticated AI services. Familiarity with APIs, databases, and frontend concepts is useful; the material provides guided setup for FastAPI, account flows, credits, Stripe billing, and testing.

]]> <![CDATA[ 10 Practice Exams for Microsoft Azure AI Cloud Developer Associate (AI-200) : Test your AI-200 readiness with ten complete exams and detailed answer keys ]]> http://skills.scholarvox.com/catalog/book/88982138 Editeur :

Measure AI-200 readiness with ten practice exams, answer keys, setup guidance, and coverage aligned to the certification objectives. Identify weak areas, refine exam strategy, and focus study where it matters most.

Key Features

  • Ten complete practice exams designed to reveal readiness across all AI-200 objective areas
  • Detailed answer keys supporting review, correction, and focused study after every exam attempt
  • Structured setup and exam guidance for building a consistent, measurable preparation routine

Book Description

Effective certification preparation depends on more than reading technical material; it also requires repeated practice, honest self-assessment, and familiarity with the exam experience. This collection gives readers a structured way to test their readiness for AI-200 and understand how well they can apply the knowledge expected by Microsoft. The opening sections explain exam details, question coverage, prerequisites, setup, and a practical method for taking each test. Readers then work through ten complete practice exams, reviewing an answer key after every attempt. This format supports a steady cycle of testing, checking, and revisiting weak areas rather than relying on passive review. Across repeated attempts, readers can improve recall, spot patterns in missed questions, manage time more deliberately, and build confidence under exam-style conditions. By the end of this book, they will have a clearer view of their strengths, a focused plan for final study, and greater readiness to sit the AI-200 exam.

What you will learn

  • Assess readiness across the AI-200 exam objectives
  • Identify knowledge gaps through repeated exam practice
  • Apply exam strategies under realistic question conditions
  • Review answers to strengthen technical reasoning
  • Track progress across ten complete practice exams
  • Plan targeted study using performance insights

Who this book is for

AI-200 candidates who have completed or are actively studying the relevant Microsoft Azure development objectives will gain the most value. It is suited to Azure developers, cloud engineers, AI application developers, and technical learners who want to assess readiness, identify weak areas, and sharpen exam strategy through repeated practice.

]]> <![CDATA[ Building Claude Skills : Turn repeatable work into reusable Claude Skills ]]> http://skills.scholarvox.com/catalog/book/88982135 Editeur :

Learn to design, test, and optimize Claude Code Skills that automate real business workflows. Connect Claude to external tools, build reusable AI assistants, and scale productivity without writing code.

Key Features

  • Design reusable Claude Skills that automate real business workflows
  • Connect Claude with business tools to build intelligent AI assistants
  • Test, optimize, and scale Skills for individuals and teams

Book Description

Turn repeatable ways of working into Claude Skills that preserve your processes, examples, formatting rules, and quality standards. This book shows you how to move beyond one-off prompts and create reusable workflows that produce more consistent, professional results. Starting with the Claude ecosystem, you’ll learn when to use Skills, Projects, Cowork, connectors, and subagents. You’ll explore five ways to create Skills, refine them for repeated use, schedule recurring workflows, and connect Claude to Gmail, Google Calendar, HubSpot, and other business services. You’ll also learn how to keep SKILL.md focused, organize supporting references, add human-in-the-loop checkpoints, run evals, compare versions through A/B testing, and verify that a Skill activates at the right time. Practical examples cover sales follow-ups, meeting preparation, invoicing, reporting, research, presentations, and content workflows. By the end of the book, you’ll be able to build and share a library of Claude Skills, combine them with connectors and subagents, and reduce repetitive work while keeping your judgment, creativity, and approval central to the process.

What you will learn

  • Understand the Claude ecosystem and when to use Skills
  • Build reusable Claude Skills for business workflows
  • Connect Claude to external business tools and services
  • Design reliable multi-step AI workflows
  • Test, evaluate, and improve Skill performance
  • Optimize Skills for quality, consistency, and efficiency
  • Share and manage Skills across teams
  • Automate everyday work without writing code

Who this book is for

This book is for AI practitioners, automation specialists, solution consultants, technical product managers, low-code builders, and technically inclined professionals who want to design reusable AI workflows with Claude. It is equally valuable for founders, marketers, consultants, operations specialists, analysts, and other knowledge workers looking to automate repetitive tasks and improve productivity. No programming experience is required, although a basic familiarity with AI tools and business workflows will help readers get the most from the book.

]]> <![CDATA[ Claude Code Automation : MCP, Skills, and Production Agentic Workflows ]]> http://skills.scholarvox.com/catalog/book/88982100 Editeur :

Extend Claude Code with MCP connections, GitHub workflows, browser automation, and reusable skills. Build guarded processes that can inspect repositories, manage changes, and investigate web applications.

Key Features

  • Connected workflows linking Claude Code with MCP servers, GitHub operations, and Playwright
  • Clear context for why tool contracts and guardrails make automation safer and easier to audit
  • Practical setup patterns show how reusable skills turn repeated tasks into reliable workflows

Book Description

Useful automation starts with a clear model of what the agent may do, what each tool provides, and where human approval belongs. The opening sections establish project guidance, working memory, planning, and scope controls before introducing the Model Context Protocol. Readers learn how MCP servers expose capabilities through defined tool contracts and why those contracts matter for safe execution. The practical work connects Claude Code to external systems. Readers configure MCP connections, inspect available tools, prepare a local repository, create a GitHub access token, and move through branch, commit, push, and pull-request-oriented tasks. Browser investigation with Playwright adds another layer, showing how an agent can inspect a web application while staying within explicit goals and observable checks. By the end of this guide, readers can design connected workflows rather than isolated prompts. They will be able to combine repository operations, browser automation, and project-level skills into repeatable processes that remain understandable, guarded, and easier to verify.

What you will learn

  • Model agentic workflows with plans and guardrails
  • Configure MCP servers and inspect tool connections
  • Define clear tool contracts for automated tasks
  • Automate GitHub branches, commits, and pushes
  • Investigate web applications with Playwright
  • Package repeatable workflows as Claude Code skills

Who this book is for

Developers, DevOps-minded engineers, automation practitioners, and technical teams using Claude Code across repositories and web applications. Command-line familiarity and a basic understanding of Git, GitHub, and software projects will support the guided setup.

]]> <![CDATA[ Microsoft Azure AI Cloud Developer Associate (AI-200) : Build, secure, deploy, and monitor AI-ready solutions across Microsoft Azure ]]> http://skills.scholarvox.com/catalog/book/88982093 Editeur :

Prepare for AI-200 while building Azure skills across containers, vector data, messaging, serverless apps, security, and observability. Apply exam-focused guidance to design, deploy, secure, and troubleshoot cloud solutions.

Key Features

  • Broad AI-200 coverage spanning containers, data services, messaging, security, and monitoring
  • Scenario-based guidance connecting exam objectives with practical Azure architecture decisions
  • Focused exam tips and chapter reviews for checking knowledge across each major service domain

Book Description

Modern AI applications rely on far more than a model endpoint. They need secure deployment, resilient data layers, event-driven integration, careful configuration, and clear operational insight. This guide frames those demands through the Microsoft Azure services measured by AI-200, helping readers connect exam objectives with the decisions cloud developers face when building production-ready solutions. The journey starts with container images, ACR tasks, App Service, Container Apps, KEDA, and AKS, then moves into Cosmos DB, PostgreSQL, pgvector, Redis, and retrieval-augmented generation patterns. It continues through Service Bus, Event Grid, Azure Functions, Key Vault, App Configuration, OpenTelemetry, Azure Monitor, Application Insights, and KQL, with scenarios, reviews, and exam tips that clarify how the pieces work together. Readers finish with a practical view of how to choose services, secure identities and secrets, manage scale, design vector search, handle messages, deploy serverless APIs, and investigate logs and traces. By the end of this book, they can approach AI-200 questions with stronger judgment and apply the same reasoning to Azure development work.

What you will learn

  • Manage container images with Azure Container Registry
  • Deploy workloads across App Service, Container Apps, and AKS
  • Design vector data solutions with Cosmos DB and PostgreSQL
  • Build event-driven apps with Service Bus, Event Grid, and Functions
  • Secure secrets with Key Vault and Azure App Configuration
  • Monitor distributed apps with OpenTelemetry, Azure Monitor, and KQL

Who this book is for

Azure developers, cloud engineers, AI application developers, and certification candidates preparing for AI-200 will benefit most. The material suits readers with basic cloud and programming familiarity who want practical depth in containers, Python-based data access, vector search, messaging, serverless development, security, and observability.

]]> <![CDATA[ Beyond Code : Build Reliable AI-Assisted Software with Context Engineering, Mechanical Gates, and AI Agent Control ]]> http://skills.scholarvox.com/catalog/book/88982072 Editeur :

Discover why AI changes engineering by moving attention from code output to durable decisions, safer boundaries, and shared understanding

Key Features

  • Apply context engineering to control what AI coding agents see and produce
  • Replace subjective code review with mechanical gates and executable acceptance criteria
  • Design and coordinate multi-agent workflows that close the build-test-deploy loop reliably

Book Description

The advent of AI coding agents has triggered an identity crisis in tech. But the core of software engineering was never just about writing syntax. It is about solving problems, defining constraints, and translating business reality into scalable software solutions. Beyond Code is the practitioner's survival guide to the new landscape of software development, teaching you how to stop competing with the machine and start directing it. The book covers the forces that determine whether AI assistance produces reliable software: context discipline, which shapes what agents see and what they ignore; mechanical gates, which replace advice-based review with verifiable pass-fail conditions; and loop closure, which keeps multi-agent coordination from drifting off-mission through Goodhart traps and proxy decay. You will work through input design, information filtering, decomposition as constraint topology, hierarchical agent coordination, and multi-pass thinking for output verification. By the end of this book, you will be able to manage the information environment your AI agents operate within, enforce the constraint structures that keep them aligned, and build multi-agent workflows that close the build-test-deploy loop without fragile handoffs or compounding failures.

What you will learn

  • Engineer context to control what AI coding agents produce
  • Filter irrelevant information that degrades model output quality
  • Use decomposition to create verifiable, independently testable seams
  • Replace code review opinions with executable mechanical gates
  • Identify and escape Goodhart traps in developer metrics and evals
  • Coordinate AI agents hierarchically to reduce overhead and drift
  • Apply multi-pass thinking to catch failures before they compound
  • Translate software decisions into terms that align engineering teams

Who this book is for

This book is for developers, software engineers, tech leads, engineering managers, and architects who want to stay effective as code generation becomes a default part of the development workflow. It is particularly useful for those who have started using AI coding tools in production and are noticing where the outputs break down in system coherence, review quality, or team alignment. Readers should have experience building, reviewing, or leading software projects.

]]> <![CDATA[ Ollama in Action : Build Fully Private, Multimodal Al Apps ]]> http://skills.scholarvox.com/catalog/book/88982069 Editeur :

Run capable AI models locally with Ollama and connect them to Python. Manage models, build a stateful chatbot, process images with multimodal LLMs, and choose configurations that fit your hardware and privacy needs.

Key Features

  • Local-first coverage connects Ollama commands, Python integration, chat, and multimodal AI
  • Practical context explains why hardware, privacy, speed, and model size shape local choices
  • Complete projects show how conversation memory and image analysis produce useful outputs well

Book Description

Local AI can improve privacy and reduce reliance on cloud inference, but hardware and performance constraints require informed choices. The opening material explains these trade-offs and guides installation on macOS, Windows, or WSL, followed by the commands used to pull, run, list, switch, and remove models. The practical journey connects Ollama to Python and moves beyond simple prompts. Readers send images to a multimodal model, build a chatbot that retains conversation history, and create an application that scans image folders and records structured catalog details in CSV format. Model selection remains tied to available hardware and expected speed. By the end of this guide, readers can manage a local model library and build useful private AI applications. Complete project files make it easier to adapt the chatbot and multimodal workflow to personal experiments or new Python projects.

What you will learn

  • Install Ollama on macOS, Windows, or WSL
  • Manage local models with Ollama commands
  • Connect Ollama to Python applications
  • Build a chatbot with conversation memory
  • Process images with a multimodal model
  • Select models for available hardware

Who this book is for

Python users, developers, and technical learners who want greater privacy, lower ongoing inference costs, and hands-on experience with local AI. No prior AI application development is required, although basic Python familiarity will help with the chatbot and multimodal image cataloging projects.

]]> <![CDATA[ Guide du C++ moderne - de débutant à développeur Ed. 2 ]]> http://skills.scholarvox.com/catalog/book/88981958 Editeur :

Deuxième édition fondée sur le C++23 (la norme actuelle). Un livre ambitieux et exigeant à destination des personnes désireuses d'apprendre le C++ en partant de zéro. Son objectif : vous rendre capable de concevoir et d'implémenter correctement des programmes en C++, conformément aux bonnes pratiques actuelles, et vous doter de bases solides pour que vous puissiez ensuite poursuivre seul votre apprentissage.
Écrit dans un style vivant, sans déroger pour autant à la rigueur, il vous familiarise dans la première partie avec les éléments syntaxiques de base, puis vous apprend dans la deuxième à construire un programme. La troisième partie vous initie aux pratiques d'un développeur C++. Quant à la dernière, elle vous forme à l'un des paradigmes les plus populaires en C++ : la programmation orientée objet.
Intégrant dès sa conception les pratiques actuelles en matière de programmation, ce livre fondé sur le C++23 tire parti des simplifications apportées au langage et vous forme résolument à une vision moderne du C++. 

]]> <![CDATA[ Chimie Tout-en-un PSI/PSI* Ed. 5 ]]> http://skills.scholarvox.com/catalog/book/88981950 Editeur :

LE COURS 

  • Toutes les notions du programme sont abordées  dans le strict respect des textes officiels. 
  • De nombreux exemples, des illustrations et des remarques  pédagogiques vous aident à comprendre le cours en profondeur. 
  • Des scripts Python commentés pour vous aider à maîtriser la programmation. 

LES EXERCICES ET PROBLÈMES 

  • Dans chaque chapitre un grand nombre d’exercices  pour vous entraîner. 
  • Les énoncés sont classés par difficulté progressive. 
  • Les 53 tests et 91 exercices et problèmes sont intégralement résolus. 

]]> <![CDATA[ Maître d'oeuvre bâtiment : Guide pratique, technique et juridique Ed. 11 ]]> http://skills.scholarvox.com/catalog/book/88981930 Editeur :

MAÎTRE D’ŒUVRE BÂTIMENT

Guide pratique, technique et juridique - Préface de Julien Vincent

Élément clé de la stratégie nationale bas carbone, la réglementation RE2020 a profondément transformé la manière de concevoir les projets de bâtiments. Elle impose une collaboration étroite entre architectes, ingénieurs et entreprises afin de réduire significativement l’empreinte carbone des constructions.

Pour aider les acteurs du bâtiment à coopérer efficacement dans ce contexte, l’auteur s’appuie sur son expérience, notamment acquise sur les chantiers, pour apporter des réponses concrètes et directement opérationnelles aux questions les plus courantes.

  • Quel est l’impact, pour la construction bois, des bouleversements du règlement ERP ?
  • Les fluides frigorigènes sont-ils des PFAS ?
  • Dans quelles conditions peut-on utiliser des granulats recyclés ?
  • Qu’est-ce que le temps de retour carbone d’un projet de rénovation énergétique ?
  • Pourquoi prescrire un éclairage mélanopique ?
  • Quelle pente maximale de toiture végétalisée est couverte par les assureurs ?
  • Que prône le mouvement Design for Disassembly ?
  • Qu’est-ce que le « régime de neutre en schéma TN » ?
  • Comment quantifier l’impact d’un projet sur la biodiversité ?
  • Quel est le sens du mot « provisoire » dans l’expression « OS à prix provisoire » ?
  • Comment limiter les risques d’incendie en phase chantier ?
  • Qu’est-ce que la jurisprudence Babel ?
  • Quel maître d’œuvre n’a jamais reçu un avis défavorable d’un bureau de contrôle sans savoir précisément sur quels textes réglementaires il reposait ? Pour aider ses lecteurs à gagner en autonomie et à se repérer dans le maquis des normes et réglementations, l’auteur cite systématiquement les textes officiels à l’origine de chaque prescription.

 

]]> <![CDATA[ Learn Robotics Programming : Build and control cutting-edge AI robots with Raspberry Pi and Python ]]> http://skills.scholarvox.com/catalog/book/88981913 Editeur :

Build smart, AI-powered robots with Raspberry Pi and Python through hands-on projects, from rovers to intelligent autonomous systems Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

  • Boost your Python skills with practical examples to program complex robot behaviors and functionalities
  • Build a robotics learning and exploration platform
  • Leverage AI neural network models for voice recognition and synthesis, and OpenCV for computer vision

Book Description

Learn Robotics Programming, Third Edition, addresses a common challenge for developers, hobbyists, and newcomers: turning robotics ideas into building and programming real, functional robots. While many resources focus either on theory or isolated components, it can be difficult to bring together hardware, software, and intelligent behavior into a working robotic system. This book provides a structured, hands-on path to designing and building robots using Raspberry Pi and Python. You’ll start by assembling a mobile robot and setting up its core systems, then progressively add capabilities such as motor control, sensor integration, and remote operation through web interfaces. As you advance, you’ll implement vision and voice features using OpenCV and ML voice models and explore intelligent behaviors, including localisation and sensor fusion, to help your robot navigate and respond to its environment. By the end of the book, you’ll have built a fully functional robot and developed the skills to design, program, and extend your own robotic systems. Whether you are getting started or already have programming experience, you’ll learn how to combine hardware and software into cohesive solutions and apply practical techniques for creating responsive, intelligent robots. *Email sign-up and proof of purchase required

What you will learn

  • Manage your robots with control panels and view their state with interactive dashboards
  • Integrate sensor systems for dynamic robot navigation and interaction
  • Learn voice recognition with Piper and Vosk, and build computer vision with OpenCV
  • Explore encoder-based localisation with the BNO055 module to enhance your robot's movement accuracy
  • Automate installation and updates with scripts to keep your robot up to date effortlessly
  • Use practical algorithms to process sensor data to guide robot behavior

Who this book is for

This robotics book is ideal for programmers, developers, and robotics enthusiasts with at least beginner coding skills who are eager to design, build, and program cutting-edge robots using autonomous robot algorithms along with AI models. Basic knowledge of the Python programming language will help you understand the concepts covered in this robot programming book more effectively.

]]> <![CDATA[ Machine Learning for Trading : A disciplined workflow from research to live execution, with nine case studies and AI agents ]]> http://skills.scholarvox.com/catalog/book/88981912 Editeur :

Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAP

Key Features

  • Build point-in-time pipelines, integrate alternative data, and ensure data integrity
  • Build and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signals
  • Deploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generators

Book Description

The rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning. It introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems. You’ll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna. Practical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets. By the end of this book, you’ll be proficient to build your own industrial-grade “alpha factory".

What you will learn

  • Transform raw data into predictive alpha factors, validated with leak-proof cross-validation
  • Master advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agents
  • Harness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standards
  • Build production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safely

Who this book is for

If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

]]> <![CDATA[ Android Programming for Beginners : Learn Android, Kotlin & Jetpack from scratch and Turbocharge progress with AI ]]> http://skills.scholarvox.com/catalog/book/88981911 Editeur :

Learn Android app development from scratch with Kotlin and Jetpack Compose. Build 35+ hands-on apps, games, and database projects while mastering modern Android Studio workflows and Kotlin fundamentals.

Key Features

  • Build Android apps with Kotlin, Jetpack Compose, Android Studio, and Agentic Programming
  • Create hands-on projects including, UI, sound, graphics, databases and game
  • Master modern Android development workflows and Kotlin fundamentals

Book Description

Modern Android development has evolved rapidly, with Kotlin and Jetpack Compose becoming the standard tools for building powerful mobile applications. Android Programming for Beginners provides a practical introduction for readers with little or no coding experience. Rather than overwhelming you with theory, the book teaches concepts gradually through hands-on experimentation and small, focused apps that reinforce core programming fundamentals. You will begin by learning Kotlin essentials including variables, loops, functions, lambdas, collections, and object-oriented programming before moving into Android Studio and Jetpack Compose. As your skills grow, you will create interactive projects ranging from UI layouts and games to multimedia and database-driven applications. Along the way, you will explore Android development techniques such as state management, navigation, Material Design, Room databases, Canvas drawing, and responsive layouts. To help future-proof your workflow, each chapter also includes optional AI-assisted programming exercises that show how modern coding tools can support experimentation, accelerate development, and deepen understanding. By the end of the book, you will have built a portfolio of Android apps and gained the confidence to continue toward advanced mobile development projects independently.

What you will learn

  • Install and configure Android Studio for Android development
  • Build Android apps with Kotlin and Jetpack Compose
  • Understand variables, loops, functions, and Kotlin syntax
  • Create interactive apps with touch, sound, and graphics
  • Work with collections, lambdas, and scope functions
  • Build multi-screen apps with navigation and Room databases
  • Develop a game while mastering Kotlin OOP concepts
  • Learn how to (optionally) use AI to speed up learning and development

Who this book is for

This book is for beginners who want to learn Android app development using Kotlin and Jetpack Compose. It is ideal for readers with no programming experience, developers moving from Java to Kotlin, and anyone wanting a practical, project-based introduction to modern Android development.

]]> <![CDATA[ PHP Programming in the AI Era : Build faster PHP applications using GenAI, modern PHP features, and production-ready workflows ]]> http://skills.scholarvox.com/catalog/book/88981910 Editeur :

Accelerate PHP performance with AI by generating, refining, and optimizing code using modern patterns, async workflows, and secure design

Key Features

  • Use Generative AI to write, optimize, and debug PHP 8 code in real-world scenarios
  • Build high-performance applications using JIT, OPcache, Fibers, and async programming
  • Implement robust encryption, CAPTCHA, session protection, and test-driven development with AI assistance

Book Description

PHP offers several powerful features, but many developers struggle to apply them effectively in real-world applications while maintaining performance, security, and code quality. This book addresses that challenge by combining modern PHP practices with generative AI to help you build faster and more reliably. You will work through hands-on recipes that show how to design, generate, and refine PHP 8 applications using AI alongside proven engineering techniques. From writing clean object-oriented code and designing scalable architectures to automating repetitive tasks, you will learn how to use AI as a development aid without losing control over quality. As you progress, you will optimize applications using OPcache, JIT, and async programming, while applying secure coding practices and modern design patterns. The book also explores real-world use cases, including REST APIs, microservices, WebSocket applications, and database-driven systems, helping you translate concepts into production-ready solutions. You will also learn how to modernize legacy PHP codebases, validate AI-generated code, and integrate testing workflows to improve reliability and maintainability. Additionally, you will understand how to enhance PHP websites by making API calls to GenAI platforms. By the end of this book, you will be able to combine PHP and generative AI effectively to build secure, high-performance applications ready for real-world deployment.

What you will learn

  • Use GenAI prompts to generate, refactor, and optimize PHP classes and functions
  • Create high-performance apps using JIT, OPcache, and async programming
  • Build secure web apps with session protection, encryption, CAPTCHA, and token-based forms
  • Work with databases using AI-generated queries and caching strategies
  • Design scalable systems with OOP, design patterns, and microservices
  • Develop REST APIs, WebSockets, and real-time applications using AI assistance
  • Apply test-driven development with AI-generated acceptance criteria and automated tests

Who this book is for

This book is for PHP developers who already understand the basics of PHP, including variables, functions, and object-oriented programming, and have worked with earlier versions of PHP. It's aimed at developers who want to use generative AI tools to improve their day-to-day development workflow and build applications faster. Readers will learn through practical, real-world examples as they explore modern PHP features. The book is ideal for developers who want to leverage AI-powered coding tools while still maintaining control over the quality, security, and reliability of the code.

]]> <![CDATA[ Hacking Hardware : Penetration Testing and Defenses for Hardware-Based Attacks ]]> http://skills.scholarvox.com/catalog/book/88981909 Editeur :

See how attackers exploit hardware, wireless connections, and peripheral devices. Practice penetration testing, red teaming, awareness training, and defensive countermeasures for real systems.

Key Features

  • Hardware attack scenarios expose USB, Bluetooth, Wi-Fi, RFID, and wired LAN system weaknesses
  • Security awareness methods connect technical vulnerabilities with safer workplace behavior
  • Defensive guidance pairs practical pentest devices with analysis and suitable countermeasures

Book Description

Hardware can create an attack surface even when software controls appear strong. The opening chapters frame penetration testing and red teaming from the attacker's perspective, explain how vulnerabilities are identified, and use practical test scenarios to show how simple devices can compromise infrastructure. Attention then shifts to the human layer of defense. Readers examine security awareness training, successful teaching methods, and realistic exercises that help teams recognize suspicious devices and unsafe behavior. This emphasis connects technical findings with repeatable procedures that reduce exposure before an incident occurs. The final section surveys pentest hardware and real attack paths involving spy gadgets, keyloggers, USB interfaces, wireless signals, RFID, Bluetooth, Wi-Fi, wired LANs, and multipurpose hacking devices. Guidance on analyzing detected hardware and selecting countermeasures keeps the focus on prevention as well as discovery. By the end of this journey, readers can plan practical hardware-focused assessments, communicate risks, and strengthen defenses against device-based attacks.

What you will learn

  • Perform structured IT security penetration tests
  • Apply red teaming methods to realistic scenarios
  • Design effective security awareness training
  • Assess USB, Bluetooth, Wi-Fi, RFID, and LAN attack paths
  • Identify threats from loggers, spy gadgets, and pentest tools
  • Select countermeasures for hardware-based vulnerabilities

Who this book is for

Written for security administrators, IT consultants, developers, and administration teams responsible for protecting systems and infrastructure. It is especially useful for professionals planning penetration tests, security awareness programs, or defenses against hardware-based attacks.

]]> <![CDATA[ PyTorch : Build, Evaluate, and Deploy Deep Learning Models ]]> http://skills.scholarvox.com/catalog/book/88981908 Editeur :

Build deep learning models with PyTorch for vision, recommendations, time series, and language tasks. Train, evaluate, fine-tune, monitor, and deploy models with modern supporting tools.

Key Features

  • Broad model coverage spans vision, recommendations, graphs, forecasting, and language tasks
  • Production-focused workflows combine training metrics, monitoring, fine-tuning, and deployment
  • Modern PyTorch tooling includes Lightning, TensorBoard, MLflow, FastAPI, and Hugging Face tools

Book Description

Deep learning concepts are introduced alongside the PyTorch workflow needed to turn them into working models. Readers begin with model creation and progress through regression and classification, gaining the theoretical context required to understand evaluation tools such as confusion matrices and ROC curves. The middle of the journey expands into computer vision, recommendation systems, autoencoders, graph neural networks, time series forecasting, and language models. Hands-on exercises show how to create datasets, train networks, process sequential data, and generate images, while pretrained networks, Hugging Face fine-tuning, and PyTorch Lightning broaden the options for efficient development. The closing material focuses on training visibility and production use. MLflow and TensorBoard support logging, metric review, and monitoring, while FastAPI and Heroku illustrate deployment on local infrastructure or in the cloud. By the end of this journey, readers can build, tune, evaluate, and deploy PyTorch models across a wide range of practical deep learning tasks.

What you will learn

  • Build neural networks with PyTorch
  • Train regression and classification models
  • Create computer vision and recommendation systems
  • Develop autoencoders and graph neural networks
  • Forecast time series and process language data
  • Evaluate, monitor, and deploy trained models

Who this book is for

Ideal for developers, machine learning engineers, data scientists, and research scientists who want practical PyTorch experience. Readers will benefit from an interest in building, evaluating, and deploying deep learning models across several application areas.

]]> <![CDATA[ Applied Machine Learning : Practical Models for Solving Real-World Business Problems ]]> http://skills.scholarvox.com/catalog/book/88981907 Editeur :

Turn business data into reliable machine learning models through practical use cases. Prepare data, select algorithms, evaluate results, and monitor performance to measure real-world impact.

Key Features

  • Business-focused use cases link data preparation, model selection, and measurable outcomes
  • Model decision guidance clarifies when regression, trees, boosting, or clustering fit best
  • End-to-end coverage connects evaluation, interpretability, deployment, and monitoring plans

Book Description

Business-focused machine learning begins with a clear view of the data and the decision it must support. The opening material establishes practical tools such as GitHub and Anaconda, introduces three use cases with dedicated datasets, and shows how visualization, descriptive statistics, correlation analysis, cleaning, and dummy coding shape dependable inputs. The discussion then moves through a structured model-selection process. Readers compare regression, decision trees, random forests, gradient boosting, and clustering, while learning when each approach fits a business need. Validation metrics, interpretability, and iterative feature engineering provide a disciplined way to judge results, expose weak assumptions, and refine performance without treating the model as a black box. The final stage connects analysis to operations through implementation, monitoring, prediction workflows, and impact measurement. Readers see how model quality must be maintained after launch and how outcomes can be linked to business value. By the end of this journey, readers can prepare data, choose and evaluate suitable models, and manage machine learning solutions from initial idea through long-term use.

What you will learn

  • Prepare datasets for machine learning analysis
  • Compare models using a structured decision framework
  • Build regression, tree, boosting, and clustering models
  • Evaluate predictions with appropriate validation metrics
  • Improve interpretability through feature engineering
  • Deploy and monitor models for measurable business impact

Who this book is for

Designed for administrators, DevOps teams, and professionals who want to apply machine learning to business problems. It suits readers seeking a practical path through data preparation, model selection, evaluation, implementation, and ongoing monitoring.

]]> <![CDATA[ Microsoft 365 Copilot and Agent Administration Fundamentals : Build practical skills and confidently prepare for the Microsoft AB-900 certification exam ]]> http://skills.scholarvox.com/catalog/book/88981906 Editeur :

Build a solid foundation in Microsoft 365 administration by understanding Microsoft 365 core services, identity and access, security, compliance, governance, Microsoft 365 Copilot, and AI agents while preparing for the AB-900 certification DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

  • Learn Microsoft 365 Copilot, security, governance, and AI agent administration
  • Configure, secure, monitor, and govern Copilot in Microsoft 365
  • Verify your knowledge of key concepts through chapter assessments, insider tips, and practice questions

Book Description

Microsoft 365 Copilot and AI agents are transforming how organizations collaborate, automate workflows, and manage productivity. This practical certification guide prepares you for the Microsoft AB-900 exam while building real-world administration skills for modern AI-powered workplaces. Aligned with the latest Microsoft Learn objectives, the book covers Microsoft 365 services, security principles, governance controls, and Copilot administration fundamentals. You’ll begin with Microsoft 365 essentials, including licensing, Exchange Online, SharePoint, Teams, identity management, authentication, Conditional Access, and Zero Trust. You’ll then explore Microsoft Purview capabilities, including data classification, sensitivity labels, retention, DLP, Insider Risk Management, Communication Compliance, and DSPM for AI. The book also explains how Copilot accesses organizational data through Microsoft Graph, how to identify oversharing risks in SharePoint, and how to use Purview tools to investigate compliance and governance issues. Finally, you’ll learn how to manage Copilot licensing, billing, prompts, usage analytics, and AI agent lifecycles using Microsoft 365 and Power Platform admin centers. By the end of this book, you’ll be ready to confidently take the AB-900 exam and support secure, compliant Microsoft 365 Copilot deployments. *Email sign-up and proof of purchase required

What you will learn

  • Configure core Microsoft 365 services and admin centers
  • Understand Zero Trust, identity, and access management
  • Protect data using Microsoft Purview governance tools
  • Identify and reduce oversharing risks in SharePoint
  • Understand how Copilot accesses organizational data
  • Manage Copilot licensing, billing, and analytics
  • Create, approve, and monitor Microsoft 365 agents
  • Prepare confidently for the Microsoft AB-900 exam

Who this book is for

This book is for Microsoft 365 administrators, IT support professionals, security and compliance teams, and technical professionals preparing for the Microsoft AB-900 certification exam. It is also ideal for business technology professionals who want to understand how Microsoft 365 Copilot and AI agents are deployed, governed, and managed in enterprise environments. Familiarity with Microsoft 365 services such as Teams, SharePoint, Exchange Online, and Microsoft Entra will help you follow along, but prior Copilot administration experience is not required.

]]> <![CDATA[ Cyber Threat Intelligence : Collect, Analyze, and Operationalize Actionable Threat Intelligence ]]> http://skills.scholarvox.com/catalog/book/88981905 Editeur :

Transform raw threat data into intelligence that strengthens security operations. Apply collection, profiling, forensics, threat hunting, incident response, and automation techniques.

Key Features

  • Full-lifecycle coverage connects intelligence planning, collection, analysis, and feedback
  • Operational examples unite threat profiling, forensics, incident response, and threat hunting
  • Platform and automation guidance supports feed enrichment, integration, and scalable workflows

Book Description

Effective cyber threat intelligence starts with a disciplined understanding of what intelligence is, where it comes from, and how it supports decisions. Readers follow the intelligence lifecycle from planning through feedback and compare sources including OSINT, HUMINT, SIGINT, and material from the deep and dark web. The central chapters turn collection into analysis. Threat actor profiling, behavioral mapping, feed integrity, poisoning, and enrichment are connected with network-centric forensics, host-based analysis, and Windows telemetry. The MITRE ATT&CK framework and practical workflows help readers convert technical evidence into clear, actionable intelligence rather than isolated indicators. The final stage integrates intelligence with security operations. Incident response, proactive threat hunting, automation, and threat intelligence platforms are explored through practical examples and case-based guidance, with attention to feed quality and operational adoption. By the end of this journey, readers can organize collection, assess adversary behavior, interpret forensic evidence, and apply intelligence across modern defensive workflows.

What you will learn

  • Explain the cyber intelligence lifecycle
  • Collect intelligence from OSINT, HUMINT, and SIGINT
  • Profile threat actors and map their behavior
  • Analyze network and host forensic evidence
  • Integrate intelligence into incident response
  • Automate threat intelligence workflows

Who this book is for

Best suited to security analysts, cyber threat intelligence teams, and security operations center professionals. It supports readers who need to strengthen intelligence collection, forensic analysis, incident response, threat hunting, automation, and operational integration.

]]> <![CDATA[ Ethical Hacking : Practical Penetration Testing and Red Teaming Techniques ]]> http://skills.scholarvox.com/catalog/book/88981904 Editeur :

Develop practical ethical hacking skills across the full penetration testing lifecycle. Use labs and challenges to investigate vulnerabilities, exploit safely, escalate access, and report findings.

Key Features

  • Complete pentest coverage follows reconnaissance, exploitation, escalation, and reporting steps
  • Realistic labs address web attacks, passwords, social engineering, malware, and covert channels
  • Professional context links hands-on techniques to MITRE ATT&CK, NIST, PTES, and OWASP standards

Book Description

Professional ethical hacking begins with a clear understanding of authorization, responsible practice, and the stages of a penetration test. Readers move from footprinting and reconnaissance into scanning, enumeration, and fuzzing, using guided environments such as TryHackMe to explore how weaknesses are found. The technical core develops practical attack and analysis skills across Metasploit, cryptography, covert communication, and password cracking. Web application risks are examined through the OWASP Top 10, the OWASP Juice Shop, cross-site scripting, and SQL injection, followed by social engineering, reverse shells, privilege escalation, and malware concepts. Security models, MITRE ATT&CK, zero trust, PTES, NIST, and OWASP standards provide professional context. The final material brings the techniques together through realistic challenges and professional pentest practices, including evidence gathering, risk communication, and reporting. Integrated exercises and supporting tutorials reinforce both exploitation and defense. By the end of this journey, readers can conduct structured security assessments, test common attack paths responsibly, and communicate findings that support stronger protection.

What you will learn

  • Conduct reconnaissance, scanning, and enumeration
  • Exploit XSS and SQL injection in controlled labs
  • Use Metasploit for structured penetration testing
  • Analyze cryptography and password-cracking methods
  • Perform privilege escalation and reverse-shell techniques
  • Document findings through professional pentest reports

Who this book is for

Created for security administrators, red team members, and practitioners developing professional penetration testing skills. It is appropriate for readers who want structured, hands-on experience with common attack methods, security frameworks, responsible testing, and reporting.

]]> <![CDATA[ The Orange Book of Machine Learning Green edition : The essentials of making predictions using supervised regression and classification for tabular data ]]> http://skills.scholarvox.com/catalog/book/88981903 Editeur :

Learn supervised machine learning for tabular data using Python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL for regression, classification, and predictive modeling

Key Features

  • Explore, clean, and prepare tabular datasets for machine learning workflows
  • Build regression and classification models using modern machine learning tools
  • Improve predictions with calibration, conformal intervals, and optimization techniques

Book Description

Master the essential tools and techniques for supervised machine learning on tabular data with this practical guide to regression and classification. Through clear explanations, code snippets, and hands-on notebooks, you'll learn how to use Python and leading machine learning libraries, including pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL, to build predictive models for real-world datasets. The book covers the complete workflow, from data exploration and cleaning to model development, evaluation, and optimization. You'll learn how to perform regression analysis for accurate point predictions and estimate uncertainty using conformal prediction intervals. For classification tasks, you'll explore probabilistic predictions and calibration techniques to improve model reliability. You'll also discover practical approaches to feature engineering, feature selection, and hyperparameter optimization to enhance model performance. In addition, the book introduces tabular foundation models and in-context learning techniques, providing insight into the latest advances in machine learning for structured data. By the end of the book, you'll have the skills and confidence to develop, evaluate, and deploy supervised machine learning models for a wide range of tabular data applications.

What you will learn

  • Perform exploratory data analysis and data cleaning
  • Apply cross-validation for reliable model evaluation
  • Build regression models and prediction intervals
  • Develop calibrated probabilistic classification models
  • Optimize models through hyperparameter tuning
  • Engineer and select features for improved performance
  • Use ensemble learning methods effectively
  • Explore tabular foundation models and in-context learning

Who this book is for

This book is designed for motivated self-learners, university students studying applied machine learning, junior data scientists, and academic researchers looking to incorporate machine learning into their analytical workflows. Readers should have a basic familiarity with Python and data analysis concepts. Whether you're developing predictive models for business, research, or educational purposes, this book provides the practical guidance needed to apply modern machine learning techniques to structured and tabular datasets.

]]> <![CDATA[ Santé environnementale et politiques locales : Guide pratique à l'usage des collectivités ]]> http://skills.scholarvox.com/catalog/book/88981876 Editeur :

À la fois guide pratique et ouvrage de référence, l'objectif de Gilbert Hangard à travers cet ouvrage est d'aider les élus locaux en leur donnant les clés de compréhension des liens entre action publique et impact environnemental. Il invite à agir pour la préservation de l'environnement. Il explore les multiples dimensions de l'approche « One Health » et les conditions de sa mise en oeuvre, mais aussi tout une multitude d'initiatives pensées pour construire une transition juste, inclusive et résiliente. L'ouvrage propose des solutions concrètes pour les territoires à travers des exemples de terrain et des leviers opérationnels.

]]> <![CDATA[ Fruits Oubliés n° 83 : A la quête des glands doux, Passiflores, décroiscience, et cultiver la pluie ]]> http://skills.scholarvox.com/catalog/book/88981844 Editeur :

Pour ce numéro d'été, nous retournons vers quelques fruits moins connus...

Ainsi nous traiterons du bigaradier, qui devient de plus en plus adaptable sur nos territoires puisque vous trouverez dans ce numéro une description et un contact pour garnir votre verger d'agrumes fructifère qui résiste à des températures inférieures à -15°C.

Vous trouverez aussi dans ce numéro, la troisième et dernière partie de notre dossier sur les figuiers de Barbarie, dans lequel nous avons essayé de faire un point non exhaustif de quelques variétés cultivées...

Et bien sur, plusieurs autres dossiers concernant un appel à la gratuité des cantines scolaires bio signé de Paul Ariès, ou d'autres initiatives à propos d'une alimentation plus saine pour la restauration collective... Et un autre dossier liés à l'alimentation fermentée...

C'est donc un numéro que nous vous invitons à dévorer avec passion...

]]> <![CDATA[ Fruits Oubliés n° 82 : Saveur et bienfaits des oignons et de l'olivier ]]> http://skills.scholarvox.com/catalog/book/88981843 Editeur :

Pour ce numéro d'Automne, nous abordons une thématique que nous n'avions pas encore exploré : les Oignons. Pourtant indispensable à une cuisine savoureuse, nous devions commencer d'en faire le tour...

Nous revenons aussi sur l'Olivier, avec un nouveau dossier et une méthode de multiplication par éclat de souche...

Vous trouverez aussi dans ce numéro, un excellent article sur l'élevage et la châtaigneraie en Corse, la suite du dossier sur les aliments fermentés, le début d'un travail sur la Rose, et  notamment la Rose de Provins..

Et bien sur, un Bona Fama sur les usages du Gatillier, arbre réputé d'être l'allié des femmes...

Un nouveau numéro que nous vous invitons à découvrir pour les fêtes de fin d'année 2025, que nous vous souhaitons joyeuses...

]]> <![CDATA[ Marine Propeller Theory and Application ]]> http://skills.scholarvox.com/catalog/book/88981805 Editeur :

Marine propellers have been the primary means of ship propulsion for centuries and remain at the heart of modern marine engineering. Reflecting the latest advances in the field, this book provides a comprehensive introduction to the theory, design, analysis, and performance of marine propellers. Combining fundamental principles with practical applications, it presents a wealth of numerical results derived from computational fluid dynamics (CFD) simulations, state-of-the-art measurement and testing techniques, and detailed design case studies. The eight chapters cover the development of marine propulsion systems, propeller geometry, propulsion theory, open-water testing, ship–propeller interaction, cavitation, structural strength, and chart-based propeller design. Moving beyond purely theoretical treatments, the book offers an accessible and practice-oriented approach. Through extensive CFD visualizations and engineering examples, it illustrates the complex relationships between propeller geometry, hydrodynamic performance, cavitation behaviour, and structural integrity. It also introduces modern design, manufacturing, measurement, and testing technologies, together with advanced chart-based design methods that extend beyond conventional efficiency-focused approaches.

This work will be a valuable reference for researchers, graduate students, and engineers in naval architecture, marine engineering, ship hydrodynamics, and propulsion system design.

]]> <![CDATA[ Python for Algorithmic Trading Cookbook : Recipes for designing, building, and deploying algorithmic trading strategies with Python ]]> http://skills.scholarvox.com/catalog/book/88981729 Editeur :

Transform financial market data into algorithmic trading strategies and deploy them into a live trading environment with recipes leveraging modern Python libraries like pandas, Polars, and DuckDB

Key Features

  • Backtest Python trading strategies with VectorBT and Zipline Reloaded using walk-forward analysis
  • Measure risk, performance, and alpha quality with Alphalens Reloaded and PyFolio
  • Automate strategy execution with the Interactive Brokers API for live trading

Book Description

Get practical Python code for algorithmic trading from Jason Strimpel, founder of PyQuant News and a veteran of global trading, risk management, and machine learning. This hands-on guide shows you how to turn market data into tested, automated trading strategies using modern Python tools. You’ll source equities, options, and futures data with OpenBB and FMP, then accelerate Python for data analysis workflows with Pandas, Polars, Parquet, DuckDB, and ArcticDB. You’ll visualize market data with Matplotlib, Seaborn, and Plotly Dash before moving into alpha research and quantitative trading techniques. Detailed recipes help you engineer alpha factors with PCA, regression, Fama-French models, SciPy, and statsmodels. You’ll design and evaluate quantitative trading strategies using VectorBT, Zipline Reloaded, Alphalens Reloaded, and PyFolio, including walk-forward analysis and risk-aware performance review. For execution, you’ll connect to the Interactive Brokers API to stream ticks, manage orders, retrieve portfolio state, and monitor live trading workflows. By the end, you’ll have reusable Python templates for researching, backtesting, evaluating, and operating algorithmic trading strategies.

What you will learn

  • Acquire equities, futures, and options data using OpenBB and FMP
  • Process and analyze time series data efficiently with pandas and Polars
  • Store and query massive datasets with ArcticDB, DuckDB, and Parquet
  • Visualize trading data using Matplotlib, Seaborn, and Plotly Dash
  • Engineer alpha factors using PCA, regression, and Fama-French models
  • Backtest strategies with VectorBT and Zipline Reloaded frameworks
  • Evaluate performance and risk using Alphalens Reloaded and PyFolio
  • Deploy and automate live trades using the Interactive Brokers API

Who this book is for

This book is for traders, investors, and Python enthusiasts who need practical code to acquire, analyze, and automate algorithmic trading strategies using modern, high-performance Python tools. Readers should have some exposure to investing or trading, a basic familiarity with Python syntax, and a basic knowledge of libraries such as Pandas and NumPy. This book is ideal for discretionary traders who want to adopt a systematic approach and apply professional techniques, such as factor modeling, backtesting, and execution automation, to trading workflows using Python.

]]> <![CDATA[ AI for Anyone : The Beginner's Guide to AI ]]> http://skills.scholarvox.com/catalog/book/88981728 Editeur :

Start using AI with confidence, master prompting, generate content, automate tasks, and apply AI safely in daily life and work, even with zero technical background

Key Features

  • Build effective prompts using clarity, context, and structured frameworks
  • Apply AI for writing, visuals, planning, and everyday productivity tasks
  • Use AI responsibly with guidance on privacy, bias, and professional boundaries

Book Description

Artificial intelligence is no longer limited to specialists. It is a practical tool that anyone can use to improve how they work, create, and solve problems. This book provides a clear path to understanding and applying AI tools without requiring technical expertise. You will start by learning how to access and navigate popular AI platforms, followed by a focused introduction to prompting techniques that turn vague requests into useful outputs. The book introduces structured methods such as goal definition, context setting, and iterative refinement to improve results. You will then explore practical applications, including drafting emails, summarizing content, generating visuals, brainstorming ideas, and organizing information. Each use case focuses on execution so you can apply AI immediately in real scenarios. The book also addresses responsible AI use. You will learn how to identify unreliable outputs, avoid common prompting mistakes, protect sensitive data, and apply ethical considerations. By the end, you will be able to use AI tools confidently, improve output quality through better prompts, and make informed decisions about when and how to use AI effectively.

What you will learn

  • Write clear prompts using structured frameworks
  • Refine AI outputs through iteration techniques
  • Generate text and images with practical methods
  • Automate daily tasks using AI tools effectively
  • Apply AI for planning, research, and idea generation
  • Identify errors, bias, and unreliable AI outputs
  • Protect data and use AI responsibly in workflows
  • Decide when AI is appropriate vs human input

Who this book is for

This book is for developers and technical professionals who want to understand how AI tools can improve productivity without requiring deep AI expertise. It is also suited for beginners, career switchers, and knowledge workers looking to integrate AI into everyday workflows. Readers will benefit if they want practical guidance on prompting, using AI tools effectively, and applying them safely in both personal and professional contexts. No prior experience with AI or programming is required.

]]> <![CDATA[ Polished Ruby Programming : Principles and practices for building scalable, maintainable, and performant software ]]> http://skills.scholarvox.com/catalog/book/88981727 Editeur :

Learn design principles, best practices, and trade-offs involved in implementation approaches, to improve your Ruby programming skills

Key Features

  • Understand the design principles behind polished Ruby code, and trade-offs involved in different implementation approaches
  • See how to use plugin systems to build libraries for maximum flexibility and performance
  • Learn about the advantages and disadvantages of different approaches to concurrency

Book Description

Most successful Ruby applications become more difficult to maintain as the codebase grows in size. Polished Ruby Programming, 2nd Edition provides you with the skills and advice you need to design Ruby programs and libraries that are robust, performant, scalable, and maintainable. The book takes you through possible implementation approaches for many common programming situations, discusses the trade-offs inherent in each approach, and explains why you may sometimes choose to use different approaches. You'll start by learning fundamental Ruby programming principles, such as correctly using core classes, class and method design, variable usage, error handling, and code formatting. Then you’ll move on to higher-level topics such as library design, metaprogramming, domain-specific languages, and refactoring. Finally, you'll learn about the pros and cons of different approaches to concurrency, what you should consider when deciding whether to use static types in your Ruby code, and how best to optimize your Ruby code. The 2nd edition of Polished Ruby Programming has been updated to include relevant changes between Ruby 3.0 and 4.0. While most principles discussed in the book apply to all recent Ruby versions, some of the content in the book is specific to Ruby 4.0, the latest release at the time of publication.

What you will learn

  • Use Ruby's core classes and design custom classes effectively
  • Explore the principles behind variable usage and method argument choice
  • Design extensible libraries and plugin systems in Ruby
  • Use metaprogramming and DSLs to avoid code redundancy
  • Implement different approaches to testing and understand their trade-offs
  • Discover design patterns, refactoring, and optimization with Ruby
  • Learn about the trade-offs inherent in different concurrency approaches
  • Determine whether using static types in Ruby makes sense for you

Who this book is for

If you already know how to program in Ruby and want to learn more about the principles and best practices behind writing maintainable, scalable, optimized, and well-structured Ruby code, then this book is for you. Intermediate to advanced-level working knowledge of the Ruby programming language is expected to get the most out of this book.

]]> <![CDATA[ You Don't Need JavaScript : A practical guide to creating modern websites and interfaces using only CSS ]]> http://skills.scholarvox.com/catalog/book/88981726 Editeur :

A CSS-first guide to replace unnecessary JavaScript with modern HTML and CSS. Learn to build modals, dark mode, smooth scrolling, form validation, popovers, and more with modern HTML and CSS.

Key Features

  • Build common UI patterns with modern HTML and CSS instead of unnecessary JavaScript
  • Learn solid production techniques and latest platform features you can adopt with progressive enhancement
  • Explore real interface problems about accessibility, performance, and maintainability with real-world examples

Book Description

For years, JavaScript has been the default answer for almost every interactive detail on the web. Need a modal, a dropdown, dark mode, smooth scrolling, or form validation? Most frontend developers reach for JavaScript without thinking twice. But the platform has changed. This book shows how much modern HTML and CSS can already do on their own. Through practical examples, you will learn how to build real interface patterns with less code, fewer dependencies, and a stronger focus on accessibility, performance, and maintainability. You will explore features such as :has(), native dialogs, accordions, sliders, counters, dark mode, smooth scrolling, form validation, border animations, view transitions, motion paths, and mask effects. The book also introduces newer browser features like popovers, anchor positioning, scroll-driven animations, customizable selects, and CSS carousels. The goal is not to avoid JavaScript at all costs. It is to use it when it actually adds value, and not by default. By the end of the book, you will have a clearer sense of what the platform can handle today and how to build interfaces that are lighter, cleaner, and more resilient.

What you will learn

  • Apply the Rule of Least Power in real UI decisions
  • Choose modern HTML and CSS over JS where it's sufficient
  • Build lightweight, accessible interfaces in practice
  • Use modern CSS features like :has(), view transitions, motion paths, masks, popovers, and anchor positioning
  • Create UI patterns like dark mode, dialogs, accordions, smooth scrolling, custom selects, and carousels
  • Implement progressive enhancement in real projects
  • Improve performance by reducing unnecessary JS

Who this book is for

This book is for people who build interfaces and want to rely less on JavaScript when they do not have to. It is aimed at front-end developers, designers who code, and curious CSS people who already know the basics and want to see how far modern HTML and CSS can really go. If you have ever added JavaScript out of habit for something the browser can already handle, this book will give you a different way to think about that.

]]> <![CDATA[ Building AI-Powered Financial Products : Use responsible AI to launch ROI-driven FinTech products at scale ]]> http://skills.scholarvox.com/catalog/book/88981725 Editeur :

Apply generative AI, AI agents, LLMs, and proven frameworks to build trustworthy, scalable FinTech products across payments, lending, digital banking, insurance, and wealth management

Key Features

  • Build scalable AI-powered financial products with real-world case studies, templates, and frameworks
  • Integrate generative AI and LLMs into financial products while ensuring trust and compliance
  • Learn from real-world successes, failures, and best practices across the financial services ecosystem
  • Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Book Description

In today’s financial product landscape, AI is no longer optional, it is non-negotiable. AI is not here to replace product managers and FinTech professionals, but to enhance their skills and amplify their impact. As the industry moves beyond traditional apps into AI-driven platforms, Building AI-Powered Financial Products is the practical guide for everyone building in this space. Unlike financial management books that lean heavily on theory, this book delivers actionable, real-world insights. Each chapter is grounded in real-world scenarios, including a FinTech startup case study that demonstrates frameworks and best practices in action. For financial professionals looking to harness generative AI and LLMs, this book offers the practical tools and templates needed to innovate confidently within regulatory boundaries. By the end, you’ll be equipped to design and scale AI-driven financial products, strengthening your position as a forward-thinking product and FinTech leader in this fast-moving landscape shaped by generative AI. *Email sign-up and proof of purchase required

What you will learn

  • Design, launch, and scale AI-powered financial products
  • Integrate generative AI and LLMs into FinTech products
  • Navigate AI governance, regulation, and compliance
  • Balance customer-centric design with monetization models
  • Learn from FinTech success and failure stories
  • Apply AI-first product frameworks to scale products
  • Discover how AI agents will reshape financial services

Who this book is for

This book is for product managers, engineers, innovation leads, entrepreneurs, and professionals across financial services and technology who aim to become AI-driven leaders in product innovation. Written for professionals involved in building digital products, it shows how generative AI can be used to improve customer experiences and unlock new growth opportunities across the financial ecosystem. Working experience with software products and digital platforms is helpful, but no deep expertise in AI or finance is required, key ideas are explained with clear context.

]]> <![CDATA[ From Cloud Native to AI Native : Catching the Next Wave of Innovation ]]> http://skills.scholarvox.com/catalog/book/88981724 Editeur :

Cloud Native to AI Native explores how organizations transition to AI-first systems using proven patterns, scalable architectures, and strategic frameworks to build, deploy, and optimize next-generation AI-powered platforms

Key Features

  • Proven frameworks to transition from cloud-native to AI-native architectures
  • Practical pattern-based approach to designing scalable AI platforms
  • Strategic guidance for driving enterprise AI transformation and innovation

Book Description

Cloud Native to AI Native is a strategic guide to navigating the next major shift in technology evolution moving from cloud-first systems to AI-driven platforms. As organizations race to adopt artificial intelligence, many struggle to align architecture, strategy, and execution. This book provides a structured framework to help leaders and engineers make informed decisions and avoid costly mistakes. Drawing on real-world experience and proven methodologies, it introduces a pattern-based approach to transformation, enabling you to assess your organization’s maturity and identify the right path forward. You’ll explore key operational modes such as bootstrapping, scaling, optimizing, and innovating, while learning how to design AI-ready platforms that integrate seamlessly with existing cloud-native foundations. The book offers practical insights into building resilient, scalable systems that treat data and models as first-class components, ensuring long-term adaptability. By connecting the evolution of cloud-native architectures with the emerging AI-native paradigm, it provides clarity in an otherwise rapidly changing landscape. This book equips you with the tools, frameworks, and mindset needed to successfully transition into the AI-native era and unlock real business value.

What you will learn

  • Understand the shift from cloud-native to AI-native architectures
  • Identify where your organization sits on the technology evolution curve
  • Apply transformation patterns to design scalable AI platforms
  • Navigate operational modes like scaling, optimizing, and innovating
  • Build AI-ready systems integrated with existing cloud infrastructure
  • Anticipate future technology trends and adoption strategies
  • Avoid common anti-patterns in enterprise AI transformation
  • Drive measurable business value from AI initiatives

Who this book is for

This book is designed for technology leaders, solution architects, cloud engineers, and enterprise decision-makers looking to navigate the transition from cloud-native to AI-native systems. It is ideal for professionals responsible for digital transformation, platform engineering, and AI adoption strategies. Consultants, CTOs, and innovation leaders will benefit from its structured frameworks, enabling them to design scalable AI platforms, align business goals with emerging technologies, and drive measurable outcomes in rapidly evolving markets.

]]> <![CDATA[ AI Under Attack : A Practical Guide to Threats, Defenses, and Governance for AI Systems ]]> http://skills.scholarvox.com/catalog/book/88981723 Editeur :

Built on Fortune 500 experience, this guide delivers hands-on methods to secure generative AI with extensive coverage of RAG, agents, prompt injection, data pipelines, Zero Trust, and sustainable programs. Includes the AI Under Attack Practitioner Toolkit, featuring chapter-specific Field Artifacts for real-world AI security practice.

Key Features

  • Defend LLMs, RAG, and autonomous agents against prompt injection, jailbreaks, and tool abuse
  • Apply Zero Trust architecture to AI agents with tool access, memory, and goal-directed reasoning
  • Run AI governance and red teaming programs aligned to NIST AI RMF, ISO 42001, and OWASP for LLMs
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Contrary to general AI texts or cybersecurity books with limited AI coverage, this guide offers a comprehensive dive into securing the generative AI ecosystem. It moves through four parts: Foundations establishes why AI security is fundamentally different, covering threat modeling, attack surfaces, and core defense principles. Attacks provides deep technical examination of prompt injection, memory and context abuse, RAG system vulnerabilities, agent exploitation techniques, training data poisoning, and AI red teaming methodology. Building Secure AI Systems covers infrastructure and MLOps hardening, secure application and API design, defensive prompt engineering, guardrails with human oversight, supply chain integrity, and Zero Trust architecture for agents. Running AI Security Programs addresses governance, risk and compliance frameworks, security engineering practices, security operations, and building sustainable organizational capabilities. Throughout, you will gain access to practical insights and structured approaches applicable to real-world scenarios. By the end, you will be able to design, implement, and maintain security programs for generative AI, defend against advanced threats, communicate risks to stakeholders, and establish governance ensuring secure, compliant operations across the lifecycle.

What you will learn

  • Identify AI-specific risks and clearly communicate them to business teams
  • Defend models, data, RAG, and agents from threats like poisoning, prompt injection, jailbreaking, and data exfiltration
  • Design resilient cloud/MLOps with Zero Trust, supply chain security, and isolation
  • Build secure APIs, apps, and agents with strong auth, validation, and safe tool use
  • Apply AI-focused GRC, alignment checks, bias mitigation, monitoring, and incident response
  • Translate complex concepts into actionable steps, using threat intel and collaboration for lasting security

Who this book is for

This book is for mid- to senior-level cybersecurity professionals, security architects, and tech leaders managing risks in generative AI deployments. It’s also valuable for early-career practitioners, AI/ML engineers, red teamers, DevSecOps, governance specialists, compliance officers, and product stakeholders with foundational cybersecurity knowledge. Readers should have basic familiarity with security concepts, some exposure to cloud platforms (AWS, Azure, or GCP), and a fundamental grasp of AI/ML, though no prior AI security expertise is required.

]]> <![CDATA[ LLMs for Modern Software Delivery and DevOps : Applying Large Language Models to Software Delivery and SRE ]]> http://skills.scholarvox.com/catalog/book/88981722 Editeur :

A practical guide to applying LLMs across the software development and delivery lifecycle, improve development, testing, operations, and project efficiency across modern software organizations.

Key Features

  • Apply LLMs to modern DevOps workflows across development and operations with practical enterprise examples
  • Build architectural fluency in GPT, fine-tuning, RAG, and agent-based systems
  • Strengthen software delivery pipelines with AI-informed automation and operational intelligence

Book Description

If you work in DevOps, SRE, platform engineering, software delivery, operations, testing, or security, this book shows how large language models (LLMs) can reduce delivery friction, improve operational visibility, and support more reliable engineering workflows. Written by enterprise digital transformation and delivery specialists, it focuses on moving LLMs beyond isolated experiments into practical software delivery systems. You will build the LLM foundations needed to understand modern AI systems, including language model evolution, Transformer architecture, GPT-style generation, and efficient fine-tuning techniques such as LoRA and QLoRA. The book then connects these foundations to enterprise-ready patterns such as retrieval-augmented generation (RAG), multi-agent systems, and platform-based AI assistance. Through operations, testing, coding, project management, and cybersecurity scenarios, you will see how LLMs can support log analysis, ticket handling, root cause analysis, test generation, code generation, risk management, and security workflows. By the end of the book, you will understand how to move from model experimentation to practical AI-assisted delivery, evaluate where LLMs create measurable value across DevOps, SRE, and platform engineering workflows, and recognize the constraints, risks, and governance considerations involved.

What you will learn

  • Apply RAG and multi-agent patterns to enterprise software delivery and platform engineering scenarios
  • Use LLMs to support operations tasks such as log analysis, ticket handling, incident response, and root cause analysis
  • Explore how LLMs can improve software testing, static analysis, vulnerability repair, and test automation workflows
  • Apply code LLMs to development workflows, including code generation, completion, review support, and project-level coding tasks
  • Use LLMs to support project management, delivery coordination, risk analysis, and cybersecurity workflows
  • Evaluate the practical value, risks, and constraints of introducing LLMs into DevOps, SRE, and platform engineering environments

Who this book is for

This book is for software engineers, DevOps and SRE professionals, QA and security teams, and technical managers who want to apply and operationalize LLMs across the software delivery lifecycle.

]]> <![CDATA[ Machine Learning Engineering on AWS : Build, deploy, and operationalize LLMs, AI agents, and generative AI systems on AWS ]]> http://skills.scholarvox.com/catalog/book/88981721 Editeur :

Solve machine learning engineering challenges for GenAI-powered systems and AI agents on AWS, and automate LLMOps pipelines using Amazon Bedrock, SageMaker AI, Bedrock AgentCore, and Strands Agents. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

  • Build and scale AI agents using Amazon Bedrock AgentCore and Strands Agents
  • Fine-tune, evaluate, and deploy ML models using Amazon SageMaker AI
  • Automate LLMOps workflows with SageMaker Pipelines

Book Description

Modern AI systems increasingly leverage large language models, retrieval-augmented generation, and AI agents to power generative AI applications in the cloud. As organizations operationalize these systems at scale, there is a growing need for engineers with strong machine learning engineering expertise. To stay ahead in this rapidly evolving field, you need a deep understanding of AI and ML concepts as well as, practical, hands-on experience with the platforms and tools used to build and operate production-grade AI systems. Machine Learning Engineering on AWS is a practical guide that shows you how to use AWS services such as Amazon Bedrock and Amazon SageMaker AI to fine-tune, evaluate, and deploy LLMs and generative AI systems. You'll learn how to develop RAG-powered systems, build and deploy AI agents using Bedrock AgentCore and Strands Agents, evaluate models using LLM-as-a-judge techniques, and automate LLMOps pipelines using SageMaker Pipelines. The book also covers best practices for building scalable, secure, and production-ready GenAI systems. AWS AI hero Joshua Arvin Lat equips you with the skills and practical knowledge to handle a wide variety of ML engineering requirements, helping you design, operationalize, and secure generative AI systems and AI agents on AWS with confidence. *Email sign-up and proof of purchase required"

What you will learn

  • Build and deploy AI agents using Bedrock AgentCore and Strands Agents
  • Dive deep into ML engineering with Amazon SageMaker AI
  • Evaluate model performance using LLM-as-a-judge
  • Explore advanced model fine-tuning and deployment using SageMaker AI
  • Build RAG-powered systems using Bedrock Knowledge Bases and S3 Vectors
  • Modernize analytics with a managed transactional data lake
  • Automate LLMOps pipelines using SageMaker Pipelines and AWS Lambda
  • Explore best practices for building GenAI systems and AI agents on AWS

Who this book is for

This book is intended for AI engineers, data scientists, machine learning engineers, and technology leaders who want to deepen their understanding of machine learning engineering, generative AI, large language models, retrieval-augmented generation, AI agents, and MLOps on AWS. A foundational understanding of artificial intelligence, machine learning, generative AI, and cloud engineering concepts is recommended.

]]> <![CDATA[ Godot 4 Best Practices : Practical techniques and strategies for efficient, scalable game development ]]> http://skills.scholarvox.com/catalog/book/88981720 Editeur :

Elevate your Godot 4 development skills with SOLID principles, game architecture patterns, and scalable workflows. Learn to create maintainable systems, organize projects, and build release-ready games that grow beyond prototypes Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

  • Apply SOLID principles and proven design patterns in Godot 4
  • Build scalable game architecture using components, events, and services
  • Refactor prototypes into maintainable, production-ready projects

Book Description

Many Godot projects start as quick prototypes but become difficult to maintain as they grow. Deep node hierarchies, tightly coupled scripts, and expanding gameplay systems can slow development and make it harder to add features, fix bugs, and ship with confidence. Godot 4 Best Practices addresses those challenges by focusing on architectural practices that help projects stay scalable and maintainable. You’ll start by applying the SOLID principles to Godot’s node and script model, learning when to choose scenes or scripts, how to keep hierarchies shallow, and when data-driven Resources are a better fit than extra nodes. Next, you’ll implement core design patterns, including signals and notifications for decoupling, state machines, strategy-based AI, and swapping deep inheritance for modular components. You’ll also apply Factories, Builders, Commands, and Services with clear Godot examples, so systems stay flexible and testable. Finally, you’ll structure larger projects with data-driven saves and preferences, as well as layered gameplay architecture to avoid God classes. By the end, you’ll write cleaner GDScript, organize large scenes with confidence, and build systems that are easier to test, maintain, and extend. No hype—just proven practices for building Godot projects that scale. *Email sign-up and proof of purchase required

What you will learn

  • Apply SOLID principles in Godot's node and script system
  • Decide when to use scenes, scripts, or Resources for logic
  • Use signals, State, and Strategy patterns for gameplay logic
  • Build modular, component-based systems over inheritance
  • Automate instancing with Factory and Builder patterns
  • Decouple input, audio, and saves with Command and Service patterns
  • Separate logic from data with Resources and data-driven design
  • Structure gameplay systems using scalable architectural patterns

Who this book is for

This book is ideal for intermediate and advanced Godot developers who already know how to make simple games but want to adopt professional practices for larger, more complex projects. It is especially useful for indie developers, technical leads, and small teams who need scalable workflows, as well as educators and advanced students who want to learn how to apply SOLID principles and design patterns in Godot. If you’ve ever struggled with messy prototypes, monolithic scripts, or unscalable Scene Trees, this book will show you how to organize, refactor, and future-proof your projects.

]]> <![CDATA[ AI in UX Design : How UX Designers are Using AI in the Age of Artificial Intelligence ]]> http://skills.scholarvox.com/catalog/book/88981719 Editeur :

Move beyond AI hype and learn how modern UX designers use ChatGPT, Claude, Figma, Lovable, Replit, and no-code tools to turn insight into tested experiences while keeping judgment, ethics, and users at the center

Key Features

  • Discover how AI shifts UX from static deliverables to active product orchestration
  • Apply prompts for briefs, personas, journeys, requirements, and usability planning
  • Prototype earlier with no-code AI tools and reduce handoff risk before build

Book Description

AI in UX Design helps practicing designers understand what changes when generative AI becomes part of everyday product work. Rather than treating AI as a shortcut or a threat, this updated and expanded edition shows how to use it as a teammate for framing problems, synthesizing research, drafting artifacts, and learning from users sooner. Explore how ChatGPT, Claude, Gemini, Figma, Lovable, Replit, v0.dev, Bolt.new, and Base44 fit into modern UX workflows. The book shows how AI can support discovery, personas, JTBD, user flows, requirements, accessibility checks, localization tests, and no-code prototyping. A real case study follows a marketplace idea from opportunity framing to personas, storyboards, UI flows, a functional prototype, and user validation, showing how designers can move from concept to evidence in hours or days rather than weeks. The book also addresses the expanding role of UX designers as orchestrators who guide intelligent systems, shape product direction, and collaborate closer to build. With guidance on responsible AI design, transparency, human control, fairness, safety, and accountability, it keeps human-centered design at the core of faster workflows. Designed for professionals who know UX fundamentals, this book offers a grounded path to working with AI confidently, critically, and creatively.

What you will learn

  • Judge AI outputs as hypotheses, not final answers
  • Turn raw research into clearer design briefs
  • Draft personas, JTBD, flows, and requirements
  • Prototype earlier with Lovable, Replit, and Figma
  • Validate ideas with users before engineering begins
  • Spot AI bias, hallucinations, and false confidence
  • Apply responsible AI principles to UX decisions
  • Position your UX role for strategy and ownership

Who this book is for

This book is for practicing UX designers, product designers, senior designers, and design leaders who understand UX fundamentals and want to adapt their process for a generative AI world. It is ideal for professionals in digital products, SaaS, enterprise software, platforms, and consumer apps who want faster discovery, sharper synthesis, earlier prototyping, and stronger product influence. No AI expertise, coding background, or machine learning knowledge is required; the focus is on practical workflows, judgment, validation, and responsible design.

]]> <![CDATA[ Practical LLM Evaluation for Production Systems : Measure, monitor, and improve AI system reliability across training and inference ]]> http://skills.scholarvox.com/catalog/book/88981718 Editeur :

Build reliable Build reliable AI evaluation frameworks that measure quality, safety, grounding, and production readiness across modern LLM and SLM applications Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

  • Design evaluation frameworks for LLMs, SLMs, multimodal, reasoning, and agentic AI systems
  • Measure quality, safety, grounding, robustness, and production readiness with practical metrics
  • Apply unified evaluation methods to text, multimodal, and agentic AI systems

Book Description

Modern AI systems are expected to do far more than generate fluent text. They should be able to retrieve information, reason through complex problems, understand images and documents, call external tools, execute workflows, and support critical business decisions. Evaluating these systems requires methods that go beyond traditional NLP benchmarks. Taking a product-first approach, this book presents evaluation as a continuous operational capability spanning training, inference, and end-to-end system operation. You'll learn how to connect evaluation metrics directly to deployment gates, rollback criteria, monitoring systems, and production reliability objectives. Using practical examples and real-world workflows, you'll explore evaluation strategies for text LLMs, vision-language models, multimodal conversational systems, mixture-of-experts architectures, reasoning models, agentic systems, retrieval pipelines, Text2SQL and Text2Cypher systems, embedding models, OCR workflows, and guardrail SLMs. You'll also learn how to manage non-determinism, design repeatable test suites, validate tool execution, and measure long-horizon agent behavior in production. By the end of the book, you'll be able to design robust evaluation systems that help teams deploy reliable, safe, and economically viable LLM-powered applications with confidence. *Email sign-up and proof of purchase required

What you will learn

  • Design repeatable evaluation pipelines for LLM systems
  • Assess inference quality, latency, and operational cost
  • Evaluate multimodal, agentic, and reasoning AI systems
  • Build regression gates and deployment evaluation workflows
  • Detect hallucinations and grounding failures in VLMs
  • Assess routing stability in mixture-of-experts models
  • Evaluate Text2SQL, OCR, and retrieval-based systems
  • Translate evaluation signals into production decisions

Who this book is for

ML engineers, GenAI engineers, AI architects, data scientists, platform engineers, and engineering managers responsible for deploying LLM-powered systems in production will benefit from this book. Applied AI researchers and technical decision-makers looking to measure reliability, safety, and operational readiness across modern AI systems will also find it valuable. Readers should have a working understanding of machine learning, Python, and modern LLM concepts.

]]> <![CDATA[ Odoo 19 Development Cookbook : Build production-grade ERP applications with OWL, REST APIs, and scalable server-side logic ]]> http://skills.scholarvox.com/catalog/book/88981717 Editeur :

Master Odoo's latest development practices to build powerful, scalable ERP applications with modern integrations, OWL, and frontend tools. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

  • Develop advanced Odoo apps using OWL, integrations, and modular design
  • Configure, scale, and maintain secure, multi-user ERP systems
  • Test, integrate, and optimize Odoo apps with APIs, profiling, and caching tools

Book Description

The Odoo 19 Development Cookbook, Sixth Edition, equips developers to build high-performance, production-ready ERP applications using the most current version of Odoo. Whether you’re working on backend models or crafting interactive frontend components with OWL, this edition delivers practical, developer-focused recipes aligned with the platform’s latest features and architecture. You’ll explore key workflows such as external integrations, system profiling, advanced debugging, and deploying applications with tools like Odoo.sh. Real-world scenarios guide you through configuration screens, POS customization, and multi-language deployment, helping you navigate and master the evolving Odoo development stack. This edition introduces expanded coverage of OWL and JavaScript development, external integrations, automated testing, performance profiling, and real-world deployment setups. It also includes new recipes for debugging, POS customization, and integration with legacy systems based on developer feedback and platform updates. Written with input from leading Odoo experts and shaped by direct user feedback, this updated edition features modern JavaScript development, frontend and backend integration, and scalable architecture, all tailored to the needs of ERP professionals building production systems. *Email sign-up and proof of purchase required

What you will learn

  • Set up a reliable and scalable Odoo development environment
  • Build reusable models, views, and OWL components
  • Use RPC APIs and controllers to integrate external platforms
  • Extend the Odoo POS with new UI features and logic
  • Optimize app performance through profiling and caching
  • Secure apps with roles, permissions, and visibility rules
  • Manage configuration and deployment using Odoo.sh workflows
  • Debug backend and frontend flows with real-time tooling

Who this book is for

This book is for Python and JavaScript developers building or extending ERP applications with Odoo. It’s ideal for newcomers seeking practical guidance and experienced developers adapting to recent platform changes. A working knowledge of Python and basic web technologies is recommended.

]]> <![CDATA[ Python Automation Cookbook : 100+ new and updated recipes for scalable workflows, MCP integrations, and AI-powered automation ]]> http://skills.scholarvox.com/catalog/book/88981716 Editeur :

Build practical automation skills through an expanded third edition featuring new chapters on AI models, MCP, and intelligent agents, guiding you from core Python workflows to modern agentic automation.

Key Features

  • Apply proven Python automation recipes to solve real-world tasks
  • Integrate AI models and build intelligent agents for business workflows
  • Extend automation across systems using MCP and modern integrations

Book Description

Automating repetitive tasks and integrating systems efficiently becomes increasingly complex as workflows scale. This book helps you solve that problem with practical Python recipes that guide you from foundational automation to advanced, AI-powered workflows. You start by building a strong base in Python automation, exploring tested solutions for file handling, web scraping, APIs, testing, and system operations, and learning how to design reliable automation workflows. The cookbook approach enables you to quickly apply solutions to real problems while building a deeper understanding through hands-on practice. This third edition expands the scope of automation by introducing AI-powered capabilities. You learn how to call AI models within your scripts, use and implement the Model Context Protocol (MCP) for system integration, and design intelligent agents that automate decision-making processes. New chapters provide real-world examples of AI agents in business automation, helping you move beyond scripts to adaptive systems. This book combines practical knowledge with modern techniques to ensure you stay current with evolving automation practices. By the end of this book, you will be able to design, build, and extend Python automation workflows, including AI-driven solutions, to handle complex real-world tasks with confidence.

What you will learn

  • Automate file, system, and network tasks using Python
  • Build robust scripts for web scraping and API integration
  • Design scalable automation workflows for real use cases
  • Integrate AI models into automation pipelines
  • Implement MCP for system-level automation integration
  • Develop intelligent agents for business automation
  • Apply testing and debugging techniques for automation
  • Create real-world AI-driven automation solutions

Who this book is for

Python developers, automation engineers, DevOps professionals, and system administrators who want to streamline workflows and integrate modern AI capabilities into automation. A basic understanding of Python programming is recommended.

]]> <![CDATA[ The Ultimate Ethical Hacker's Guide : Build job-ready skills with hands-on labs in recon, exploitation, and professional reporting ]]> http://skills.scholarvox.com/catalog/book/88981715 Editeur :

Develop end-to-end ethical hacking skills with hands-on labs covering reconnaissance, exploitation, and reporting, designed to mirror real security assessments and build job-ready cybersecurity skills

Key Features

  • Practice ethical hacking through hands-on labs across the full attack lifecycle
  • Test modern environments, including wireless and enterprise networks
  • Create clear, professional reports from real-world security findings
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Build practical ethical hacking skills through realistic attack scenarios that reflect how security assessments are performed in real organizations. This book helps you move beyond theory by showing how vulnerabilities are discovered, exploited responsibly, and reported clearly. Written by Glen D. Singh, a cybersecurity instructor and author with deep experience in offensive security, networking, and security operations, this book emphasizes practical skill development and responsible testing practices. You begin by learning ethical hacking principles, methodologies, and legal considerations before setting up a safe lab. Then you progress through reconnaissance, scanning, and enumeration to understand how attackers gather intelligence and how defenders reduce exposure. You’ll continue with vulnerability assessment, system hacking, malware basics, and network attacks using structured labs. As environments grow more complex, you will explore wireless attacks and the techniques used to assess and secure wireless networks. The final chapters focus on professional reporting and career development, showing how to present findings clearly and ethically. By the end of this book, you will be able to perform ethical hacking engagements from reconnaissance to reporting with confidence.

What you will learn

  • Build and manage a safe ethical hacking lab
  • Perform reconnaissance, scanning, and enumeration techniques
  • Identify, assess, and prioritize security vulnerabilities
  • Exploit systems and networks using ethical methods
  • Escalate privileges and maintain access during assessments
  • Use industry-standard ethical hacking tools and frameworks
  • Create clear technical and executive security reports

Who this book is for

This book is for aspiring ethical hackers, penetration testers, and cybersecurity professionals who want hands-on experience with ethical hacking techniques, reconnaissance, and security assessment methodologies. Basic knowledge of networking and operating systems is recommended.

]]> <![CDATA[ System Design for the LLM Era : Patterns and principles for production-grade AI architecture ]]> http://skills.scholarvox.com/catalog/book/88981714 Editeur :

STOP building fragile AI wrappers; START designing resilient AI systems.

Key Features

  • From LLM fundamentals to real-world practicalities
  • Patterns and principles for architecting LLM-based systems
  • Learn from in-depth case studies
  • Decouple from premium models using tiered fallback
  • Event-driven architectures for decoupling high-latency agentic workflows
  • Cost management approaches
  • Security strategies for LLM systems
  • Glossary of LLM and AI systems design terminology included

Book Description

Many companies are trying to turn their small AI experiments into big products, but they lack a good plan. Engineers need a practical guide to building these new AI systems the right way, so that they can handle scale, won't cost too much to build or operate, and perform reliably. This book is that guide, combining technical depth with breadth and practicality. Starting from LLM fundamentals, the book details the architectural patterns and design principles needed to build production-grade AI systems. In-depth case studies then show you how to apply them to a range of real-world application scenarios, including AI-native IDEs, adaptive learning platforms, and intelligent search solutions. The book provides a deep, practical look at the real-world challenges and solutions for building systems with LLMs at their core.

What you will learn

  • Architect a complete, production-grade AI-powered system from scratch
  • Design and mitigate the unique challenges of LLM APIs, like high latency and cost
  • Implement key software engineering patterns like circuit breakers and rate limiting for AI systems
  • Choose the right databases and data models for AI applications, including vector search engines
  • Build a scalable and resilient system that can handle high load and ensure user privacy

Who this book is for

This book will be an invaluable learning resource for engineers, architects and leads working with LLMs or looking to integrate LLMs into their existing systems.

]]> <![CDATA[ Distributed AI Systems : A practical guide to building scalable training, inference, and serving systems for production AI ]]> http://skills.scholarvox.com/catalog/book/88981713 Editeur :

Learn distributed AI through hands-on experience with training frameworks, inference engines, and orchestration tools to build production-ready training, inference, and serving systems for modern large-scale AI. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

  • Understand GPU hardware, high-speed interconnects, and parallelism strategies
  • Hands-on exercises at the end of every chapter
  • Learn distributed training with resource-optimized techniques
  • Deploy high-performance inference with advanced optimization and memory management
  • Build production serving stacks with job schedulers, orchestration, and observability

Book Description

As AI models grow to billions and trillions of parameters, distributed systems are essential for training and serving them. Many resources cover fragments of this domain, but none provide a full path from distributed training to inference and production deployment. This book fills that gap with practical, production-focused examples. It starts with GPU and memory estimation, data preparation, and an overview of GPU architecture, interconnects, and core parallelism strategies. You’ll learn training techniques including data parallelism for single and multi-node setups, parameter sharding for memory-efficient scaling, and methods to reduce memory usage in large models. The next section covers distributed inference and deployment. You’ll build high-performance systems using optimized attention, caching, operator fusion, and router-based designs. You’ll deploy on schedulers and container platforms with GPU-aware orchestration and assemble production stacks emphasizing reliability, scalability, and observability. The final section covers benchmarking, performance tuning, and trends like MoE models, edge-cloud coordination, and advanced parallelism. Each chapter includes tested code and debugging guidance. By the end, you’ll be able to build distributed AI systems that scale from a single GPU to large clusters.

What you will learn

  • Estimate memory and compute requirements for training and inference
  • Understand GPU hardware, interconnects, and parallelism strategies
  • Implement distributed training with parallel and sharded techniques
  • Build production inference systems with batching and memory management
  • Deploy via cluster orchestration with optimized GPU scheduling
  • Create production serving stacks with routing and observability
  • Benchmark distributed systems using industry-standard methodologies
  • Explore emerging model trends, scaling strategies, and future paths

Who this book is for

This book is designed for ML engineers, AI researchers, and DevOps professionals who need to train or serve large AI models at scale. Platform engineers, HPC cluster administrators, and cloud architects will also find it valuable for advancing their skill sets. A basic understanding of Python and PyTorch is required to get started. Prior experience with distributed systems, cluster schedulers, or container orchestration is helpful but not necessary - the book introduces these concepts from the ground up, beginning with resource estimation, data preparation, and hardware fundamentals.

]]> <![CDATA[ Hands-On Image Processing and Computer Vision with Python : From image processing fundamentals to modern computer vision and generative AI ]]> http://skills.scholarvox.com/catalog/book/88981712 Editeur :

Explore the world of image processing, computer vision, and generative AI with Python—from fundamental concepts and classical methods to deep learning, modern vision systems, and real-world visual content generation. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

Key Features

  • Master end-to-end image processing and computer vision workflows using Python
  • Build visual AI systems with classical, deep learning, and generative AI techniques
  • Apply theory with production-ready implementations using leading Python libraries
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Analyzing and understanding visual data has become essential in modern applications such as healthcare, security, remote sensing, manufacturing, and digital media. This book provides a hands-on guide to image processing and computer vision using Python, following a practical approach that bridges theory with implementation. As you progress through the chapters, you will develop proficiency in Python 3 and implement algorithms spanning classical image processing, modern computer vision, and state-of-the-art (SOTA) deep learning and generative AI. The book covers image enhancement, restoration, filtering, segmentation, feature extraction, classification, and object detection using libraries including NumPy, OpenCV, PIL, SciPy, scikit-image, scikit-learn, TensorFlow, Keras, and PyTorch. Advanced chapters introduce CNNs, Vision Transformers, transformer-based segmentation, modern detection frameworks, GANs, diffusion models, foundation models, image-to-image translation, super-resolution, and multimodal vision-language understanding. Real-world applications span medical imaging, remote sensing, banking, augmented reality, autonomous driving, industrial inspection, and intelligent visual analytics. By the end of the book, you will be equipped to design and implement real-world visual computing solutions. *Email sign-up and proof of purchase required

What you will learn

  • Build image processing and computer vision pipelines
  • Apply image enhancement, restoration, and segmentation
  • Implement image classification and object detection models
  • Explore CNNs, Vision Transformers, and attention models
  • Generate and edit images using GANs and diffusion models
  • Develop multimodal vision-language AI applications
  • Apply visual AI across diverse real-world domains
  • Implement super-resolution, style transfer, and image-to-image translation

Who this book is for

Python developers, engineers, applied researchers, students, and AI practitioners who want to build end-to-end image processing and computer vision systems. A working knowledge of Python is required, while familiarity with linear algebra, calculus, and basic machine learning concepts will help you get the most from the advanced topics.

]]> <![CDATA[ How to Sell to the CIO and CISO : ?An insider's guide to building trust and gaining executive buy-in ]]> http://skills.scholarvox.com/catalog/book/88981711 Editeur :

Selling to CIOs and CISOs isn't easy. Learn how internal champions drive change and how trusted vendors consistently win C-suite deals.

Key Features

  • Understand what CIOs and CISOs truly prioritize when selecting vendors
  • ?Build and enable champions who influence decisions from within
  • ?Learn how to become a trusted partner to the C-Suite
  • ?Purchase of the print or Kindle book includes a free PDF eBook

Book Description

The days of the 'polished pitch' are dead. You can’t just show up with a slick slide deck and expect a CIO or CISO to be impressed. Today’s tech leaders are juggling digital transformation and risk management while trying to cut costs and keep the board happy. They don't have a spare second for a generic sales pitch that doesn't "get" their reality. To win today, you have to shift your perspective. You need to understand how these leaders actually make decisions, what’s keeping them up at night, and how internal politics can stall even the best ideas. With insights from a plethora of cross-industry CIOs and CISOs, this book is your guide to seeing the world through the eyes of the C-suite. Taking you through the entire sales journey, from initial contact to walking into the office of the CIO or CISO, from gap analysis to contracting, you will learn what executives look for beyond the demo, how to empower internal champions to fight for you, and how to earn real credibility. You will know how to shift every conversation from feature-talk to outcome-talk, anchoring your value to the metrics that really matter to the board. ?The C-suite sale isn't about pressure, it’s about understanding people and helping them succeed. This book is essential reading for anyone navigating high stakes enterprise technology sales.

What you will learn

  • How CIOs and CISOs evaluate vendors beyond features and price
  • ?The real priorities driving C-suite technology buying decisions
  • ?How to identify and enable champions who advocate from within
  • ?The language of outcomes that resonate with executive buyers
  • ?Practical frameworks for navigating C-suite sales conversations
  • ?Learn to sell into major transformations
  • ?How to build trust that goes beyond the contract and the close

Who this book is for

?This book provides a practical guide to mastering the C-suite technology sale, written for account executives and sales leaders who struggle to close with CIOs and CISOs, and for internal champions who want to build influence, drive strategic decisions, and grow their career from within. Unlike most sales books, this is written by someone who has seen both sides of this process, as a former CIO, CISO, and an internal champion, and someone who has sold consulting services, products, and business cases.

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