AI & Machine Learning ENG (52 résultats)

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Paperback. Etat : new. Paperback. Mastering XGBoost is an essential skill for professionals working in data science, machine learning, and predictive model engineering in critical environments. Widely used in technical competitions, business solutions, and large-scale production systems, XGBoost is a leading reference for performance, stability, and control in supervised learning pipelines. This book offers a direct, applied, and technically precise approach to all core aspects of the library, focusing on real-world applications and professional validation.Developed in accordance with the TECHWRITE 2.3 Protocol, the content is ideal for data scientists, machine learning engineers, technical analysts, and students seeking to master XGBoost with an operational focus and full system integration. Its modular structure allows for a progression from conceptual understanding to technical deployment, with explained code, recommended practices, and structured error resolution.You will learn how to build robust models for classification, regression, and multiclass problems, with advanced evaluation, automated tuning, and integration into production environments.Includes: Structured data pipelines using Pandas and NumPySupervised modeling with XGBClassifier and XGBRegressorTuning with GridSearchCV, RandomizedSearchCV, and cross-validationExplainability using SHAP Values, importance_gain, and visualizationsTime series forecasting with sliding windowsDeployment via Flask, FastAPI, Streamlit, and DockerGPU (CUDA) execution and Dask clustersIntegration with AWS SageMaker and enterprise modelingCase studies using public datasets and a final professional checklistMaster XGBoost and position yourself with technical authority in projects that demand accurate predictions, reliable validation, and end-to-end delivery aligned with systems and strategic objectives. This book is your professional reference manual for the most powerful algorithm in modern supervised modeling.xgboost, supervised machine learning, classification and regression, hyperparameter tuning, deployment with Flask and FastAPI, SHAP Values, time series, Dask, GPU, SageMaker, enterprise projects, explainability, predictive modeling, production pipelines This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Etat : new. Paperback. LEARN Scikit-Learn: Essential Machine Learning for Data ScienceThis book is the key to mastering Scikit-Learn and building market-ready Machine Learning projects. Ideal for professionals and students who seek fast, direct, and applicable results. Learn high-impact techniques, design intelligent solutions, and boost your career in data science.Includes: - Quick setup of a productive environment with Python and Scikit-Learn- Data preprocessing and full pipeline automation- Fine-tuning models with cross-validation and hyperparameters- Practical application of regression, classification, and clustering- Model deployment integrated with Big Data and MLOps environmentsMaster the most widely used algorithms in the market and accelerate your entry into data science projects with Scikit-Learn.scikit-learn, machine learning, data science, smart pipelines, model automation, cross-validation, machine learning deployment, big data integration, mlops, spark This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Etat : new. Paperback. This book is ideal for developers and students who want to build deep learning models with PyTorch, combining flexibility, control, and real-world performance.You will learn to apply modern techniques to design, train, and scale robust neural networks in real-world environments, covering everything from fundamentals to advanced architectures.Includes: - Tensor manipulation and Autograd usage- Modular neural network construction with torch.nn- Training with DataLoader, optimizers, and loss functions- Practical application with CNNs, RNNs, Transformers, and GANs- Integration with PyTorch Lightning, TorchScript, and model export- Real-world projects with NLP, computer vision, IoT, and cloud deploymentBy the end, you will master PyTorch as a professional tool to design scalable AI solutions with technical precision and development agility. ytorch, deep learning, neural networks, torch.nn, autograd, distributed training, cnn, rnn, transformers, deployment This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Etat : new. Paperback. CONTEXT ENGINEERING (A.I.) Book 01: Fundamentals & Frameworks Master Concepts to Optimize Interactions and Design High-Performance AI ModelsThis book is intended for students and professionals seeking to understand the concepts of Context Engineering to design, implement, and optimize Artificial Intelligence systems based on context-awareness and situational inference - the core of modern cognitive architectures.Structured for practical application and integration with real frameworks, the content presents solid foundations and functional examples that connect semantic modeling, contextual memory, adaptive inference, and operational governance within AI ecosystems. It explores architectures, pipelines, and frameworks that support the new generation of contextual agents, focusing on efficiency, traceability, and scalability.You will perform: - Implementation of context pipelines using Python, LangChain, FAISS, Pinecone, and Milvus- Structuring of contextual layers and cognitive orchestration with Semantic Kernel, CrewAI, and LangGraph- Application of RAG (Retrieval-Augmented Generation) in contextual generation flows and expanded memory- Configuration of vector databases, embeddings, and semantic search mechanisms with OpenAI and Hugging Face- Context integration in adaptive UX, conversational interfaces, and cognitive automation via REST and WebSocket APIs- Contextual governance and compliance with auditing, telemetry, and semantic versioning policies- Monitoring and contextual performance metrics (precision, recall, coherence score, context drift detection)- Scalability, caching, and load-balancing strategies in distributed architectures (Kubernetes, Redis, Kafka, Docker)- Enterprise applications of Context Engineering in AWS, Azure, and Google Cloud environmentsBy the end, readers will gain conceptual and operational mastery to structure cognitive solutions capable of perceiving, interpreting, and acting based on contextual variables - unifying perception, reasoning, and action in highly responsive intelligent systems. context engineering, context-aware systems, langchain, semantic kernel, crewai, langgraph, faiss, pinecone, milvus, vector database, embeddings, rag, llm, openai, hugging face, ai pipelines, python, kubernetes, redis, kafka, docker, aws, azure, gcp, adaptive ux, contextual governance, telemetry, compliance, situational inference, cognitive automation, contextual ai, applied artificial intelligence This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Etat : New. Print on Demand.

Etat : New. Print on Demand.

Etat : New. Print on Demand.

Etat : New. Print on Demand.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Etat : new. Paperback. LEARN MLflow - Manage Machine Learning Pipelines and Models EfficientlyThis book offers a technical and practical approach for professionals looking to master MLflow - one of the leading platforms for managing the machine learning model lifecycle. The content covers everything from environment setup to production deployment, with a strong focus on reproducibility, versioning, tracking, and technical governance. Each chapter presents a key MLflow component (Tracking, Projects, Models, Model Registry) and demonstrates how to apply it in real-world scenarios, with clear examples, progressive structure, and established best practices.More than an introduction, this is a professional operations guide. Throughout the chapters, you will learn how to build auditable pipelines, automate CI/CD integrations, manage model versions in production, analyze metrics, and plan for scalability. Integrations with AutoML, Spark, cloud environments, security validation, access control, and post-deployment monitoring are also explored.The content was developed based on the TECHWRITE 2.2 protocol, ensuring immediate applicability in corporate and technical environments. Ideal for data engineers, data scientists, MLOps architects, and technical leaders seeking to raise the standard of model delivery in real-world environments.MLflow, MLOps, model management, experiment tracking, model deployment. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Etat : new. Paperback. Learn how to build Deep Learning models with Keras, the high-level library based onTensorFlow and the most widely used for neural networks in Python. This book offers a technical, straight-to-the-point guide that takes you from the fundamentals of neural networks to advanced applications with CNNs, RNNs, LSTM, Transfer Learning, NLP, and MLOps.You will learn how to set up the ideal development environment, build architectures using the sequential model and the functional API, tune hyperparameters, apply regularization, interpret networks with Grad-CAM, and monitor results with TensorBoard. Additionally, this manual details integration with libraries such as Pandas, Matplotlib, and Scikit-Learn, enabling complete workflows for modeling, evaluation, and deployment in production using APIs or serverless environments.The book covers essential topics such as training with GPUs and TPUs, tuning with Keras Tuner, using callbacks, best practices with TensorFlow, and modern strategies for model engineering. Each chapter follows the TECHWRITE 2.1 Protocol, featuring clear explanations, practical examples, commented common errors, applicable best practices, and a full focus on immediate applicability.Suitable for beginners in neural networks as well as professionals in data science, machine learning, and predictive automation, this book is designed to transform theory into real-world solutions, with a focus on technical clarity, progressive structure, and practical mastery of neural networks.APRENDA Keras is your definitive step to begin your journey toward mastering Deep Learning with efficiency, robustness, and professional purpose. Ideal for those who want to master artificial intelligence with depth and pragmatism. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New. Print on Demand.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.