Aranha vivian (36 resultados)

Paperback or Softback. Condición: New. Designing NVIDIA AI Infrastructure: GPU compute, networking, orchestration, and security in NVIDIA's stack, explained. Book.

Paperback or Softback. Condición: New. Hands-On AI Development with Python: Build and Deploy Real-World AI, Machine Learning, Deep Learning, and NLP Applications. Book.

Paperback or Softback. Condición: New. NVIDIA GPU Infrastructure Fundamentals: A structured guide to NVIDIA GPU infrastructure, from CUDA to production operations. Book.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Taschenbuch. Condición: Neu. Neuware - Build practical AI and machine learning skills from Python, NumPy, Pandas, mathematics, and statistics to XGBoost, deep learning, computer vision, NLP, transformers, and fine-tuning, reinforced through hands-on projects and model evaluation.

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

Paperback. Condición: new. Paperback. Build real-world AI projects with Python, pandas, NumPy, Matplotlib, Seaborn, scikit-learn, NLP, neural networks, sentiment analysis, and web deployment as you move from first code to portfolio-ready AI apps and skills.Key FeaturesBuild portfolio-ready AI projects with Python, data analysis, ML, NLP, and deploymentUse pandas, NumPy, Matplotlib, Seaborn, scikit-learn, and neural networks on real datasetsMove from zero programming to practical AI workflows through guided, hands-on projectsBook DescriptionMany beginners learn Python syntax or AI theory but struggle to build projects they can explain, demonstrate, and add to a portfolio. This book closes that gap by turning AI fundamentals into practical Python projects that move from first code to working AI deployment.You will begin with Python setup and the foundations needed for AI development, including variables, data types, functions, control flow, and libraries. You will then use NumPy and pandas to load, clean, transform, and inspect datasets, before applying EDA with Matplotlib and Seaborn to uncover patterns, relationships, and missing values. With these foundations in place, you will build machine learning models using scikit-learn. You will work through prediction and classification workflows, prepare features, train models, evaluate results, and understand how choices affect accuracy and usefulness. The book introduces neural networks in a beginner-friendly way, showing how layers, training, and performance connect in applied AI work.You will create an NLP sentiment analysis project, turning text into features and classifying opinions. Finally, you will package a trained model as a web service used beyond a notebook. By the end, you will have a practical AI portfolio and a strong foundation for machine learning, data science, and applied AI development.What you will learnSet up Python for hands-on AI and machine learning projectsUse core syntax, data types, control flow, functions, and libraries for AI workClean, transform, analyze, and visualize data with NumPy, pandas, Matplotlib, and SeabornApply EDA to find patterns, missing values, relationships, and featuresBuild prediction and classification models using scikit-learn workflowsUnderstand neural network basics and practical deep learning conceptsCreate an NLP sentiment analysis model from text dataDeploy a trained AI model as a web serviceWho this book is forThis book is for absolute beginners, students, career changers, junior developers, analysts, data enthusiasts, and hobbyists who want a practical entry point into AI development. It is useful for readers building their first AI portfolio, exploring machine learning or data science roles, or learning how trained models become simple applications. No prior programming or AI experience is required. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Navigate NVIDIA's enterprise AI infrastructure with confidence, from GPUs and data movement to orchestration, security, monitoring, edge systems, and model serving.Key FeaturesBuild career-relevant knowledge of the NVIDIA AI infrastructure stackMake informed architecture decisions for performance, scalability, security, and costLearn through practical configurations, deployment patterns, and enterprise case studiesBook DescriptionDesigning NVIDIA AI Infrastructure is a concise reference guide for professionals who want to develop career-relevant knowledge of GPU-powered platforms without working through a lengthy manual.The book explains how CPUs, GPUs, DPUs, storage, networking, software, and orchestration combine to support AI workloads. You will explore MIG and vGPU resource models, Kubernetes and Slurm scheduling, data pipelines, performance profiling, monitoring, TensorRT optimization, multi-tenant security, and governance. You will also learn how NVIDIA Jetson and Orin support edge AI and how NGC and Triton Inference Server contribute to model deployment and scalable serving.Selected commands, configuration examples, architecture diagrams, and enterprise scenarios connect these technologies to operational contexts. By the end, you will be able to discuss the NVIDIA AI infrastructure stack with greater confidence, evaluate common design choices and bottlenecks, and use the book as a quick reference when planning cloud, on-premises, hybrid, and edge AI environments.What you will learnUnderstand what MIG and vGPU isolate and what they don'tDistinguish RBAC, network policy, and encryption's separate rolesSee how storage, NVLink, and InfiniBand affect GPU utilizationRecognize where Kubernetes tools' responsibilities stopUnderstand how GDPR, HIPAA, and FedRAMP shape AI infrastructure controls and evidenceUse GPU profiling and telemetry data to investigate bottlenecksLearn how NGC, Triton, and ensembles fit a serving pipelineCompare on-prem, cloud, and hybrid AI cluster trade-offsWho this book is forThis book is for infrastructure engineers, ML and MLOps engineers, solutions architects, and technical leads who need a reliable mental model of NVIDIAs AI infrastructure stack before designing, evaluating, or securing a GPU platform. It also suits professionals moving into AI infrastructure roles. Familiarity with Linux, containers, networking, cloud computing, or Kubernetes is helpful; advanced model-development knowledge and access to enterprise GPU hardware are not required. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. Decode the NVIDIA GPU ecosystem in one structured guide. Compare technologies, understand how platform layers interact, and build the judgment to evaluate infrastructure choices and trade-offs.Key FeaturesUnderstand how GPUs, CUDA, networking, storage, and DPUs support AI workloadsLearn the roles of MIG, vGPU, DCGM, Kubernetes, Slurm, NGC, and TritonConnect infrastructure components across the AI development and deployment lifecycleBook DescriptionNVIDIA GPU infrastructure spans hardware, system software, networking, storage, orchestration, MLOps, and inference. Understanding how these components fit together, where their responsibilities overlap, and which distinctions matter requires a clear, structured path.This book provides that path through one coherent narrative of the NVIDIA GPU infrastructure stack. It covers accelerated computing, CUDA, and the NVIDIA software ecosystem before comparing data center GPUs against workload characteristics. You will examine MIG, vGPU, and DCGM for resource sharing and monitoring; Kubernetes and Slurm for GPU scheduling; and the networking and storage layer, including Ethernet, InfiniBand, RDMA, GPUDirect Storage, BlueField DPUs, and DOCA.Later chapters connect infrastructure to the AI lifecycle through Airflow, MLflow, and Kubeflow for MLOps, NGC for software delivery, and ONNX, TensorRT, and Triton for inference. You will also explore the Kubernetes components, monitoring technologies, scaling considerations, and diagnostic concepts that support production GPU clusters.By connecting these technologies instead of presenting them as isolated products, the book helps you compare platform choices, understand component boundaries, discuss trade-offs, and develop a durable mental model of NVIDIA GPU infrastructure.What you will learnDistinguish AI, machine learning, and deep learningExplain why GPUs accelerate modern AI workloadsMatch NVIDIA GPUs to training and inference requirementsSelect MIG or vGPU for common resource-sharing scenariosCompare Ethernet and InfiniBand for distributed AI workloadsMap MLOps tools to the right stage of the AI lifecycleDifferentiate ONNX, TensorRT, and Triton in inference workflowsTrace GPU cluster issues across platform layersWho this book is forThis book is for system administrators, cloud and DevOps professionals, data center and networking teams, solution architects, technical managers, presales professionals, and beginners who need a clear understanding of NVIDIA GPU infrastructure. It is especially relevant to professionals moving into AI infrastructure roles, evaluating GPU platform technologies, or collaborating across compute, networking, MLOps, and operations teams. Basic familiarity with IT, cloud, or data center concepts is helpful; programming, data science, and previous GPU experience are not required. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. Learn AI, machine learning, and deep learning with Python from the ground up, then build and tune models with XGBoost, TensorFlow, PyTorch, CNNs, RNNs, transformers, and transfer learning through practical Python projects.Key FeaturesBuild Python, data science, mathematics, and statistics foundations for machine learningTrain, evaluate, and tune regression, classification, ensemble, XGBoost, LightGBM, and CatBoost modelsCreate CNN, RNN, transformer, and transfer learning projects with TensorFlow, Keras, and PyTorchBook DescriptionLearning modern AI often feels fragmented: Python in one resource, mathematics in another, and deep learning or transformers somewhere else. This book brings those foundations into one practical progression, helping you connect the concepts behind machine learning with the code used to build models.You will start with Python programming, NumPy, Pandas, data visualization, exploratory data analysis, linear algebra, calculus, probability, and statistics. From there, you will build supervised learning skills across regression, classification, k-NN, feature engineering, model evaluation, cross-validation, and hyperparameter tuning. You will then work with ensemble methods including random forests, gradient boosting, XGBoost, LightGBM, and CatBoost, while learning to handle imbalanced data and justify modeling decisions.The final part moves into neural networks and deep learning with TensorFlow, Keras, and PyTorch. You will build CNNs for image classification, explore RNNs, LSTMs, GRUs, word embeddings, and sequence-to-sequence models, and understand attention and transformer architectures through BERT and GPT examples. You will finish by applying transfer learning and fine-tuning to computer vision and NLP tasks, supported by projects that turn theory into practical AI and machine learning skills for real-world problem solving.What you will learnUse Python, NumPy, Pandas, Matplotlib, and Seaborn for data analysisApply linear algebra, calculus, probability, and statistics to MLBuild regression, classification, and k-NN modelsEngineer features and evaluate models with cross-validation and hyperparameter tuningTrain ensembles with random forests, XGBoost, LightGBM, and CatBoostBuild neural networks and CNNs with TensorFlow, Keras, and PyTorchDevelop RNNs, LSTMs, GRUs, attention, BERT, and GPT workflows for NLPApply transfer learning and fine-tuning to computer vision and NLPWho this book is forThis book is for aspiring data scientists, machine learning engineers, AI developers, students, and career changers who want one structured path from Python fundamentals to machine learning and deep learning. It suits readers seeking practical skills in data analysis, predictive modeling, computer vision, NLP, transformers, and transfer learning. No prior machine learning or deep learning experience is required. Python is introduced from the fundamentals, although basic programming familiarity and foundational mathematics will help. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

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

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Paperback. Condición: new. Paperback. Navigate NVIDIA's enterprise AI infrastructure with confidence, from GPUs and data movement to orchestration, security, monitoring, edge systems, and model serving.Key FeaturesBuild career-relevant knowledge of the NVIDIA AI infrastructure stackMake informed architecture decisions for performance, scalability, security, and costLearn through practical configurations, deployment patterns, and enterprise case studiesBook DescriptionDesigning NVIDIA AI Infrastructure is a concise reference guide for professionals who want to develop career-relevant knowledge of GPU-powered platforms without working through a lengthy manual.The book explains how CPUs, GPUs, DPUs, storage, networking, software, and orchestration combine to support AI workloads. You will explore MIG and vGPU resource models, Kubernetes and Slurm scheduling, data pipelines, performance profiling, monitoring, TensorRT optimization, multi-tenant security, and governance. You will also learn how NVIDIA Jetson and Orin support edge AI and how NGC and Triton Inference Server contribute to model deployment and scalable serving.Selected commands, configuration examples, architecture diagrams, and enterprise scenarios connect these technologies to operational contexts. By the end, you will be able to discuss the NVIDIA AI infrastructure stack with greater confidence, evaluate common design choices and bottlenecks, and use the book as a quick reference when planning cloud, on-premises, hybrid, and edge AI environments.What you will learnUnderstand what MIG and vGPU isolate and what they don'tDistinguish RBAC, network policy, and encryption's separate rolesSee how storage, NVLink, and InfiniBand affect GPU utilizationRecognize where Kubernetes tools' responsibilities stopUnderstand how GDPR, HIPAA, and FedRAMP shape AI infrastructure controls and evidenceUse GPU profiling and telemetry data to investigate bottlenecksLearn how NGC, Triton, and ensembles fit a serving pipelineCompare on-prem, cloud, and hybrid AI cluster trade-offsWho this book is forThis book is for infrastructure engineers, ML and MLOps engineers, solutions architects, and technical leads who need a reliable mental model of NVIDIAs AI infrastructure stack before designing, evaluating, or securing a GPU platform. It also suits professionals moving into AI infrastructure roles. Familiarity with Linux, containers, networking, cloud computing, or Kubernetes is helpful; advanced model-development knowledge and access to enterprise GPU hardware are not required. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. Build real-world AI projects with Python, pandas, NumPy, Matplotlib, Seaborn, scikit-learn, NLP, neural networks, sentiment analysis, and web deployment as you move from first code to portfolio-ready AI apps and skills.Key FeaturesBuild portfolio-ready AI projects with Python, data analysis, ML, NLP, and deploymentUse pandas, NumPy, Matplotlib, Seaborn, scikit-learn, and neural networks on real datasetsMove from zero programming to practical AI workflows through guided, hands-on projectsBook DescriptionMany beginners learn Python syntax or AI theory but struggle to build projects they can explain, demonstrate, and add to a portfolio. This book closes that gap by turning AI fundamentals into practical Python projects that move from first code to working AI deployment.You will begin with Python setup and the foundations needed for AI development, including variables, data types, functions, control flow, and libraries. You will then use NumPy and pandas to load, clean, transform, and inspect datasets, before applying EDA with Matplotlib and Seaborn to uncover patterns, relationships, and missing values. With these foundations in place, you will build machine learning models using scikit-learn. You will work through prediction and classification workflows, prepare features, train models, evaluate results, and understand how choices affect accuracy and usefulness. The book introduces neural networks in a beginner-friendly way, showing how layers, training, and performance connect in applied AI work.You will create an NLP sentiment analysis project, turning text into features and classifying opinions. Finally, you will package a trained model as a web service used beyond a notebook. By the end, you will have a practical AI portfolio and a strong foundation for machine learning, data science, and applied AI development.What you will learnSet up Python for hands-on AI and machine learning projectsUse core syntax, data types, control flow, functions, and libraries for AI workClean, transform, analyze, and visualize data with NumPy, pandas, Matplotlib, and SeabornApply EDA to find patterns, missing values, relationships, and featuresBuild prediction and classification models using scikit-learn workflowsUnderstand neural network basics and practical deep learning conceptsCreate an NLP sentiment analysis model from text dataDeploy a trained AI model as a web serviceWho this book is forThis book is for absolute beginners, students, career changers, junior developers, analysts, data enthusiasts, and hobbyists who want a practical entry point into AI development. It is useful for readers building their first AI portfolio, exploring machine learning or data science roles, or learning how trained models become simple applications. No prior programming or AI experience is required. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A structural guide to NVIDIA's AI infrastructure stack-GPU sharing, networking, orchestration, security, and edge deployment for engineers who need an accurate mental model before making architecture decisions or starting hands-on work.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build practical AI projects from scratch with Python, covering setup, core syntax, NumPy, pandas, EDA, visualization, machine learning, classification, neural networks, NLP sentiment analysis, and deploying a trained model as a web service.…

Paperback. Condición: new. Paperback. Decode the NVIDIA GPU ecosystem in one structured guide. Compare technologies, understand how platform layers interact, and build the judgment to evaluate infrastructure choices and trade-offs.Key FeaturesUnderstand how GPUs, CUDA, networking, storage, and DPUs support AI workloadsLearn the roles of MIG, vGPU, DCGM, Kubernetes, Slurm, NGC, and TritonConnect infrastructure components across the AI development and deployment lifecycleBook DescriptionNVIDIA GPU infrastructure spans hardware, system software, networking, storage, orchestration, MLOps, and inference. Understanding how these components fit together, where their responsibilities overlap, and which distinctions matter requires a clear, structured path.This book provides that path through one coherent narrative of the NVIDIA GPU infrastructure stack. It covers accelerated computing, CUDA, and the NVIDIA software ecosystem before comparing data center GPUs against workload characteristics. You will examine MIG, vGPU, and DCGM for resource sharing and monitoring; Kubernetes and Slurm for GPU scheduling; and the networking and storage layer, including Ethernet, InfiniBand, RDMA, GPUDirect Storage, BlueField DPUs, and DOCA.Later chapters connect infrastructure to the AI lifecycle through Airflow, MLflow, and Kubeflow for MLOps, NGC for software delivery, and ONNX, TensorRT, and Triton for inference. You will also explore the Kubernetes components, monitoring technologies, scaling considerations, and diagnostic concepts that support production GPU clusters.By connecting these technologies instead of presenting them as isolated products, the book helps you compare platform choices, understand component boundaries, discuss trade-offs, and develop a durable mental model of NVIDIA GPU infrastructure.What you will learnDistinguish AI, machine learning, and deep learningExplain why GPUs accelerate modern AI workloadsMatch NVIDIA GPUs to training and inference requirementsSelect MIG or vGPU for common resource-sharing scenariosCompare Ethernet and InfiniBand for distributed AI workloadsMap MLOps tools to the right stage of the AI lifecycleDifferentiate ONNX, TensorRT, and Triton in inference workflowsTrace GPU cluster issues across platform layersWho this book is forThis book is for system administrators, cloud and DevOps professionals, data center and networking teams, solution architects, technical managers, presales professionals, and beginners who need a clear understanding of NVIDIA GPU infrastructure. It is especially relevant to professionals moving into AI infrastructure roles, evaluating GPU platform technologies, or collaborating across compute, networking, MLOps, and operations teams. Basic familiarity with IT, cloud, or data center concepts is helpful; programming, data science, and previous GPU experience are not required. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. Navigate NVIDIA's enterprise AI infrastructure with confidence, from GPUs and data movement to orchestration, security, monitoring, edge systems, and model serving.Key FeaturesBuild career-relevant knowledge of the NVIDIA AI infrastructure stackMake informed architecture decisions for performance, scalability, security, and costLearn through practical configurations, deployment patterns, and enterprise case studiesBook DescriptionDesigning NVIDIA AI Infrastructure is a concise reference guide for professionals who want to develop career-relevant knowledge of GPU-powered platforms without working through a lengthy manual.The book explains how CPUs, GPUs, DPUs, storage, networking, software, and orchestration combine to support AI workloads. You will explore MIG and vGPU resource models, Kubernetes and Slurm scheduling, data pipelines, performance profiling, monitoring, TensorRT optimization, multi-tenant security, and governance. You will also learn how NVIDIA Jetson and Orin support edge AI and how NGC and Triton Inference Server contribute to model deployment and scalable serving.Selected commands, configuration examples, architecture diagrams, and enterprise scenarios connect these technologies to operational contexts. By the end, you will be able to discuss the NVIDIA AI infrastructure stack with greater confidence, evaluate common design choices and bottlenecks, and use the book as a quick reference when planning cloud, on-premises, hybrid, and edge AI environments.What you will learnUnderstand what MIG and vGPU isolate and what they don'tDistinguish RBAC, network policy, and encryption's separate rolesSee how storage, NVLink, and InfiniBand affect GPU utilizationRecognize where Kubernetes tools' responsibilities stopUnderstand how GDPR, HIPAA, and FedRAMP shape AI infrastructure controls and evidenceUse GPU profiling and telemetry data to investigate bottlenecksLearn how NGC, Triton, and ensembles fit a serving pipelineCompare on-prem, cloud, and hybrid AI cluster trade-offsWho this book is forThis book is for infrastructure engineers, ML and MLOps engineers, solutions architects, and technical leads who need a reliable mental model of NVIDIAs AI infrastructure stack before designing, evaluating, or securing a GPU platform. It also suits professionals moving into AI infrastructure roles. Familiarity with Linux, containers, networking, cloud computing, or Kubernetes is helpful; advanced model-development knowledge and access to enterprise GPU hardware are not required. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condición: new. Paperback. Build real-world AI projects with Python, pandas, NumPy, Matplotlib, Seaborn, scikit-learn, NLP, neural networks, sentiment analysis, and web deployment as you move from first code to portfolio-ready AI apps and skills.Key FeaturesBuild portfolio-ready AI projects with Python, data analysis, ML, NLP, and deploymentUse pandas, NumPy, Matplotlib, Seaborn, scikit-learn, and neural networks on real datasetsMove from zero programming to practical AI workflows through guided, hands-on projectsBook DescriptionMany beginners learn Python syntax or AI theory but struggle to build projects they can explain, demonstrate, and add to a portfolio. This book closes that gap by turning AI fundamentals into practical Python projects that move from first code to working AI deployment.You will begin with Python setup and the foundations needed for AI development, including variables, data types, functions, control flow, and libraries. You will then use NumPy and pandas to load, clean, transform, and inspect datasets, before applying EDA with Matplotlib and Seaborn to uncover patterns, relationships, and missing values. With these foundations in place, you will build machine learning models using scikit-learn. You will work through prediction and classification workflows, prepare features, train models, evaluate results, and understand how choices affect accuracy and usefulness. The book introduces neural networks in a beginner-friendly way, showing how layers, training, and performance connect in applied AI work.You will create an NLP sentiment analysis project, turning text into features and classifying opinions. Finally, you will package a trained model as a web service used beyond a notebook. By the end, you will have a practical AI portfolio and a strong foundation for machine learning, data science, and applied AI development.What you will learnSet up Python for hands-on AI and machine learning projectsUse core syntax, data types, control flow, functions, and libraries for AI workClean, transform, analyze, and visualize data with NumPy, pandas, Matplotlib, and SeabornApply EDA to find patterns, missing values, relationships, and featuresBuild prediction and classification models using scikit-learn workflowsUnderstand neural network basics and practical deep learning conceptsCreate an NLP sentiment analysis model from text dataDeploy a trained AI model as a web serviceWho this book is forThis book is for absolute beginners, students, career changers, junior developers, analysts, data enthusiasts, and hobbyists who want a practical entry point into AI development. It is useful for readers building their first AI portfolio, exploring machine learning or data science roles, or learning how trained models become simple applications. No prior programming or AI experience is required. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condición: new. Paperback. Learn AI, machine learning, and deep learning with Python from the ground up, then build and tune models with XGBoost, TensorFlow, PyTorch, CNNs, RNNs, transformers, and transfer learning through practical Python projects.Key FeaturesBuild Python, data science, mathematics, and statistics foundations for machine learningTrain, evaluate, and tune regression, classification, ensemble, XGBoost, LightGBM, and CatBoost modelsCreate CNN, RNN, transformer, and transfer learning projects with TensorFlow, Keras, and PyTorchBook DescriptionLearning modern AI often feels fragmented: Python in one resource, mathematics in another, and deep learning or transformers somewhere else. This book brings those foundations into one practical progression, helping you connect the concepts behind machine learning with the code used to build models.You will start with Python programming, NumPy, Pandas, data visualization, exploratory data analysis, linear algebra, calculus, probability, and statistics. From there, you will build supervised learning skills across regression, classification, k-NN, feature engineering, model evaluation, cross-validation, and hyperparameter tuning. You will then work with ensemble methods including random forests, gradient boosting, XGBoost, LightGBM, and CatBoost, while learning to handle imbalanced data and justify modeling decisions.The final part moves into neural networks and deep learning with TensorFlow, Keras, and PyTorch. You will build CNNs for image classification, explore RNNs, LSTMs, GRUs, word embeddings, and sequence-to-sequence models, and understand attention and transformer architectures through BERT and GPT examples. You will finish by applying transfer learning and fine-tuning to computer vision and NLP tasks, supported by projects that turn theory into practical AI and machine learning skills for real-world problem solving.What you will learnUse Python, NumPy, Pandas, Matplotlib, and Seaborn for data analysisApply linear algebra, calculus, probability, and statistics to MLBuild regression, classification, and k-NN modelsEngineer features and evaluate models with cross-validation and hyperparameter tuningTrain ensembles with random forests, XGBoost, LightGBM, and CatBoostBuild neural networks and CNNs with TensorFlow, Keras, and PyTorchDevelop RNNs, LSTMs, GRUs, attention, BERT, and GPT workflows for NLPApply transfer learning and fine-tuning to computer vision and NLPWho this book is forThis book is for aspiring data scientists, machine learning engineers, AI developers, students, and career changers who want one structured path from Python fundamentals to machine learning and deep learning. It suits readers seeking practical skills in data analysis, predictive modeling, computer vision, NLP, transformers, and transfer learning. No prior machine learning or deep learning experience is required. Python is introduced from the fundamentals, although basic programming familiarity and foundational mathematics will help. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …