Название: Learn Data Science Fundamentals: A Beginner's Guide To Data Science Programs, Analysis And Visualization
Автор: Declan Ashford
Издательство: Independently published
Год: 2024
Страниц: 246
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Are you eager to dive into the world of Data Science but unsure where to begin? Do you want to gain the skills to analyze and visualize data like a pro, even if you have no prior experience? Learn Data Science Fundamentals is your comprehensive guide to mastering the foundational concepts of Data Science, equipping you with the tools and techniques needed to thrive in today’s data-driven landscape. This book is designed to take you from a complete novice to a confident Data Science practitioner, guiding you through each step of the process with clear explanations and practical examples. Whether you’re looking to enhance your career, transition into a data-focused role, or simply satisfy your curiosity, this guide offers everything you need to get started. Understand the core tools and programming languages used in Data Science, including Python, R, SQL, and more. Learn how to set up your environment and get comfortable with the interfaces that professionals use every day. Go beyond the basics with techniques for transforming, aggregating, and merging datasets. Learn how to use libraries like Pandas and NumPy to perform complex operations with ease.

Название: Fault Diagnosis and Prognostics Based on Cognitive Computing and Geometric Space Transformation
Автор: Chen Lu, Laifa Tao, Jian Ma, Yujie Cheng, Yu Ding
Издательство: Springer
Год: 2024
Страниц: 565
Язык: английский
Формат: pdf (true)
Размер: 47.8 MB

This monograph introduces readers to new theories and methods applying cognitive computing and geometric space transformation to the field of fault diagnosis and prognostics. It summarizes the basic concepts and technical aspects of fault diagnosis and prognostics technology. Existing bottleneck problems are examined, and the advantages of applying cognitive computing and geometric space transformation are explained. In turn, the book highlights fault diagnosis, prognostic, and health assessment technologies based on cognitive computing methods, including Deep Learning (DL), Transfer Learning, visual cognition, and compressed sensing. Lastly, it covers technologies based on differential geometry, space transformation, and pattern recognition.

Название: Python Programming For Beginners - Practical Lessons for Building Real-World Applications
Автор: Anthony Joseph
Издательство: Independently published
Год: 2025
Страниц: 205
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.1 MB

This eBook, "Python Programming for Beginners - Practical Lessons for Building Real-World Applications", provides a comprehensive introduction to Python through ten structured chapters. It starts by explaining why Python is popular, guiding you through environment setup, and teaching essential syntax and data types. You then learn about more advanced subjects such as control flow, functions, and how to write clean, modular code. The book goes deeper into data structures, including dictionaries, lists, tuples, and sets, illustrating how each one is suited for different tasks. You also discover the fundamentals of object-oriented programming—classes, inheritance, and encapsulation—making it easier to design maintainable applications. The chapters on error handling show how to manage exceptions and create custom error types, ensuring your code remains robust. You learn how to work with files, directories, and popular libraries such as NumPy, Pandas, and Requests, exploring data manipulation and API integration. Finally, the eBook culminates in a real-world project, walking you through step-by-step project organization, core feature implementation, testing, and deployment strategies.

Название: Mathematics for Data Science: Linear Algebra with Matlab
Автор: César Pérez López
Издательство: Scientific Books
Год: 2025
Страниц: 447
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB

Mathematics is an essential toolset for data scientists. By mastering these mathematical concepts, data scientists can better understand the underlying mechanisms of Machine Learning algorithms, improve their ability to clean and manipulate data, and make more accurate predictions and decisions based on data. Matlab has functions that allow easy work in Linear Algebra. In this book, typical algebra topics are developed, such as work in discrete mathematics through numerical algebra in the real and complex fields. Work with algebraic expressions, polynomials, equations, systems of equations, matrices, vector spaces, linear maps, and quadratic forms is presented. Matrix algebra is specially developed with advanced treatment of eigenvalues, eigenvectors and diagonalization. He also delves into drawing curves and surfaces in explicit, implicit, parametric, and polar coordinates. The concepts are accompanied by examples solved step by step with Matlab.

Название: Introduction to Machine Learning with Security: Theory and Practice Using Python in the Cloud, 2nd Edition
Автор: Pramod Gupta, Naresh Kumar Sehgal, John M. Acken
Издательство: Springer
Год: 2025
Страниц: 509
Язык: английский
Формат: pdf (true)
Размер: 16.6 MB

This book provides an introduction to Machine Learning, security and cloud computing, from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and case studies. The authors of this book have leveraged their hands-on experience with solving real-world problems using Python and the Machine Learning ecosystem to help the readers gain solid knowledge needed to apply essential concepts, methodologies, tools, and techniques for solving their own real-world problems. The book aims to cater to readers with varying skill levels ranging from beginners to experts and enable them in structuring and building practical Machine Learning and AI solutions. This book is appropriate for both advanced undergraduate or master’s students who want to work in this domain, or for individuals working in the area of Machine Learning.

Название: Health Analytics with R: Learning Data Science Using Examples from Healthcare and Direct-to-Consumer Genetics
Автор: Mary Regina Boland
Издательство: Springer
Год: 2025
Страниц: 663
Язык: английский
Формат: pdf (true)
Размер: 101.3 MB

This textbook teaches health analytics using examples from the statistical programming language R. It utilizes real-world examples with publicly available datasets from healthcare and direct-to-consumer genetics to provide learners with real-world examples and enable them to get their hands on actual data. This textbook is designed to accompany either a senior-level undergraduate course or a Masters level graduate course on health analytics. The reader will advance from no prior knowledge of R to being well versed in applications within R that apply to Data Science and health analytics.

Название: Visualization for Artificial Intelligence
Автор: Shixia Liu, Weikai Yang, Junpeng Wang, Jun Yuan
Издательство: Springer
Год: 2025
Страниц: 147
Язык: английский
Формат: pdf (true)
Размер: 11.5 MB

This book explores how visualization provides an effective way of improving not only the interpretability but also the generalization capabilities of Machine Learning models. It shows how visualization can bridge the gap between complex models or algorithms and human understanding while also facilitating data curation and model refinement. Therefore, visualization for Artificial Intelligence (VIS4AI) has become an emerging area that combines interactive visualization with Machine Learning techniques to maximize their values. VIS4AI techniques focus on every phase of the Machine Learning life cycle, from data preprocessing to model development and deployment. These techniques are closely aligned with the well-established data and model pipelines in Machine Learning. In the data pipeline, they contribute to improving data quality and feature quality, including training data cleaning and feature engineering. In the model pipeline, they support (1) model development by focusing on model understanding, diagnosis, and steering; and (2) model deployment by enabling decision explanation, model performance monitoring, and model maintenance.

Название: The Science of Machine Learning, Part 1
Автор: Shubhodip Sasmal, Pushpa Raikwar Diwan, Sheetal Temara, Shahrukh Irfan
Издательство: AMKCORP Academics
Год: 2024
Страниц: 212
Язык: английский
Формат: pdf
Размер: 30.7 MB

The book titled “The Science of Machine Learning Part 1” serves as a comprehensive guide for both beginners and experienced practitioners in the field of Machine Learning. Covering a wide range of topics, the book provides a thorough introduction to the fundamentals of Machine Learning, as well as advanced techniques and emerging trends. In the first chapter, readers are introduced to the concept of Machine Learning, its historical context, and its importance and applications in various domains. The chapter also explores different types of Machine Learning and addresses key terminologies, challenges, and limitations associated with the field. The second chapter delves into the mathematical foundations essential for understanding Machine Learning algorithms. Topics covered include linear algebra, probability and statistics, calculus, optimization techniques, information theory basics, and numerical methods and algorithms.

Название: Deep Learning Through the Prism of Tensors
Автор: Pradeep Singh, Balasubramanian Raman
Издательство: Springer
Год: 2025
Страниц: 627
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

In the rapidly evolving field of Artificial Intelligence (AI), this book serves as a crucial resource for understanding the mathematical foundations of AI. It explores the intricate world of tensors, the fundamental elements powering today's advanced Deep Learning models. Combining theoretical depth with practical insights, the text navigates the complex landscape of tensor calculus, guiding readers to master the principles and applications of tensors in AI. From the basics of tensor algebra and geometry to the sophisticated architectures of neural networks, including multi-layer perceptrons, convolutional, recurrent, and transformer models, this book provides a comprehensive examination of the mechanisms driving modern AI innovations. It delves into the specifics of autoencoders, generative models, and geometric interpretations, offering a fresh perspective on the complex, high-dimensional spaces traversed by Deep Learning technologies. This book not only covers the theoretical underpinnings but also showcases practical implementations using popular frameworks such as TensorFlow and PyTorch.

Название: The Definitive Guide to Machine Learning Operations in AWS: Machine Learning Scalability and Optimization with AWS
Автор: Neel Sendas, Deepali Rajale
Издательство: Apress
Год: 2025
Страниц: 432
Язык: английский
Формат: pdf
Размер: 15.1 MB

This book focuses on deploying, testing, monitoring, and automating ML systems in production. It covers AWS MLOPS tools like Amazon SageMaker, Data Wrangler, and AWS Feature Store, along with best practices for operating ML systems on AWS. This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected pillars. It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. Performance efficiency and cost optimization, like Right-sizing ML resources, are explored. The book concludes with MLOps best practices, MLOPS for GenAI, emerging trends, and future developments in MLOps. This book suits ML engineers, DevOps engineers, software developers, architects, and team leaders aspiring to be MLOps professionals on AWS.