Название: Machine Learning and Data Mining Annual Volume 2023
Автор: Marco Antonio Aceves-Fernández, Andries Engelbrecht
Издательство: ITexLi
Год: 2023
Страниц: 132
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book offers a multifaceted perspective on Machine Learning and Data Mining. Whether you’re an experienced researcher or a newcomer, this collection is an essential resource for staying at the forefront of these dynamic and influential disciplines. The interest within the academic community regarding AI has experienced exponential growth in recent years. Several key factors have contributed to this surge in interest. Firstly, the rapid advancements in AI technologies have showcased their potential to revolutionize various fields, such as healthcare, finance, and transportation, sparking curiosity and enthusiasm among researchers and scholars. Secondly, the availability of vast amounts of data and computing power has enabled academics to delve deeper into AI research, exploring complex algorithms and models to tackle real-world problems. Additionally, the interdisciplinary nature of AI has encouraged collaboration among experts from diverse fields like Computer Science, neuroscience, psychology, and ethics, fostering a rich exchange of ideas and approaches.

Название: Unsupervised Machine Learning: with Python
Автор: Науdеn Vаn Dеr Роst, Мikе Smith
Издательство: Reactive Publishing
Год: December 28, 2023
Страниц: 371
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Dive into the world of Artificial Intelligence with "Unsupervised Machine Learning with Python," the essential guide forprofessionals eager to master the most sophisticated analysis skills and unlock new dimensions of data interpretation. Building on the knowledge foundation of those who have already ventured into the realm of supervised Machine Learning, this book takes you one step further into the nuanced techniques that are shaping the future of AI. As a follow-up to our top-selling predecessor, this in-depth resource is perfectly tailored for analysts, data scientists, and curious minds looking to leverage Python for advanced Machine Learning tasks. With a clear, practical approach, it demystifies the complex algorithms and models that underpin unsupervised learning frameworks. Unsupervised learning, a pivotal branch of machine intelligence, thrives on the premise that even in the absence of explicit instructions, profound insights can be extracted from raw data. At the heart of unsupervised learning lies the ability of algorithms to identify patterns and structures within datasets without prior labeling or classification, a task emblematic of human cognitive adaptability. Python, the lingua franca of machine learning, offers a versatile toolkit for implementing unsupervised learning models.

Название: Data Science and Machine Learning for Non-Programmers: Using SAS Enterprise Miner
Автор: Dоthаng Тruоng
Издательство: CRC Press
Год: 2024
Страниц: 590
Язык: английский
Формат: pdf (true)
Размер: 35.9 MB

As data continues to grow exponentially, knowledge of Data Science and Machine Learning has become more crucial than ever. Machine Learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize Machine Learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. The book begins with Part I, introducing the core concepts of data science, data mining, and Machine Learning. My aim is to present these principles without overwhelming readers with complex math, empowering them to comprehend the underlying mechanisms of various algorithms and models. This foundational knowledge will enable readers to make informed choices when selecting the right tool for specific problems. In Part II, I focus on the most popular Machine Learning algorithms, including regression methods, decision trees, neural networks, ensemble modeling, principal component analysis, and cluster analysis.

Название: Image Processing and Machine Learning, Volume 1: Foundations of Image Processing
Автор: Еrik Сuеvаs, Аlmа Nауеli Rоdríguеz
Издательство: CRC Press
Год: 2024
Страниц: 225
Язык: английский
Формат: pdf (true)
Размер: 40.9 MB

Image processing and Machine Learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, Machine Learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches. Divided into two volumes, this first installment explores the fundamental concepts and techniques in image processing, starting with pixel operations and their properties and exploring spatial filtering, edge detection, image segmentation, corner detection, and geometric transformations. Our primary objective was to create a comprehensive textbook that serves as an invaluable resource for an image processing class. With this goal in mind, we carefully crafted a book that encompasses both the theoretical foundations and practical applications of the most prevalent image processing methods. From pixel operations to geometric transformations, spatial filtering to image segmentation, and edge detection to color image processing, we have meticulously covered a wide range of topics essential to understanding and working with images. Moreover, recognizing the increasing relevance of ML in image processing, we have incorporated fundamental ML concepts and their applications in this field. By introducing readers to these concepts, we aim to equip them with the necessary knowledge to leverage ML techniques for various image processing tasks. Volume 1 is organized in a way that allows readers to easily understand the goal of each chapter and reinforce their understanding through practical exercises using MATLAB programs.

Название: Image Processing and Machine Learning, Volume 2: Advanced Topics in Image Analysis and Machine Learning
Автор: Еrik Сuеvаs, Аlmа Nауеli Rоdríguеz
Издательство: CRC Press
Год: 2024
Страниц: 239
Язык: английский
Формат: pdf (true)
Размер: 31.6 MB

Image processing and Machine Learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, Machine Learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches. Divided into two volumes, this second installment explores the more advanced concepts and techniques in image processing, including morphological filters, color image processing, image matching, feature-based segmentation utilizing the mean shift algorithm, and the application of singular value decomposition for image compression. This second volume also incorporates several important Machine Learning techniques applied to image processing, building on the foundational knowledge introduced in Volume 1. Machine Learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn from data and make informed predictions or decisions without the need for explicit programming. ML finds extensive applications in various domains. For instance, in automation, ML algorithms can automate tasks that would otherwise rely on human intervention, thereby reducing errors and enhancing overall efficiency. Predictive analytics is another area where ML plays a crucial role. By analyzing vast datasets, ML models can detect patterns and make predictions, facilitating applications such as stock market analysis, fraud detection, and customer behavior analysis. We have observed that students grasp the material more effectively when they have access to code that they can manipulate and experiment with. In line with this, our book utilizes MATLAB as the programming language for implementing the systems.

Название: Quantum Machine Learning: Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing
Автор: Сlаudiо Соnti
Издательство: Springer
Год: 2024
Страниц: 393
Язык: английский
Формат: pdf (true)
Размер: 14.6 MB

This book presents a new way of thinking about quantum mechanics and Machine Learning by merging the two. Quantum mechanics and Machine Learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and Machine Learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits’ performance. The book begins with the introduction of programming tools and basic concepts of Machine Learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs.

Название: .NET Core For Machine Learning: Build Smart, Fast, And Reliable Solutions
Автор: Науdеn Vаn Dеr Роst, Мikе Smith
Издательство: Reactive Publishing
Год: 2023
Страниц: 361
Язык: английский
Формат: pdf
Размер: 32.1 MB

.NET Core For Machine Learning" is an advanced guide designed for professionals seeking to enhance their expertise in Data Science and Machine Learning using the capabilities of .NET Core 3.1. This cutting-edge resource provides valuable insights and techniques to bolster analytical skills and leverage the robust features of .NET Core 3.1 in the field of Machine Learning. As you dive deeper into this comprehensive book, you will discover a treasure trove of sophisticated techniques to elevate your expertise. It is specifically crafted for readers who were captivated by the top-selling predecessors but are now craving more complex challenges and solutions. With each chapter, you are presented with a rich array of real-world projects, designed to push the boundaries of your knowledge and skills. Implement smart algorithms, build fast and reliable data processing pipelines, and create interactive Machine Learning models that can seamlessly scale to meet the demanding needs of today's data-driven industry. ".NET Core For Machine Learning" is not just a technical manual; it is your gateway to mastering the art of Machine Learning and transforming data into actionable knowledge. Whether you're looking to revolutionize your business strategy or innovate within your technical team, this book will prove an invaluable asset on your quest for excellence. Your journey towards becoming a Data Science maestro begins here.

Название: Machine Learning and Optimization for Engineering Design
Автор: Арооrvа S. Shаstri, Каilаsh Shаw, Маngаl Singh
Издательство: Springer
Год: 2023
Страниц: 175
Язык: английский
Формат: pdf (true), epub
Размер: 35.5 MB

This book aims to provide a collection of state-of-the-art scientific and technical research papers related to Machine Learning-based algorithms in the field of optimization and engineering design. The theoretical and practical development for numerous engineering applications such as smart homes, ICT-based irrigation systems, academic success prediction, future agro-industry for crop production, disease classification in plants, dental problems and solutions, loan eligibility processing, etc., and their implementation with several case studies and literature reviews are included as self-contained chapters. Additionally, the book intends to highlight the importance of study and effectiveness in addressing the time and space complexity of problems and enhancing accuracy, analysis, and validations for different practical applications by acknowledging the state-of-the-art literature survey. The book targets a larger audience by exploring multidisciplinary research directions such as computer vision, Machine Learning, Artificial Intelligence, modified/newly developed Machine Learning algorithms, etc., to enhance engineering design applications for society. State-of-the-art research work with illustrations and exercises along with pseudo-code has been provided here. There are several deterministic and approximation-based optimization methods that have been developed by the researchers, such as branch-and-bound techniques, simplex methods, approximation and Artificial Intelligence-based methods such as evolutionary methods, Swarm-based methods, physics-based methods, socio-inspired methods, etc. In the paper "OpenCV and MQTT Based Intelligent Management System", a system is proposed which is intelligent and can perform identification, counting, and calculation of density of vehicles. After calculating the traffic density, the system classifies the density into low, medium, and high density with the help of a decision algorithm. This system is based on Python programming, and the libraries used in Python are Open-source Computer Vision, NumPy, Chardet, and time library.

Название: Supervised Machine Learning with Python: A Comprehensive guide to Supervised Learning for 2024
Автор: Науdеn Vаn Dеr Роst
Издательство: Reactive Publishing
Год: 2023
Страниц: 365
Язык: английский
Формат: pdf
Размер: 54.4 MB

Push the boundaries of Machine Learning with Python and elevate your data analysis skills to new heights! 'Supervised Machine Learning with Python' is the essential guide for professionals who have mastered the basics and are ready to dive into the more complex and powerful aspects of machine learning. If you were captivated by the top-selling introductory texts, this book is the perfect next step to satisfy your thirst for advanced knowledge. With clear explanations, practical examples, and expert techniques, this book will help you unlock deeper insights from your data. Inside, you will find advanced strategies for model selection, feature engineering, and tuning that will enable you to achieve superior predictive performance. 'Supervised Machine Learning with Python' is specifically designed for professionals who are ready to use Python's powerful libraries, such as Scikit-learn, Pandas, and NumPy, to deploy models that can learn from data. You'll learn not only the theory behind the algorithms but also how to implement them in practice to solve the kind of data analytical problems you encounter every day. In these pages, you will find advanced case studies that vividly illustrate the applications of supervised Machine Learning in various industries. Whether you work in finance, healthcare, retail, or any other field where data drives decisions, this book is tailored to help you add a new level of sophistication to your work.

Название: Machine Learning Crash Course for Engineers
Автор: Еklаs Ноssаin
Издательство: Springer
Год: 2024
Страниц: 465
Язык: английский
Формат: pdf
Размер: 12.7 MB

​Machine Learning Crash Course for Engineers is a reader-friendly introductory guide to Machine Learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of Machine Learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement Machine Learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of Machine Learning quickly. Python and R are the two most highly used programming languages for developing ML programs. Python will be used as the only programming language in all examples of this book due to its ease of use, popularity, and the large, friendly, helpful, and interactive community Python encompasses. It is open-source, highly used in academic and research-based works, and is recommended by experts in almost every field. It is very efficient in terms of the amount of code needed to be written. The short, simple lines of Python code with obvious implications can be easily handled by beginners and are easy to read, debug, and expand. Python is also a cross-platform programming language, implying that it can run well on all operating systems and computers. The most used ML libraries are NumPy, Pandas, Scipy, Theano, Keras, Scikit-learn, Matplotlib, etc., while the most common frameworks are PyTorch and TensorFlow.