Download or read book Data Science Tshirt Random Forest Machine Learning Analyst written by Cherie Mazzoni and published by . This book was released on 2020-12-29 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science Tshirt Random Forest Machine Learning Analyst/h3>

Download or read book Data Science and Intelligent Systems written by Radek Silhavy and published by Springer Nature. This book was released on 2021-11-16 with total page 1073 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the second part of refereed proceedings of the 5th Computational Methods in Systems and Software 2021 (CoMeSySo 2021) proceedings. The real-world problems related to data science and algorithm design related to systems and software engineering are presented in this papers. Furthermore, the basic research’ papers that describe novel approaches in the data science, algorithm design and in systems and software engineering are included. The CoMeSySo 2021 conference is breaking the barriers, being held online. CoMeSySo 2021 intends to provide an international forum for the discussion of the latest high-quality research results

Download or read book Data Science in Practice written by Alan Said and published by Springer. This book was released on 2018-09-19 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.

Download or read book Supervised Machine Learning for Text Analysis in R written by Emil Hvitfeldt and published by CRC Press. This book was released on 2021-10-22 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.

Download or read book Computational Science and Its Applications written by Anupama Chadha and published by CRC Press. This book was released on 2024-01-09 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new volume explores the overlapping behavior of some aspects of computational science, focusing on the intersection of computing hardware, algorithms, mathematics, and data management components. The chapters discuss the various application areas of the computational science techniques such as artificial intelligence, machine learning, data science, quantum computing, image processing, evolutionary algorithms, process simulation, deep learning, big data analysis, etc.

Download or read book Advances in Data Driven Computing and Intelligent Systems written by Swagatam Das and published by Springer Nature. This book was released on with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Novel Financial Applications of Machine Learning and Deep Learning written by Mohammad Zoynul Abedin and published by Springer Nature. This book was released on 2023-03-01 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.

Download or read book Intelligent Systems and Applications written by Kohei Arai and published by Springer Nature. This book was released on with total page 890 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book IoT and WSN based Smart Cities A Machine Learning Perspective written by Shalli Rani and published by Springer Nature. This book was released on 2022-05-30 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an investigative approach to how machine learning is helping to maintain and secure smart cities, including principal uses such as smart monitoring, privacy, reliability, and public protection. The authors cover important areas and issues around implementation roadblocks, ideas, and opportunities in smart city development. The authors also include new algorithms, architectures and platforms that can accelerate the growth of smart city concepts and applications. Moreover, this book provides details on specific applications and case studies related to smart city infrastructures, big data management, and prediction techniques using machine learning.

Download or read book MSIEID 2022 written by Haocun Wu and published by European Alliance for Innovation. This book was released on 2023-03-14 with total page 1596 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Management Science Informatization and Economic Innovation Development Conference is a leading conference held annually. It aims at building an academic platform for the communication and academic exchange among participants from various fields related to management science informatization and economic innovation development. Here, scholars, experts, and researchers are welcomed to share their research progress and inspirations. It is a great opportunity to promote academic communication and collaboration worldwide. This volume contains the papers presented at the 4th Management Science Informatization and Economic Innovation Development Conference (MSIEID 2022), held during December 9th-11th, 2022 in Chongqing, China (virtual event). For the safety concern of all participants under nowadays situation, we decided to hold it as a virtual conference which is also effective and convenient for academic exchange and communication. Everyone interested in these fields were welcomed to join the online conference and to give comments and raise questions to the speeches and presentations.

Download or read book Hands On Explainable AI XAI with Python written by Denis Rothman and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key FeaturesLearn explainable AI tools and techniques to process trustworthy AI resultsUnderstand how to detect, handle, and avoid common issues with AI ethics and biasIntegrate fair AI into popular apps and reporting tools to deliver business value using Python and associated toolsBook Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learnPlan for XAI through the different stages of the machine learning life cycleEstimate the strengths and weaknesses of popular open-source XAI applicationsExamine how to detect and handle bias issues in machine learning dataReview ethics considerations and tools to address common problems in machine learning dataShare XAI design and visualization best practicesIntegrate explainable AI results using Python modelsUse XAI toolkits for Python in machine learning life cycles to solve business problemsWho this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications

Download or read book Practical Machine Learning with Rust written by Joydeep Bhattacharjee and published by Apress. This book was released on 2019-12-10 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will Learn Write machine learning algorithms in RustUse Rust libraries for different tasks in machine learningCreate concise Rust packages for your machine learning applicationsImplement NLP and computer vision in RustDeploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust.

Download or read book Interpretable Machine Learning with Python written by Serg Masís and published by Packt Publishing Ltd. This book was released on 2021-03-26 with total page 737 pages. Available in PDF, EPUB and Kindle. Book excerpt: A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.

Download or read book Data Science from Scratch written by Joel Grus and published by "O'Reilly Media, Inc.". This book was released on 2015-04-14 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Download or read book Building a Brand Image Through Electronic Customer Relationship Management written by Naim, Arshi and published by IGI Global. This book was released on 2022-06-30 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: Effective e-customer relationship management is imperative for increasing customer satisfaction, online sales, website patronage, loyalty, and retention. To understand exactly how this business strategy can be applied to enhance business operations, further study on its various benefits, opportunities, and challenges is required. Building a Brand Image Through Electronic Customer Relationship Management develops electronic customer relationship management strategies for achieving customer satisfaction and explains the concepts and uses of electronic customer relationship management to meet strategic objectives, improve customer loyalty, and build brand image. Covering topics such as marketing, brand equity, customer loyalty, and social media, this reference work is ideal for business owners, managers, entrepreneurs, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Download or read book Modern Data Science with R written by Benjamin S. Baumer and published by CRC Press. This book was released on 2021-03-31 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.

Download or read book Applied Predictive Modeling written by Max Kuhn and published by Springer Science & Business Media. This book was released on 2013-05-17 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.