Knowledge Guided Machine Learning

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Knowledge Guided Machine Learning

Author : Anuj Karpatne
Publisher : CRC Press
ISBN 13 : 1000598101
Total Pages : 442 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Knowledge Guided Machine Learning by : Anuj Karpatne

Download or read book Knowledge Guided Machine Learning written by Anuj Karpatne and published by CRC Press. This book was released on 2022-08-15 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML

Knowledge Guided Machine Learning

Author : Anuj Karpatne
Publisher : CRC Press
ISBN 13 : 1000598136
Total Pages : 520 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Knowledge Guided Machine Learning by : Anuj Karpatne

Download or read book Knowledge Guided Machine Learning written by Anuj Karpatne and published by CRC Press. This book was released on 2022-08-15 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML

Machine Learning

Author : Tom M. Mitchell
Publisher : Springer Science & Business Media
ISBN 13 : 1461322790
Total Pages : 413 pages
Book Rating : 4.4/5 (613 download)

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Book Synopsis Machine Learning by : Tom M. Mitchell

Download or read book Machine Learning written by Tom M. Mitchell and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.

Automated Machine Learning

Author : Frank Hutter
Publisher : Springer
ISBN 13 : 3030053180
Total Pages : 223 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Automated Machine Learning by : Frank Hutter

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Machine learning

Author : Tom Michael Mitchell
Publisher :
ISBN 13 :
Total Pages : 429 pages
Book Rating : 4.:/5 (636 download)

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Book Synopsis Machine learning by : Tom Michael Mitchell

Download or read book Machine learning written by Tom Michael Mitchell and published by . This book was released on 1986 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Machine Learning for Beginners

Author : Samuel Hack
Publisher : Samuel Hack
ISBN 13 : 9781801728560
Total Pages : 220 pages
Book Rating : 4.7/5 (285 download)

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Book Synopsis Machine Learning for Beginners by : Samuel Hack

Download or read book Machine Learning for Beginners written by Samuel Hack and published by Samuel Hack. This book was released on 2021-03-07 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: TODAY ONLY 55% OFF for Bookstores! Are you interested in learning about the amazing capabilities of machine learning, but you're worried it will be just too complicated? Or are you a programmer looking for a solid introduction into this field? Your customers must have this guide to understand the hidden secrets of artificial intelligence! Machine learning is an incredible technology which we're only just beginning to understand. Those who break into this industry early will reap the rewards as this field grows more and more important to businesses the world over. And the good news is, it's not too late to start! This guide breaks down the fundamentals of machine learning in a way that anyone can understand. With reference to the different kinds of machine learning models, neural networks, and the way these models learn data, you'll find everything you need to know to get started with machine learning in a concise, easy-to-understand way. Here's what you'll discover inside: What is Artificial Intelligence Really, and Why is it So Powerful? Choosing the Right Kind of Machine Learning Model for You An Introduction to Statistics Supervised and Unsupervised Learning The Power of Neural Networks Reinforcement Learning and Ensemble Modeling "Random Forests" and Decision Trees Must-Have Programming Tools And Much More! Whether you're already a programmer or if you're a complete beginner, now you can break into machine learning in no time! Covering all the basics from simple decision trees to the complex decision-making processes which mirror our own brains, Machine Learning for Beginners is your comprehensive introduction to this amazing field! Buy it NOW and let your customers become to addicted to this incredible book!

Mastering Machine Learning: A Comprehensive Guide to Success

Author : Rick Spair
Publisher : Rick Spair
ISBN 13 :
Total Pages : 462 pages
Book Rating : 4.2/5 (231 download)

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Book Synopsis Mastering Machine Learning: A Comprehensive Guide to Success by : Rick Spair

Download or read book Mastering Machine Learning: A Comprehensive Guide to Success written by Rick Spair and published by Rick Spair. This book was released on with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to "Mastering Machine Learning: A Comprehensive Guide to Success." In this book, we embark on an exciting journey into the world of machine learning (ML), exploring its concepts, techniques, and practical applications. Whether you are a beginner taking your first steps into the field or an experienced practitioner seeking to deepen your knowledge, this comprehensive guide will equip you with the tools, strategies, and insights needed to succeed in the ever-evolving landscape of ML. Machine learning is a rapidly advancing field that has revolutionized industries and transformed the way we tackle complex problems. From personalized recommendations and speech recognition systems to autonomous vehicles and medical diagnostics, machine learning has become an integral part of our daily lives. Its ability to analyze vast amounts of data, identify patterns, and make predictions has paved the way for groundbreaking advancements across various domains. However, mastering machine learning requires more than just understanding the algorithms and techniques. It requires a holistic approach that encompasses data collection and preparation, exploratory data analysis, model building, evaluation, deployment, and continuous learning. It also demands a deep understanding of the ethical and social implications of machine learning, ensuring responsible and fair use of this powerful technology. In this book, we have carefully crafted 20 comprehensive chapters that cover a wide range of topics, from the fundamentals of machine learning to advanced techniques and future trends. Each chapter provides a deep dive into a specific aspect of machine learning, offering tips, recommendations, and strategies for success. You will learn about various algorithms, data preprocessing techniques, model evaluation methods, interpretability approaches, and much more. Throughout the book, we emphasize a practical approach to machine learning. Real-world examples, case studies, and hands-on exercises are incorporated to help you gain a deeper understanding of the concepts and apply them to your own projects. We believe that active learning and practical experience are crucial for mastering machine learning, and we encourage you to explore, experiment, and build your own models. While this book serves as a comprehensive guide, it is important to note that machine learning is a rapidly evolving field. New algorithms, techniques, and technologies are constantly emerging, and staying up-to-date with the latest advancements is essential. However, the principles and foundations discussed in this book will provide you with a solid framework to adapt and navigate the ever-changing landscape of machine learning. Whether you are an aspiring data scientist, a software engineer, a researcher, or a business professional, this book is designed to be your trusted companion in your journey to mastering machine learning. By the time you reach the end, you will have gained a deep understanding of the fundamental concepts, acquired practical skills for applying machine learning in real-world scenarios, and developed the mindset needed to tackle complex challenges and drive innovation. Get ready to embark on an exciting adventure into the world of machine learning. Let's begin our journey towards mastering machine learning and unlocking its full potential. Happy learning!

A Guided Tour of Artificial Intelligence Research

Author : Pierre Marquis
Publisher : Springer Nature
ISBN 13 : 3030061647
Total Pages : 808 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis A Guided Tour of Artificial Intelligence Research by : Pierre Marquis

Download or read book A Guided Tour of Artificial Intelligence Research written by Pierre Marquis and published by Springer Nature. This book was released on 2020-05-08 with total page 808 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Then the volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, reasoning based on similarity (case-based, interpolative, or analogical), as well as reasoning about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statistical computational learning and reinforcement learning (other machine learning topics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.

Machine Learning: The Complete Step-By-Step Guide to Learning and Understanding Machine Learning from Beginners, Intermediate Advanced,

Download Machine Learning: The Complete Step-By-Step Guide to Learning and Understanding Machine Learning from Beginners, Intermediate Advanced, PDF Online Free

Author : Peter Bradley
Publisher : Independently Published
ISBN 13 : 9781798105016
Total Pages : 334 pages
Book Rating : 4.1/5 (5 download)

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Book Synopsis Machine Learning: The Complete Step-By-Step Guide to Learning and Understanding Machine Learning from Beginners, Intermediate Advanced, by : Peter Bradley

Download or read book Machine Learning: The Complete Step-By-Step Guide to Learning and Understanding Machine Learning from Beginners, Intermediate Advanced, written by Peter Bradley and published by Independently Published. This book was released on 2019-02-26 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Book Includes: Machine Learning: A Comprehensive, Step-by-Step Guide to Learning and Understanding Machine Learning Concepts, Technology and Principles for Beginners Machine Learning: A Comprehensive, Step-by-Step Guide to Intermediate Concepts and Techniques in Machine Learning Machine Learning: A Comprehensive, Step-by-Step Guide to Learning and Applying Advanced Concepts and Techniques in Machine Learning Machine Learning: A Complete Exploration of Highly Advanced Machine Learning Concepts, Best Practices and Techniques Buy the Paperback version of this book, and get the Kindle eBOOK version for FREE Graphics in this book are printed in black and white. Machines are created to make work easier for us, but so many have seen machines as a major barrier due to their supposed technicality of machines. Are you a novice trying to understand the basics of machine? Do you have prior knowledge and you wish to acquire further understanding about tensorFlow, scikit- learn, algorithms, decision trees, random forest, deep learning or neural networks? Are you even a pro and you wish to add to your knowledge? This book is all you need. This painstakingly compiled manuscript unravels the rudiments and generality of machine learning. It is total and all encompassing with accurate and concise principles of machine learning. This quintessential book comprises modules that cut across various level of knowledge in machine learning. It is an exquisite material that grants you practical knowledge in machines. It weighs more than mere words, it is gold in manuscript. You might not know how much you know or how much you need to know until you avail yourself with essential materials. This book is not one of all you need to understand machine learning; it is all you need to uncover the full scope of learning machines. Technicality is very relative when you have the right knowledge. Stay ahead; make a choice that will last. Would You Like To Know More? Scroll to the top of the page and select the buy now button.

Understanding Machine Learning

Author : Shai Shalev-Shwartz
Publisher : Cambridge University Press
ISBN 13 : 1107057132
Total Pages : 415 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz

Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Machine Learning and Its Application: A Quick Guide for Beginners

Author : Indranath Chatterjee
Publisher : Bentham Science Publishers
ISBN 13 : 1681089416
Total Pages : 360 pages
Book Rating : 4.6/5 (81 download)

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Book Synopsis Machine Learning and Its Application: A Quick Guide for Beginners by : Indranath Chatterjee

Download or read book Machine Learning and Its Application: A Quick Guide for Beginners written by Indranath Chatterjee and published by Bentham Science Publishers. This book was released on 2021-12-22 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, which include the types of machine learning algorithms and the statistical knowledge required for devising relevant computer algorithms. The book also covers advanced topics such as deep learning and feature engineering. Key features: - 8 organized chapters on core concepts of machine learning for learners - Accessible text for beginners unfamiliar with complex mathematical concepts - Introductory topics are included, including supervised learning, unsupervised learning, reinforcement learning and predictive statistics - Advanced topics such as deep learning and feature engineering provide additional information - Introduces readers to python programming with examples of code for understanding and practice - Includes a summary of the text and a dedicated section for references Machine Learning and Its Application: A Quick Guide for Beginners is an essential book for students and learners who want to understand the basics of machine learning and equip themselves with the knowledge to write algorithms for intelligent data processing applications.

Deep Learning for Coders with fastai and PyTorch

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Machine Learning: A Comprehensive, Step-By-Step Guide To Learning And Understanding Machine Learning From Beginners, Intermediate, Advan

Download Machine Learning: A Comprehensive, Step-By-Step Guide To Learning And Understanding Machine Learning From Beginners, Intermediate, Advan PDF Online Free

Author : Peter Bradley
Publisher :
ISBN 13 : 9781393114611
Total Pages : 418 pages
Book Rating : 4.1/5 (146 download)

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Book Synopsis Machine Learning: A Comprehensive, Step-By-Step Guide To Learning And Understanding Machine Learning From Beginners, Intermediate, Advan by : Peter Bradley

Download or read book Machine Learning: A Comprehensive, Step-By-Step Guide To Learning And Understanding Machine Learning From Beginners, Intermediate, Advan written by Peter Bradley and published by . This book was released on 2019-09-20 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes: Machine Learning: A Complete Exploration of Highly Advanced Machine Learning Concepts, Best Practices and Techniques Machine Learning: A Comprehensive, Step-by-Step Guide to Intermediate Concepts and Techniques in Machine Learning Machine Learning: A Comprehensive, Step-by-Step Guide to Learning and Applying Advanced Concepts and Techniques in Machine Learning Machine Learning For Beginners: A Comprehensive, Step-by-Step Guide to Learning and Understanding Machine Learning Concepts, Technology and Principles for Beginners Machines are created to make work easier for us, but so many have seen machines as a major barrier due to their supposed technicality of machines. Are you a novice trying to understand the basics of machine? Do you have prior knowledge and you wish to acquire further understanding about tensorFlow, scikit-learn, algorithms, decision trees, random forest, deep learning or neural networks? Are you even a pro and you wish to add to your knowledge? This book is all you need. This painstakingly compiled manuscript unravels the rudiments and generality of machine learning. It is total and all encompassing with accurate and concise principles of machine learning. This quintessential book comprises modules that cut across various level of knowledge in machine learning. It is an exquisite material that grants you practical knowledge in machines. It weighs more than mere words, it is gold in manuscript. You might not know how much you know or how much you need to know until you avail yourself with essential materials. This book is not one of all you need to understand machine learning; it is all you need to uncover the full scope of learning machines. Technicality is very relative when you have the right knowledge. Stay ahead; make a choice that will last. Would You Like To Know More? Scroll to the top of the page and select the buy now button.

Practical Machine Learning with R

Author : Carsten Lange
Publisher : CRC Press
ISBN 13 : 1040021247
Total Pages : 369 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Practical Machine Learning with R by : Carsten Lange

Download or read book Practical Machine Learning with R written by Carsten Lange and published by CRC Press. This book was released on 2024-05-20 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook is a comprehensive guide to machine learning and artificial intelligence tailored for students in business and economics. It takes a hands-on approach to teach machine learning, emphasizing practical applications over complex mathematical concepts. Students are not required to have advanced mathematics knowledge such as matrix algebra or calculus. The author introduces machine learning algorithms, utilizing the widely used R language for statistical analysis. Each chapter includes examples, case studies, and interactive tutorials to enhance understanding. No prior programming knowledge is needed. The book leverages the tidymodels package, an extension of R, to streamline data processing and model workflows. This package simplifies commands, making the logic of algorithms more accessible by minimizing programming syntax hurdles. The use of tidymodels ensures a unified experience across various machine learning models. With interactive tutorials that students can download and follow along at their own pace, the book provides a practical approach to apply machine learning algorithms to real-world scenarios. In addition to the interactive tutorials, each chapter includes a Digital Resources section, offering links to articles, videos, data, and sample R code scripts. A companion website further enriches the learning and teaching experience: https://ai.lange-analytics.com. This book is not just a textbook; it is a dynamic learning experience that empowers students and instructors alike with a practical and accessible approach to machine learning in business and economics. Key Features: Unlocks machine learning basics without advanced mathematics — no calculus or matrix algebra required. Demonstrates each concept with R code and real-world data for a deep understanding — no prior programming knowledge is needed. Bridges the gap between theory and real-world applications with hands-on interactive projects and tutorials in every chapter, guided with hints and solutions. Encourages continuous learning with chapter-specific online resources—video tutorials, R-scripts, blog posts, and an online community. Supports instructors through a companion website that includes customizable materials such as slides and syllabi to fit their specific course needs.

Machine Learning for Beginners

Author : Ryan Knight
Publisher : Ryan Knight
ISBN 13 :
Total Pages : 48 pages
Book Rating : 4./5 ( download)

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Book Synopsis Machine Learning for Beginners by : Ryan Knight

Download or read book Machine Learning for Beginners written by Ryan Knight and published by Ryan Knight. This book was released on 2024-05-08 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: Enter a world of algorithms, data, and artificial intelligence, this all-inclusive guide strips away the complexity of machine learning and AI, transforming them from daunting subjects into accessible and comprehendible concepts. Whether you're a total novice or a professional looking to broaden your knowledge, this guide provides a structured approach that walks you through the basics, right through to the cutting-edge applications of AI and machine learning. Crafted with the reader in mind, every chapter provides detailed explanations, relatable examples, and step-by-step instructions to ensure a comprehensive yet enjoyable learning experience. Inside this book, you'll discover: An introduction to the exciting world of machine learning and AI, making it accessible to everyone regardless of technical background. Comprehensive discussions on the foundational concepts of machine learning, including algorithms, data science principles, and the different types of machine learning. Deep dives into the transformative applications of AI and machine learning in industries such as healthcare, retail, finance, transportation, education, and entertainment. Practical guides on mastering the essential tools and techniques for building intelligent solutions, complete with hands-on exercises and examples. An exploration of the ethical considerations around AI and machine learning, and the responsibilities we have as practitioners. Future trends in machine learning and AI, providing a glimpse into what lies on the horizon. Ignite your journey into the fascinating world of machine learning and AI today. Unleash the power of data and algorithms, create intelligent solutions, and shape a better future. Are you ready to master the future? The opportunity is just a click away. Pick up your copy now, and let's get started!

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Author : Rosa Meo
Publisher :
ISBN 13 : 9783031746420
Total Pages : 0 pages
Book Rating : 4.7/5 (464 download)

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Book Synopsis Machine Learning and Principles and Practice of Knowledge Discovery in Databases by : Rosa Meo

Download or read book Machine Learning and Principles and Practice of Knowledge Discovery in Databases written by Rosa Meo and published by . This book was released on 2024-12-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The five-volume set CCIS 2133-2137 constitutes the refereed proceedings of the workshops held in conjunction with the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, during September 18-22, 2023. The 200 full papers presented in these proceedings were carefully reviewed and selected from 515 submissions. The papers have been organized in the following tracks: Part I: Advances in Interpretable Machine Learning and Artificial Intelligence -- Joint Workshop and Tutorial; BIAS 2023 - 3rd Workshop on Bias and Fairness in AI; Biased Data in Conversational Agents; Explainable Artificial Intelligence: From Static to Dynamic; ML, Law and Society; Part II: RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education; SoGood 2023 - 8th Workshop on Data Science for Social Good; Towards Hybrid Human-Machine Learning and Decision Making (HLDM); Uncertainty meets explainability in machine learning; Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation; Part III: XAI-TS: Explainable AI for Time Series: Advances and Applications; XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining; Deep Learning for Sustainable Precision Agriculture; Knowledge Guided Machine Learning; MACLEAN: MAChine Learning for EArth ObservatioN; MLG: Mining and Learning with Graphs; Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences; New Frontiers in Mining Complex Patterns; Part IV: PharML, Machine Learning for Pharma and Healthcare Applications; Simplification, Compression, Efficiency and Frugality for Artificial intelligence; Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making; 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL); Adapting to Change: Reliable Multimodal Learning Across Domains; AI4M: AI for Manufacturing; Part V: Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications; Deep learning meets Neuromorphic Hardware; Discovery challenge; ITEM: IoT, Edge, and Mobile for Embedded Machine Learning; LIMBO - LearnIng and Mining for BlOckchains; Machine Learning for Cybersecurity (MLCS 2023); MIDAS - The 8th Workshop on MIning DAta for financial applicationS; Workshop on Advancements in Federated Learning.