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Author: Vijay Gupta
Publisher: eInitial Publication
ISBN:
Category : Computers
Languages : en
Pages : 33
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Book Description
"Mastering Machine Learning: Practical Applications Across Industries" offers a comprehensive exploration of the transformative potential of machine learning (ML) across diverse sectors. From healthcare to finance, manufacturing to entertainment, this ebook delves into the practical applications and real-world case studies that showcase the power of ML in driving innovation and efficiency. Through a blend of theoretical insights and hands-on guidance, readers will embark on a journey through the fundamentals of ML techniques, understanding key concepts, algorithms, and methodologies. The ebook illuminates the path from theory to practice, providing actionable strategies for implementing ML solutions in various organizational contexts. Each chapter is carefully crafted to highlight the unique challenges and opportunities present in different industries, offering in-depth analyses of successful ML implementations and the lessons learned along the way. From predicting patient outcomes in healthcare to optimizing financial portfolios in banking, readers will discover how ML is revolutionizing decision-making processes and reshaping business landscapes. Moreover, "Mastering Machine Learning" doesn't shy away from addressing the ethical considerations inherent in ML applications. Discussions on bias, fairness, privacy, and transparency provide readers with a nuanced understanding of the social and ethical implications of ML adoption, empowering them to navigate these complex issues responsibly. Whether you're a seasoned data scientist looking to expand your expertise or a business leader seeking to leverage ML for strategic advantage, this ebook serves as an indispensable guide. Packed with insights, case studies, and practical tips, "Mastering Machine Learning" equips readers with the knowledge and tools needed to harness the full potential of ML across industries and drive meaningful impact in an increasingly data-driven world.
Author: Vijay Gupta
Publisher: eInitial Publication
ISBN:
Category : Computers
Languages : en
Pages : 33
Get Book Here
Book Description
"Mastering Machine Learning: Practical Applications Across Industries" offers a comprehensive exploration of the transformative potential of machine learning (ML) across diverse sectors. From healthcare to finance, manufacturing to entertainment, this ebook delves into the practical applications and real-world case studies that showcase the power of ML in driving innovation and efficiency. Through a blend of theoretical insights and hands-on guidance, readers will embark on a journey through the fundamentals of ML techniques, understanding key concepts, algorithms, and methodologies. The ebook illuminates the path from theory to practice, providing actionable strategies for implementing ML solutions in various organizational contexts. Each chapter is carefully crafted to highlight the unique challenges and opportunities present in different industries, offering in-depth analyses of successful ML implementations and the lessons learned along the way. From predicting patient outcomes in healthcare to optimizing financial portfolios in banking, readers will discover how ML is revolutionizing decision-making processes and reshaping business landscapes. Moreover, "Mastering Machine Learning" doesn't shy away from addressing the ethical considerations inherent in ML applications. Discussions on bias, fairness, privacy, and transparency provide readers with a nuanced understanding of the social and ethical implications of ML adoption, empowering them to navigate these complex issues responsibly. Whether you're a seasoned data scientist looking to expand your expertise or a business leader seeking to leverage ML for strategic advantage, this ebook serves as an indispensable guide. Packed with insights, case studies, and practical tips, "Mastering Machine Learning" equips readers with the knowledge and tools needed to harness the full potential of ML across industries and drive meaningful impact in an increasingly data-driven world.
Author: Dipanjan Sarkar
Publisher: Apress
ISBN: 1484232070
Category : Computers
Languages : en
Pages : 545
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Book Description
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
Author: AKASH BALAJI MALI NAGARJUNA PUTTA GOKUL SUBRAMANIAN PROF. (DR) ARPIT JAIN
Publisher: DeepMisti Publication
ISBN: 9360444693
Category : Computers
Languages : en
Pages : 168
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Book Description
In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Mastering Machine Learning Algorithms: Practical Applications Using Python and R, is conceived to bridge the gap between emerging technological advancements in machine learning and their strategic application in various domains. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic field. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of machine learning algorithms, particularly focusing on practical applications using Python and R. From foundational theories to advanced implementations, we delve into the critical aspects that drive successful application of machine learning techniques across industries. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, managers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from algorithm development and data processing to strategic management of machine learning projects. Additionally, we emphasize the importance of effective communication, dedicating sections to the art of presenting innovative ideas and solutions in a precise and academically rigorous manner. The inspiration for this book arises from a recognition of the crucial role that machine learning algorithms and their practical applications play in shaping the future of industries. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how machine learning algorithms, applied through Python and R, can be harnessed to drive innovation. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating innovative solutions that will define the future of data-driven industries. Thank you for joining us on this journey. Authors
Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
ISBN: 1838821910
Category : Computers
Languages : en
Pages : 799
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Book Description
Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applicationsBook Description Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. What you will learnUnderstand the characteristics of a machine learning algorithmImplement algorithms from supervised, semi-supervised, unsupervised, and RL domainsLearn how regression works in time-series analysis and risk predictionCreate, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANsWho this book is for This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.
Author: Shubhabrata Datta
Publisher: Springer Nature
ISBN: 3030758478
Category : Technology & Engineering
Languages : en
Pages : 202
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Book Description
This book covers different machine learning techniques such as artificial neural network, support vector machine, rough set theory and deep learning. It points out the difference between the techniques and their suitability for specific applications. This book also describes different applications of machine learning techniques for industrial problems. The book includes several case studies, helping researchers in academia and industries aspiring to use machine learning for solving practical industrial problems.
Author: Andreas François Vermeulen
Publisher: Apress
ISBN: 1484253167
Category : Computers
Languages : en
Pages : 652
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Book Description
Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data engineering and data science. Data lakes currently generated by worldwide industrialized business activities are projected to reach 35 zettabytes (ZB) as the Fourth Industrial Revolution produces an exponential increase of volume, velocity, variety, variability, veracity, visualization, and value. Industrialization of ML evolves from AI and studying pattern recognition against the increasingly unstructured resource stored in data lakes. Industrial Machine Learning supplies advanced, yet practical examples in different industries, including finance, public safety, health care, transportation, manufactory, supply chain, 3D printing, education, research, and data science. The book covers: supervised learning, unsupervised learning, reinforcement learning, evolutionary computing principles, soft robotics disruptors, and hard robotics disruptors. What You Will Learn Generate and identify transformational disruptors of artificial intelligence (AI) Understand the field of machine learning (ML) and apply it to handle big data and process the data lakes in your environment Hone the skills required to handle the future of data engineering and data science Who This Book Is For Intermediate to expert level professionals in the fields of data science, data engineering, machine learning, and data management
Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385
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Book Description
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
ISBN: 1788625900
Category : Computers
Languages : en
Pages : 567
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Book Description
Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.
Author: Dr. Saket S.R. Mengle
Publisher: Packt Publishing Ltd
ISBN: 1789347505
Category : Computers
Languages : en
Pages : 293
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Book Description
Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
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Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
