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Smarter Decisions The Intersection Of Internet Of Things And Decision Science
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Book Synopsis Smarter Decisions – The Intersection of Internet of Things and Decision Science by : Jojo Moolayil
Download or read book Smarter Decisions – The Intersection of Internet of Things and Decision Science written by Jojo Moolayil and published by Packt Publishing Ltd. This book was released on 2016-07-29 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: Enter the world of Internet of Things with the power of data science with this highly practical, engaging book About This Book Explore real-world use cases from the Internet of Things (IoT) domain using decision science with this easy-to-follow, practical book Learn to make smarter decisions on top of your IoT solutions so that your IoT is smart in a real sense This highly practical, example-rich guide fills the gap between your knowledge of data science and IoT Who This Book Is For If you have a basic programming experience with R and want to solve business use cases in IoT using decision science then this book is for you. Even if your're a non-technical manager anchoring IoT projects, you can skip the code and still benefit from the book. What You Will Learn Explore decision science with respect to IoT Get to know the end to end analytics stack – Descriptive + Inquisitive + Predictive + Prescriptive Solve problems in IoT connected assets and connected operations Design and solve real-life IoT business use cases using cutting edge machine learning techniques Synthesize and assimilate results to form the perfect story for a business Master the art of problem solving when IoT meets decision science using a variety of statistical and machine learning techniques along with hands on tasks in R In Detail With an increasing number of devices getting connected to the Internet, massive amounts of data are being generated that can be used for analysis. This book helps you to understand Internet of Things in depth and decision science, and solve business use cases. With IoT, the frequency and impact of the problem is huge. Addressing a problem with such a huge impact requires a very structured approach. The entire journey of addressing the problem by defining it, designing the solution, and executing it using decision science is articulated in this book through engaging and easy-to-understand business use cases. You will get a detailed understanding of IoT, decision science, and the art of solving a business problem in IoT through decision science. By the end of this book, you'll have an understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for it Style and approach This scenario-based tutorial approaches the topic systematically, allowing you to build upon what you learned in previous chapters.
Book Synopsis Machine Learning for Decision Makers by : Patanjali Kashyap
Download or read book Machine Learning for Decision Makers written by Patanjali Kashyap and published by Apress. This book was released on 2018-01-04 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.
Book Synopsis Mastering Machine Learning with Python in Six Steps by : Manohar Swamynathan
Download or read book Mastering Machine Learning with Python in Six Steps written by Manohar Swamynathan and published by Apress. This book was released on 2017-06-05 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. You’ll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you’ll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage. What You'll Learn Examine the fundamentals of Python programming language Review machine Learning history and evolution Understand machine learning system development frameworks Implement supervised/unsupervised/reinforcement learning techniques with examples Explore fundamental to advanced text mining techniques Implement various deep learning frameworks Who This Book Is For Python developers or data engineers looking to expand their knowledge or career into machine learning area. Non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python. Novice machine learning practitioners looking to learn advanced topics, such as hyperparameter tuning, various ensemble techniques, natural language processing (NLP), deep learning, and basics of reinforcement learning.
Book Synopsis Python Unit Test Automation by : Ashwin Pajankar
Download or read book Python Unit Test Automation written by Ashwin Pajankar and published by Apress. This book was released on 2017-02-23 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quickly learn how to automate unit testing of Python 3 code with Python 3 automation libraries, such as doctest, unittest, nose, nose2, and pytest. This book explores the important concepts in software testing and their implementation in Python 3 and shows you how to automate, organize, and execute unit tests for this language. This knowledge is often acquired by reading source code, manuals, and posting questions on community forums, which tends to be a slow and painful process. Python Unit Test Automation will allow you to quickly ramp up your understanding of unit test libraries for Python 3 through the practical use of code examples and exercises. All of which makes this book a great resource for software developers and testers who want to get started with unit test automation in Python 3 and compare the differences with Python 2. This short work is your must-have quick start guide to mastering the essential concepts of software testing in Python. What You'll Learn: Essential concepts in software testing Various test automation libraries for Python, such as doctest, unittest, nose, nose2, and pytest Test-driven development and best practices for test automation in Python Code examples and exercises Who This Book Is For: Python developers, software testers, open source enthusiasts, and contributors to the Python community
Book Synopsis Machine Learning Using R by : Karthik Ramasubramanian
Download or read book Machine Learning Using R written by Karthik Ramasubramanian and published by Apress. This book was released on 2016-12-22 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examine the latest technological advancements in building a scalable machine learning model with Big Data using R. This book shows you how to work with a machine learning algorithm and use it to build a ML model from raw data. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. For every machine learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All the images are available in color and hi-res as part of the code download. This new paradigm of teaching machine learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in this book makes it easy for someone to connect the dots.. What You'll Learn Use the model building process flow Apply theoretical aspects of machine learning Review industry-based cae studies Understand ML algorithms using R Build machine learning models using Apache Hadoop and Spark Who This Book is For Data scientists, data science professionals and researchers in academia who want to understand the nuances of machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.
Book Synopsis Machine Learning and Cognition in Enterprises by : Rohit Kumar
Download or read book Machine Learning and Cognition in Enterprises written by Rohit Kumar and published by Apress. This book was released on 2017-11-13 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn about the emergence and evolution of IT in the enterprise, see how machine learning is transforming business intelligence, and discover various cognitive artificial intelligence solutions that complement and extend machine learning. In this book, author Rohit Kumar explores the challenges when these concepts intersect in IT systems by presenting detailed descriptions and business scenarios. He starts with the basics of how artificial intelligence started and how cognitive computing developed out of it. He'll explain every aspect of machine learning in detail, the reasons for changing business models to adopt it, and why your business needs it. Along the way you'll become comfortable with the intricacies of natural language processing, predictive analytics, and cognitive computing. Each technique is covered in detail so you can confidently integrate it into your enterprise as it is needed. This practical guide gives you a roadmap for transformin g your business with cognitive computing, giving you the ability to work confidently in an ever-changing enterprise environment. What You'll Learn See the history of AI and how machine learning and cognitive computing evolved Discover why cognitive computing is so important and why your business needs it Master the details of modern AI as it applies to enterprises Map the path ahead in terms of your IT-business integration Avoid common road blocks in the process of adopting cognitive computing in your business Who This Book Is For Business managers and leadership teams.
Book Synopsis Business Analytics Using R - A Practical Approach by : Umesh R Hodeghatta
Download or read book Business Analytics Using R - A Practical Approach written by Umesh R Hodeghatta and published by Apress. This book was released on 2016-12-27 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. What You Will Learn • Write R programs to handle data • Build analytical models and draw useful inferences from them • Discover the basic concepts of data mining and machine learning • Carry out predictive modeling • Define a business issue as an analytical problem Who This Book Is For Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals.
Book Synopsis Deep Learning with Python by : Nikhil Ketkar
Download or read book Deep Learning with Python written by Nikhil Ketkar and published by Apress. This book was released on 2017-04-18 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
Book Synopsis Hands-on Scikit-Learn for Machine Learning Applications by : David Paper
Download or read book Hands-on Scikit-Learn for Machine Learning Applications written by David Paper and published by Apress. This book was released on 2019-11-16 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine. All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complex machine learning algorithms. Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python. What You'll LearnWork with simple and complex datasets common to Scikit-Learn Manipulate data into vectors and matrices for algorithmic processing Become familiar with the Anaconda distribution used in data scienceApply machine learning with Classifiers, Regressors, and Dimensionality Reduction Tune algorithms and find the best algorithms for each dataset Load data from and save to CSV, JSON, Numpy, and Pandas formats Who This Book Is For The aspiring data scientist yearning to break into machine learning through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming and very basic applied linear algebra will make learning easier, although anyone can benefit from this book.
Book Synopsis Applied Supervised Learning with R by : Karthik Ramasubramanian
Download or read book Applied Supervised Learning with R written by Karthik Ramasubramanian and published by Packt Publishing Ltd. This book was released on 2019-05-31 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object detection in driver less cars, customer churn, and loan default prediction. Key FeaturesStudy supervised learning algorithms by using real-world datasets Fine tune optimal parameters with hyperparameter optimizationSelect the best algorithm using the model evaluation frameworkBook Description R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. To prevent you from overfitting your model, a dedicated section will even demonstrate how you can add various regularization terms. By the end of this book, you will have the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs. What you will learnDevelop analytical thinking to precisely identify a business problemWrangle data with dplyr, tidyr, and reshape2Visualize data with ggplot2Validate your supervised machine learning model using k-fold Optimize hyperparameters with grid and random search, and Bayesian optimizationDeploy your model on Amazon Web Services (AWS) Lambda with plumberImprove your model’s performance with feature selection and dimensionality reductionWho this book is for This book is specially designed for novice and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its various use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this book.
Book Synopsis Machine Learning with Swift by : Oleksandr Sosnovshchenko
Download or read book Machine Learning with Swift written by Oleksandr Sosnovshchenko and published by Packt Publishing Ltd. This book was released on 2018-02-28 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of machine learning and Swift programming to build intelligent iOS applications with ease Key Features Implement effective machine learning solutions for your iOS applications Use Swift and Core ML to build and deploy popular machine learning models Develop neural networks for natural language processing and computer vision Book Description Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves. What you will learn Learn rapid model prototyping with Python and Swift Deploy pre-trained models to iOS using Core ML Find hidden patterns in the data using unsupervised learning Get a deeper understanding of the clustering techniques Learn modern compact architectures of neural networks for iOS devices Train neural networks for image processing and natural language processing Who this book is for iOS developers who wish to create smarter iOS applications using the power of machine learning will find this book to be useful. This book will also benefit data science professionals who are interested in performing machine learning on mobile devices. Familiarity with Swift programming is all you need to get started with this book.
Book Synopsis Data Science and Internet of Things by : Giancarlo Fortino
Download or read book Data Science and Internet of Things written by Giancarlo Fortino and published by Springer Nature. This book was released on 2021-02-18 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the combination of IoT and data science, in particular how methods, algorithms, and tools from data science can effectively support IoT. The authors show how data science methodologies, techniques and tools, can translate data into information, enabling the effectiveness and usefulness of new services offered by IoT stakeholders. The authors posit that if IoT is indeed the infrastructure of the future, data structure is the key that can lead to a significant improvement of human life. The book aims to present innovative IoT applications as well as ongoing research that exploit modern data science approaches. Readers are offered issues and challenges in a cross-disciplinary scenario that involves both IoT and data science fields. The book features contributions from academics, researchers, and professionals from both fields.
Book Synopsis Big Data Analytics for Internet of Things by : Tausifa Jan Saleem
Download or read book Big Data Analytics for Internet of Things written by Tausifa Jan Saleem and published by John Wiley & Sons. This book was released on 2021-04-20 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: BIG DATA ANALYTICS FOR INTERNET OF THINGS Discover the latest developments in IoT Big Data with a new resource from established and emerging leaders in the field Big Data Analytics for Internet of Things delivers a comprehensive overview of all aspects of big data analytics in Internet of Things (IoT) systems. The book includes discussions of the enabling technologies of IoT data analytics, types of IoT data analytics, challenges in IoT data analytics, demand for IoT data analytics, computing platforms, analytical tools, privacy, and security. The distinguished editors have included resources that address key techniques in the analysis of IoT data. The book demonstrates how to select the appropriate techniques to unearth valuable insights from IoT data and offers novel designs for IoT systems. With an abiding focus on practical strategies with concrete applications for data analysts and IoT professionals, Big Data Analytics for Internet of Things also offers readers: A thorough introduction to the Internet of Things, including IoT architectures, enabling technologies, and applications An exploration of the intersection between the Internet of Things and Big Data, including IoT as a source of Big Data, the unique characteristics of IoT data, etc. A discussion of the IoT data analytics, including the data analytical requirements of IoT data and the types of IoT analytics, including predictive, descriptive, and prescriptive analytics A treatment of machine learning techniques for IoT data analytics Perfect for professionals, industry practitioners, and researchers engaged in big data analytics related to IoT systems, Big Data Analytics for Internet of Things will also earn a place in the libraries of IoT designers and manufacturers interested in facilitating the efficient implementation of data analytics strategies.
Book Synopsis The Rise of Machines by : Adrian David Cheok
Download or read book The Rise of Machines written by Adrian David Cheok and published by CRC Press. This book was released on 2024-11-21 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an in-depth look at the impact of artificial intelligence (AI) on the future of work. The rise of AI and automation is transforming the world of work, and the book explores the implications of this transformation on jobs and skills. It begins by introducing readers to the basics of AI technology and its various applications in the workplace. It then moves on to examine the impact of AI on jobs and skills, including the changing nature of work and the potential for job loss due to automation. It also delves into the ethical implications of AI in the workplace, including the moral and ethical questions that arise when AI is used to make decisions that affect people's lives. Besides exploring the impact of AI on the workforce, the book provides practical advice for preparing for the future of work in the age of AI. This includes the importance of reskilling and upskilling, as well as strategies for adapting to the changing world of work in the age of AI. It concludes with a future outlook, exploring the likely direction of the workforce in the years to come and the importance of preparing for the future with a proactive approach to AI and the workforce. This book provides a comprehensive and accessible look at the impact of AI on the future of work. It is ideal for anyone interested in understanding the implications of AI on the workforce and preparing for the future of work in the age of AI.
Book Synopsis Intersection of AI and Business Intelligence in Data-Driven Decision-Making by : Natarajan, Arul Kumar
Download or read book Intersection of AI and Business Intelligence in Data-Driven Decision-Making written by Natarajan, Arul Kumar and published by IGI Global. This book was released on 2024-08-28 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today's rapidly evolving business landscape, organizations are inundated with vast amounts of data, making it increasingly challenging to extract meaningful insights and make informed decisions. The traditional business intelligence (BI) approach must often address the complexity and speed required for effective decision-making in this data-rich environment. As a result, many businesses need help to leverage their data to drive sustainable growth and remain competitive. Intersection of AI and Business Intelligence in Data-Driven Decision-Making presents a transformative solution to this pressing challenge. By exploring the convergence of artificial intelligence (AI) and BI, our book provides a comprehensive framework for leveraging AI-powered BI to revolutionize data analysis, predictive modeling, and decision-making processes. Readers will gain valuable insights into practical applications, emerging trends, and ethical considerations, inspiring and exciting them about the potential of AI in driving business success.
Book Synopsis Learning to Love Data Science by : Mike Barlow
Download or read book Learning to Love Data Science written by Mike Barlow and published by "O'Reilly Media, Inc.". This book was released on 2015-10-27 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: Until recently, many people thought big data was a passing fad. "Data science" was an enigmatic term. Today, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you’ll appreciate how data science is fundamentally altering our world, for better and for worse. Barlow paints a picture of the emerging data space in broad strokes. From new techniques and tools to the use of data for social good, you’ll find out how far data science reaches. With this anthology, you’ll learn how: Analysts can now get results from their data queries in near real time Indie manufacturers are blurring the lines between hardware and software Companies try to balance their desire for rapid innovation with the need to tighten data security Advanced analytics and low-cost sensors are transforming equipment maintenance from a cost center to a profit center CIOs have gradually evolved from order takers to business innovators New analytics tools let businesses go beyond data analysis and straight to decision-making Mike Barlow is an award-winning journalist, author, and communications strategy consultant. Since launching his own firm, Cumulus Partners, he has represented major organizations in a number of industries.
Book Synopsis AI-Driven Cybersecurity andThreat Intelligence by : Iqbal H. Sarker
Download or read book AI-Driven Cybersecurity andThreat Intelligence written by Iqbal H. Sarker and published by Springer Nature. This book was released on with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt:
