Monetizing Machine Learning
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| Author | : Manuel Amunategui |
| Publisher | : Apress |
| Total Pages | : 510 |
| Release | : 2018-09-12 |
| Genre | : Computers |
| ISBN | : 1484238737 |
Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book—Amazon, Microsoft, Google, and PythonAnywhere. You will work through a series of common Python data science problems in an increasing order of complexity. The practical projects presented in this book are simple, clear, and can be used as templates to jump-start many other types of projects. You will learn to create a web application around numerical or categorical predictions, understand the analysis of text, create powerful and interactive presentations, serve restricted access to data, and leverage web plugins to accept credit card payments and donations. You will get your projects into the hands of the world in no time. Each chapter follows three steps: modeling the right way, designing and developing a local web application, and deploying onto a popular and reliable serverless cloud provider. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book. What You’ll Learn Extend your machine learning models using simple techniques to create compelling and interactive web dashboards Leverage the Flask web framework for rapid prototyping of your Python models and ideasCreate dynamic content powered by regression coefficients, logistic regressions, gradient boosting machines, Bayesian classifications, and more Harness the power of TensorFlow by exporting saved models into web applications Create rich web dashboards to handle complex real-time user input with JavaScript and Ajax to yield interactive and tailored contentCreate dashboards with paywalls to offer subscription-based accessAccess API data such as Google Maps, OpenWeather, etc.Apply different approaches to make sense of text data and return customized intelligence Build an intuitive and useful recommendation site to add value to users and entice them to keep coming back Utilize the freemium offerings of Google Analytics and analyze the results Take your ideas all the way to your customer's plate using the top serverless cloud providers Who This Book Is For Those with some programming experience with Python, code editing, and access to an interpreter in working order. The book is geared toward entrepreneurs who want to get their ideas onto the web without breaking the bank, small companies without an IT staff, students wanting exposure and training, and for all data science professionals ready to take things to the next level.
| Author | : Puneet Mathur |
| Publisher | : Apress |
| Total Pages | : 384 |
| Release | : 2018-12-12 |
| Genre | : Computers |
| ISBN | : 1484237870 |
Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. What You Will LearnDiscover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas Who This Book Is For Data scientists and machine learning professionals.
| Author | : Andrew Roman Wells |
| Publisher | : John Wiley & Sons |
| Total Pages | : 371 |
| Release | : 2017-03-13 |
| Genre | : Business & Economics |
| ISBN | : 1119356245 |
Transforming data into revenue generating strategies and actions Organizations are swamped with data—collected from web traffic, point of sale systems, enterprise resource planning systems, and more, but what to do with it? Monetizing your Data provides a framework and path for business managers to convert ever-increasing volumes of data into revenue generating actions through three disciplines: decision architecture, data science, and guided analytics. There are large gaps between understanding a business problem and knowing which data is relevant to the problem and how to leverage that data to drive significant financial performance. Using a proven methodology developed in the field through delivering meaningful solutions to Fortune 500 companies, this book gives you the analytical tools, methods, and techniques to transform data you already have into information into insights that drive winning decisions. Beginning with an explanation of the analytical cycle, this book guides you through the process of developing value generating strategies that can translate into big returns. The companion website, www.monetizingyourdata.com, provides templates, checklists, and examples to help you apply the methodology in your environment, and the expert author team provides authoritative guidance every step of the way. This book shows you how to use your data to: Monetize your data to drive revenue and cut costs Connect your data to decisions that drive action and deliver value Develop analytic tools to guide managers up and down the ladder to better decisions Turning data into action is key; data can be a valuable competitive advantage, but only if you understand how to organize it, structure it, and uncover the actionable information hidden within it through decision architecture and guided analytics. From multinational corporations to single-owner small businesses, companies of every size and structure stand to benefit from these tools, methods, and techniques; Monetizing your Data walks you through the translation and transformation to help you leverage your data into value creating strategies.
| Author | : Jared Dean |
| Publisher | : John Wiley & Sons |
| Total Pages | : 293 |
| Release | : 2014-05-27 |
| Genre | : Computers |
| ISBN | : 1118618041 |
With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.
| Author | : Prashant Kikani |
| Publisher | : BPB Publications |
| Total Pages | : 170 |
| Release | : 2021-01-05 |
| Genre | : Computers |
| ISBN | : 9389898706 |
Learn AI & Machine Learning from the first principles. KEY FEATURESÊÊ _ Explore how different industries are using AI and ML for diverse use-cases. _ Learn core concepts of Data Science, Machine Learning, Deep Learning and NLP in an easy and intuitive manner. _ Cutting-edge coverage on use of ML for business products and services. _ Explore how different companies are monetizing AI and ML technologies. _ Learn how you can start your own journey in the AI field from scratch. DESCRIPTION AI and machine learning (ML) are probably the most fascinating technologies of the 21st century. AI is literally in every industry now. From medical to climate change, education to sport, finance to entertainment, AI is disrupting every industry as we know. So, the basic knowledge of AI/ML becomes mandatory for everyone. This book is your first step to start the journey in this field. Along with basic concepts of fields, like machine learning, deep learning and NLP, we will also explore how big companies are using these technologies to deliver greater user experience and earning millions of dollars in profit. Also, we will see how the owners of small- or medium-sized businesses can leverage and integrate these technologies with their products and services. Leveraging AI and ML can become that competitive moat which can differentiate the product from others. In this book, you will learn the root concepts of AI/ML and how these inanimate machines can actually become smarter than the humans at a few tasks, and how companies are using AI and how you can leverage AI to earn profits. WHAT YOU WILL LEARN Ê _ Core concepts of data science, machine learning, deep learning and NLP in simple and intuitive words. _ How you can leverage and integrate AI technologies in your business to differentiate your product in the market. _ The limitations of traditional non-tech businesses and how AI can bridge those gaps to increase revenues and decrease costs. _ How AI can help companies in launching new products, improving existing ones and automating mundane processes. _ Explore how big tech companies are using AI to automate different tasks and providing unique product experiences to their users. WHO THIS BOOK IS FORÊÊ This book is for anyone who is curious about this fascinating technology and how it really works at its core. It is also beneficial to those who want to start their career in AI/ ML. TABLE OF CONTENTSÊ 1. Introduction 2. Going deeper in ML concepts 3. Business perspective of AI 4. How to get started and pitfalls to avoid
| Author | : Clarence Chio |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 394 |
| Release | : 2018-01-26 |
| Genre | : Computers |
| ISBN | : 1491979852 |
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions
| Author | : Stephan Liozu |
| Publisher | : Ulaga & Associés |
| Total Pages | : 259 |
| Release | : 2018-10-30 |
| Genre | : Business & Economics |
| ISBN | : 1945815051 |
The Digital revolution promises trillions of dollars in created value by 2030. Consultants and researchers are projecting massive and disruptive disruption in entire industrial sectors. As a results, PwC reports in their DigitalIQ report that 73% of executives say that they are investing in internet of things (IoT) and 54% in artificial intelligence. So we are experiencing a deluge of digital investments, programs, and large-scale transformations. Despite this tsunami of activities, many IoT Initiatives stall in the Proof of Concept phase and few are already considered a success. Recently, Siemens revealed that less than a fifth (18%) of surveyed companies analyze more than 60% of production data they collect. In a similar vein, Simon-Kucher & Partners (SKP) reports that 3 out of 4 firms that invested in digitalization in the past 3 years fail in their efforts due to the lack of monetization strategies, the focus on the wrong priorities, the lack of customer intimacy, and the neglect of digital pricing best practices. In fact, only 18% of these firms are true digital heroes. Despite the high level of interest and investments, the reality is that most companies are just getting started. The digital champions are not yet reaping the fruit of their investments. Most companies tend to struggle with the process of designing digital business models, with the development of truly differentiated offers, and with the monetization and pricing of their data-based offers. This book focuses on the topics of data monetization and of the value-based pricing of data-driven offers. The authors introduces a newly-developed practical data monetization roadmap that can be used by digital project teams, incubators, and digital factories to better frame their offers and to apply the principles of value-based pricing. They present options in digital pricing models and practical guidelines on how to deploy them. Readers will learn: The various monetization and value creation models for data-enabled offers The 8 steps of the data monetization framework The best practices in designing differentiated data-enabled offers The value-based pricing of data and options in digital pricing models Business model implications of switching from ownership to consumption model
| Author | : Valliappa Lakshmanan |
| Publisher | : O'Reilly Media |
| Total Pages | : 408 |
| Release | : 2020-10-15 |
| Genre | : Computers |
| ISBN | : 1098115759 |
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
| Author | : Paul Asjes |
| Publisher | : Packt Publishing |
| Total Pages | : 182 |
| Release | : 2016-06-24 |
| Genre | : Computers |
| ISBN | : 9781786460806 |
| Author | : Carlos Andre Reis Pinheiro |
| Publisher | : SAS Institute |
| Total Pages | : 169 |
| Release | : 2021-08-06 |
| Genre | : Computers |
| ISBN | : 1953329624 |
Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.
