This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens.
Learn best practices for building bots by focusing on the technological implementation and UX in this practical book. You will cover key topics such as setting up a development environment for creating chatbots for multiple channels (Facebook Messenger, Skype, and KiK); building a chatbot (design to implementation); integrating to IFTT (If This Then That) and IoT (Internet of Things); carrying out analytics and metrics for chatbots; and most importantly monetizing models and business sense for chatbots. Build Better Chatbots is easy to follow with code snippets provided in the book and complete code open sourced and available to download. With Facebook opening up its Messenger platform for developers, followed by Microsoft opening up Skype for development, a new channel has emerged for brands to acquire, engage, and service customers on chat with chatbots. What You Will Learn Work with the bot development life cycle Master bot UX design Integrate into the bot ecosystem Maximize the business and monetization potential for bots Who This Book Is For Developers, programmers, and hobbyists who have basic programming knowledge. The book can be used by existing chatbot developers to gain a better understanding of analytics and the business side of bots.
Data Scientisten (m/w) sind derzeit auf dem Jobmarkt heißbegehrt. In Amerika sind erfahrene Data Scientisten so beliebt wie eine Getränkebude in der Wüste. Aber auch in Deutschland ist eine steigende Nachfrage nach diesem Skillprofil erkennbar. Immer mehr Unternehmen bauen ´´Analytics´´-Abteilungen auf bzw. aus und suchen entsprechende Mitarbeiter. Nur: was macht eigentlich ein Data Scientist? Irgendetwas mit künstlicher Intelligenz, Machine Learning, Data-Mining, Python-Programmierung und Big Data. So genau weiß es eigentlich niemand ... Das Buch ist eine Einführung und Übersicht über das weitumfassende Themengebiet Data Science. Es werden die Datenquellen (Datenbanken, Data-Warehouse, Hadoop etc.) und die Softwareprodukte für die Datenanalyse vorgestellt (Data-Science-Plattformen, ML Bibliotheken). Die wichtigsten Verfahren des Machine Learnings werden ebenso behandelt wie beispielhafte Anwendungsfälle aus verschiedenen Branchen.
This book focuses on video-based, corneal-reflection eye trackers - the most widely available and affordable type of system, and takes a look at a number of interesting and challenging applications in human factors, collaborative systems, virtual reality, marketing and advertising. The third edition has been extensively revised and extended, and includes new chapters on calibration accuracy, precision and correction; advanced eye movement analysis; binocular eye movement analysis; practical gaze analytics; design; GIS. Opening with useful background information, including an introduction to the human visual system and key issues in visual perception and eye movement, the author then surveys eye-tracking devices and provides a detailed introduction to the technical requirements necessary for installing a system and developing an application program.
Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using this book, you´ll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment alternatives), and even games. Up until very recently, Python was mostly regarded as just a web scripting language. Well, computational scientists and engineers have recently discovered the flexibility and power of Python to do more. Big data analytics and cloud computing programmers are seeing Python´s immense use. Financial engineers are also now employing Python in their work. Python seems to be evolving as a language that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical computations.