Robert Layton – författare
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Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media. As OSINT has applications in crime fighting, state-based intelligence, and social research, this book provides recent advances in text mining, web crawling, and other algorithms that have led to advances in methods that can largely automate this process.
The book is beneficial to both practitioners and academic researchers, with discussions of the latest advances in applications, a coherent set of methods and processes for automating OSINT, and interdisciplinary perspectives on the key problems identified within each discipline.
Drawing upon years of practical experience and using numerous examples, editors Robert Layton, Paul Watters, and a distinguished list of contributors discuss Evidence Accumulation Strategies for OSINT, Named Entity Resolution in Social Media, Analyzing Social Media Campaigns for Group Size Estimation, Surveys and qualitative techniques in OSINT, and Geospatial reasoning of open data.
Presents a coherent set of methods and processes for automating OSINT Focuses on algorithms and applications allowing the practitioner to get up and running quickly Includes fully developed case studies on the digital underground and predicting crime through OSINT Discusses the ethical considerations when using publicly available online data679 kr
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The Archaeology and Anthropology of Landscape contributes to the development of theory in archaeology and anthropology, provides new and varied case studies of landscape and environment from five continents, and raises important policy issues concerning development and the management of heritage.
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The Archaeology and Anthropology of Landscape contributes to the development of theory in archaeology and anthropology, provides new and varied case studies of landscape and environment from five continents, and raises important policy issues concerning development and the management of heritage.
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Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models.
About This Book
Use a wide variety of Python libraries for practical data mining purposes.Learn how to find, manipulate, analyze, and visualize data using Python.Step-by-step instructions on data mining techniques with Python that have real-world applications.Who This Book Is For
If you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected.
What You Will Learn
Apply data mining concepts to real-world problemsPredict the outcome of sports matches based on past resultsDetermine the author of a document based on their writing styleUse APIs to download datasets from social media and other online servicesFind and extract good features from difficult datasetsCreate models that solve real-world problemsDesign and develop data mining applications using a variety of datasetsPerform object detection in images using Deep Neural NetworksFind meaningful insights from your data through intuitive visualizationsCompute on big data, including real-time data from the internetIn Detail
This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK.
You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now.
With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations.
Style and approach
This book will be your comprehensive guide to learning the various data mining techniques and implementing them in Python. A variety of real-world datasets is used to explain data mining techniques in a very crisp and easy to understand manner.
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