ASP.NET 4.5 remains Microsoft’s preferred technology for creating dynamic websites, providing developers with unrivaled power and flexibility. Pro ASP.NET 4.5 in C# is the most complete reference to ASP.NET that you will find. This comprehensively revised fifth edition will teach you everything you need to know in order to create well-designed ASP.NET websites. Beginning with core concepts the book progresses steadily through key professional skills. You’ll be shown how to query databases in detail, consider the myriad applications of XML, and step through all the considerations you need to be aware of when securing your site from intruders. Finally, you’ll consider advanced topics such as using client-side validation, jQuery and Ajax. By the time you have read this book you will have learned all the skills you need to use ASP.NET 4.5 with confidence. This book is aimed at developers with a basic understanding on the .NET Framework who want to learn how to use it in a professional environment. Migrating readers without this foundation would be well served by reading Beginning ASP.NET 4.5 in C#, also by Apress, before tackling this book.
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* Steers reader through the spectrum of ASP.NET web programming concepts.
* Developers and programmers can learn language and theory simultaneously.
* Professional ASP.NET developers and wannabes can master the core techniques to develop good coding practices to enhance their long-term skill set.
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QGIS is a user-friendly open source geographic information system (GIS) that runs on Linux, Unix, Mac OS X, and Windows. The popularity of open source geographic information systems and QGIS in particular has been growing rapidly over the last few years.
Learning QGIS Third Edition is a practical, hands-on guide updated for QGIS 2.14 that provides you with clear, step-by-step exercises to help you apply your GIS knowledge to QGIS. Through clear, practical exercises, this book will introduce you to working with QGIS quickly and painlessly.
This book takes you from installing and configuring QGIS to handling spatial data to creating great maps. You will learn how to load and visualize existing spatial data and create data from scratch. You will get to know important plugins, perform common geoprocessing and spatial analysis tasks and automate them with Processing.
We will cover how to achieve great cartographic output and print maps. Finally, you will learn how to extend QGIS using Python and even create your own plugin.
What you will learn
- Install QGIS and get familiar with the user interface
- Load vector and raster data from files, databases, and web services
- Create, visualize, and edit spatial data
- Perform geoprocessing tasks and automate them
- Create advanced cartographic outputs
- Design great print maps
- Expand QGIS using Python
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Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form – It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age.
This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy.
You’ll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.
What You Will Learn
- Understand the statistical and mathematical concepts behind Predictive Analytics algorithms and implement Predictive Analytics algorithms using Python libraries
- Analyze the result parameters arising from the implementation of Predictive Analytics algorithms
- Write Python modules/functions from scratch to execute segments or the whole of these algorithms
- Recognize and mitigate various contingencies and issues related to the implementation of Predictive Analytics algorithms
- Get to know various methods of importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and numpy
- Create dummy datasets and simple mathematical simulations using the Python numpy and pandas libraries
- Understand the best practices while handling datasets in Python and creating predictive models out of them
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Write your own Digital Image Processing programs with the use of pillow, scipy.ndimage, and matplotlib in Python 3 with Raspberry Pi 3 as the hardware platform. This concise quick-start guide provides working code examples and exercises. Learn how to interface Raspberry Pi with various image sensors.
What You’ll Learn
•Understand Raspberry Pi concepts and setup
•Understand digital image processing concepts
•Study pillow, the friendly PIL fork
•Explore scipy.ndimage and matplotlib
•Master use of the Pi camera and webcam
Who This Book Is For
Raspberry Pi and IoT enthusiasts, digital image processing enthusiasts, Python and Open Source enthusiasts and professionals
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