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When most people hear “Machine Learning,” they picture a robot: a dependable butler or a deadly Terminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s already here. In fact, it has been around for decades in some specialized applications, such as Optical Character Recognition (OCR). But the first ML application that really became mainstream, improving the lives of hundreds of millions of people, took over the world back in the 1990s: it was the spam filter. Not exactly a self-aware Skynet, but it does technically qualify as Machine Learning (it has actually learned so well that you seldom need to flag an email as spam anymore). It was followed by hundreds of ML applications that now quietly power hundreds of products and features that you use regularly, from better recommendations to voice search. Where does Machine Learning start and where does it end? What exactly does it mean for a machine to learn something? If I download a copy of Wikipedia, has my computer really “learned” something? Is it suddenly smarter? In this chapter we will start by clarifying what Machine Learning is and why you may want to use it. Then, before we set out to explore the Machine Learning continent, we will take a look at the map and learn about the main regions and the most notable landmarks: supervised versus unsupervised learning, online versus batch learning, instance-based versus model-based learning. Then we will look at the workflow of a typical ML project, discuss the main challenges you may face, and cover how to evaluate and fine-tune a Machine Learning system. This chapter introduces a lot of fundamental concepts (and jargon) that every data scientist should know by heart. It will be a high-level overview (the only chapter without much code), all rather simple, but you should make sure everything is crystal-clear to you before continuing to the rest of the book. So grab a coffee and let’s get started!

When most people hear “Machine Learning,” they picture a robot: a dependable butler or a deadly Terminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s already here. In fact, it has been around for decades in some specialized applications, such as Optical Character Recognition (OCR). But the first ML application that really became mainstream, improving the lives of hundreds of millions of people, took over the world back in the 1990s: it was the spam filter. Not exactly a self-aware Skynet, but it does technically qualify as Machine Learning (it has actually learned so well that you seldom need to flag an email as spam anymore). It was followed by hundreds of ML applications that now quietly power hundreds of products and features that you use regularly, from better recommendations to voice search. Where does Machine Learning start and where does it end? What exactly does it mean for a machine to learn something? If I download a copy of Wikipedia, has my computer really “learned” something? Is it suddenly smarter? In this chapter we will start by clarifying what Machine Learning is and why you may want to use it. Then, before we set out to explore the Machine Learning continent, we will take a look at the map and learn about the main regions and the most notable landmarks: supervised versus unsupervised learning, online versus batch learning, instance-based versus model-based learning. Then we will look at the workflow of a typical ML project, discuss the main challenges you may face, and cover how to evaluate and fine-tune a Machine Learning system. This chapter introduces a lot of fundamental concepts (and jargon) that every data scientist should know by heart. It will be a high-level overview (the only chapter without much code), all rather simple, but you should make sure everything is crystal-clear to you before continuing to the rest of the book. So grab a coffee and let’s get started!

Throughout my career, I have mentored both students and fellow employees in programming, and many of them have suggested that I write my thoughts down in book form. However, I have typically responded with the rebuttal that I felt I had nothing novel to present. Being a largely self-taught programmer, I have always been able to rattle off a long list of books from which I have derived most of my knowledge. Therefore, what could I write about that has not already been said? I came to realize, however, that the majority of books that I encounter tend to focus only on pieces of design or implementation rather than taking a holistic approach. For example, if one wants to learn the C++ language, Stroustrup [24] or Lippman and Lajoie [15] are excellent references. For learning C++ best practices, one need only read the books by Sutter [25, 26, 27], Sutter and Alexandrescu [28], or Meyers [18, 17, 19]. Of course, learning to program extends well beyond C++. For data structures and algorithms, there are always the classics by Knuth [11, 12, 13] or the more accessible and concise book by Cormen et al [6]. To learn object-oriented analysis and design, the book by Booch et al [4] is an excellent reference. Of course, design patterns can be learned from Gamma et al [7], and general programming practices can be learned from many books such as those by McConnell [16], Spinellis [23], or Kernighan and Pike [10]. Certainly, the deeper the specialty one seeks, the more esoteric the book one can find (and should eventually read). This book is not such a book. Rather, I have striven to write a book that operates from the premise that the reader already possesses a working knowledge of the information encased in works such as the aforementioned titles. In this book, I instead attempt to ground the reader’s theoretical knowledge of design through practice using a single case study.

Artificial intelligence is becoming increasingly relevant in the modern world where everything is driven by data and automation. It is used extensively across many fields such as image recognition, robotics, search engines, and self-driving cars. In this book, we will explore various real-world scenarios. We will understand what algorithms to use in a given context and write functional code using this exciting book. We will start by talking about various realms of artificial intelligence. We’ll then move on to discuss more complex algorithms, such as Extremely Random Forests, Hidden Markov Models, Genetic Algorithms, Artificial Neural Networks, and Convolutional Neural Networks, and so on. This book is for Python programmers looking to use artificial intelligence algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be helpful so you can play around with the code. It is also useful to experienced Python programmers who are looking to implement artificial intelligence techniques. You will learn how to make informed decisions about the type of algorithms you need to use and how to implement those algorithms to get the best possible results. If you want to build versatile applications that can make sense of images, text, speech, or some other form of data, this book on artificial intelligence will definitely come to your rescue! What this book covers Chapter 1, Introduction to Artificial Intelligence, teaches you various introductory concepts in artificial intelligence. It talks about applications, branches, and modeling of Artificial Intelligence. It walks the reader through the installation of necessary Python packages. Chapter 2, Classification and Regression Using Supervised Learning, covers various supervised learning techniques for classification and regression. You will learn how to analyze income data and predict housing prices. Chapter 3, Predictive Analytics with Ensemble Learning, explains predictive modeling techniques using Ensemble Learning, particularly focused on Random Forests. We will learn how to apply these techniques to predict traffic on the roads near sports stadiums. Chapter 4, Detecting Patterns with Unsupervised Learning, covers unsupervised learning algorithms including K-means and Mean Shift Clustering. We will learn how to apply these algorithms to stock market data and customer segmentation.

The Python programming language reconciles many apparent contradictions: both elegant and pragmatic, both simple and powerful, it’s very high-level yet doesn’t get in your way when you need to fiddle with bits and bytes, and it’s suitable for programming novices as well as great for experts, too. This book is aimed at programmers with some previous exposure to Python, as well as experienced programmers coming to Python for the first time from other languages. The book is a quick reference to Python itself, the most commonly used parts of its vast standard library, and a few of the most popular and useful thirdparty modules and packages, covering a wide range of application areas, including web and network programming, XML handling, database interactions, and highspeed numeric computing. The book focuses on Python’s cross-platform capabilities and covers the basics of extending Python and embedding it in other applications. How This Book Is Organized This book has five parts, as follows. Part I, Getting Started with Python Chapter 1, Introduction to Python Covers the general characteristics of the Python language, its implementations, where to get help and information, how to participate in the Python community, and how to obtain and install Python on your computer(s). Chapter 2, The Python Interpreter Covers the Python interpreter program, its command-line options, and how to use it to run Python programs and in interactive sessions. The chapter mentions text editors for editing Python programs and auxiliary programs for checking your Python sources, along with some full-fledged integrated devel

Design and develop advanced computer vision projects using OpenCV with Python About This Book Program advanced computer vision applications in Python using different features of the OpenCV library Practical end-to-end project covering an important computer vision problem All projects in the book include a step-by-step guide to create computer vision applications Who This Book Is For This book is for intermediate users of OpenCV who aim to master their skills by developing advanced practical applications. Readers are expected to be familiar with OpenCV's concepts and Python libraries. Basic knowledge of Python programming is expected and assumed. What You Will Learn Generate real-time visual effects using different filters and image manipulation techniques such as dodging and burning Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor Learn feature extraction and feature matching for tracking arbitrary objects of interest Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques Track visually salient objects by searching for and focusing on important regions of an image Detect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer peceptrons (MLPs) Recognize street signs using a multi-class adaptation of support vector machines (SVMs) Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features In Detail OpenCV is a native cross platform C++ Library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android. Developers using OpenCV build applications to process visual data; this can include live streaming data from a device like a camera, such as photographs or videos. OpenCV offers extensive libraries with over 500 functions This book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this book teach the reader how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization. By the end of this book, readers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications. All source code is available on GitHub: github.com/mbeyeler/opencv-python-blueprints. Style and approach This book covers independent hands-on projects that teach important computer vision concepts like image processing and machine learning for OpenCV with multiple examples.

Building Web Applications with Visual Studio 2017 Using .NET Core and Modern JavaScript Frameworks.pdf Learn how to build web applications from three Microsoft MVPs. After building the data application layer using Entity Framework Core and a RESTful service using ASP.NET Core, you will then build the client side web application three ways: first, using ASP.NET Core, then using Angular 2, and, finally, using React. You will be able to compare and contrast these UI frameworks and select the best one for your needs. .NET Core is a complete rewrite of the popular .NET and its related frameworks. While many concepts are similar between .NET Core and the .NET 4.6 framework, there are revolutionary changes as well, including updates to Entity Framework Core and ASP.NET Core. The first section of this book covers the three main parts of building applications with C#: Entity Framework, ASP.NET Core Services, and ASP.NET Core Web Applications. There is also an explosion in popularity of JavaScript frameworks for client side development, and the authors cover two of the most popular UI frameworks. Start with TypeScript for developing clean JavaScript, along with a client side build tool such as Gulp, Grunt, and WebPack. Using the same data access layer and RESTful service from the .NET Core application, you can rebuild the UI using Angular 2. Then, repeat the process using React, for a true comparison of building client side applications using ASP.NET Core, Angular 2, and React. What You'll Learn Understand the fundamentals of .NET Core and what that means to the traditional .NET developer Build a data access layer with Entity Framework Core, a RESTful service with ASP.NET Core MVC, and a website with ASP.NET Core MVC and Bootstrap Automate many build tasks with client side build utilities Who This Book Is For Intermediate to advanced .NET developers

Angular 4.0 updates for this book are now available. Follow the Download source code link for this book on the Apress website. Get the most from Angular 2, the leading framework for building dynamic JavaScript applications. Best-selling author Adam Freeman begins by describing the MVC pattern and the benefits it can offer and then shows you how to use Angular in your projects, starting from the nuts-and-bolts and building up to the most advanced and sophisticated features, going in-depth to give you the knowledge you need. Each topic is covered clearly and concisely and is packed with the details you need to learn to be truly effective. The most important features are given a no-nonsense in-depth treatment and chapters include common problems and details of how to avoid them. What you’ll learn Gain a solid architectural understanding of the MVC Pattern Learn how to create rich and dynamic web app clients using Angular 2 Learn how to extend and customize Angular 2 Learn how to test your Angular 2 projects Who this book is for Web developers with a foundation knowledge of HTML and JavaScript who want to create rich client-side applications.

With Learning JavaScript Design Patterns, you’ll learn how to write beautiful, structured, and maintainable JavaScript by applying classical and modern design patterns to the language. If you want to keep your code efficient, more manageable, and up-to-date with the latest best practices, this book is for you. Explore many popular design patterns, including Modules, Observers, Facades, and Mediators. Learn how modern architectural patterns—such as MVC, MVP, and MVVM—are useful from the perspective of a modern web application developer. This book also walks experienced JavaScript developers through modern module formats, how to namespace code effectively, and other essential topics. Learn the structure of design patterns and how they are written Understand different pattern categories, including creational, structural, and behavioral Walk through more than 20 classical and modern design patterns in JavaScript Use several options for writing modular code—including the Module pattern, Asyncronous Module Definition (AMD), and CommonJS Discover design patterns implemented in the jQuery library Learn popular design patterns for writing maintainable jQuery plug-ins "This book should be in every JavaScript developer’s hands. It’s the go-to book on JavaScript patterns that will be read and referenced many times in the future."—Andrée Hansson, Lead Front-End Developer, presis!

In programming, there are several problems that occur frequently. To solve these problems, there are various repeatable solutions that are known as design patterns. Design patterns are a great way to improve the efficiency of your programs and improve your productivity., This book is a collection of the most important patterns you need to improve your applications’ performance and your productivity. The journey starts by explaining the current challenges when designing and developing an application and how you can solve these challenges by applying the correct design pattern and best practices., Each pattern is accompanied with rich examples that demonstrate the power of patterns for a range of tasks, from building an application to code testing. We’ll introduce low-level programming concepts to help you write TypeScript code, as well as work with software architecture, best practices, and design aspects.

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