Python Machine Learning By Example

Author: Yuxi (Hayden) Liu

Publisher: Packt Publishing Ltd

ISBN: 178355312X

Category: Computers

Page: 254

View: 548

Take tiny steps to enter the big world of data science through this interesting guide About This Book Learn the fundamentals of machine learning and build your own intelligent applications Master the art of building your own machine learning systems with this example-based practical guide Work with important classification and regression algorithms and other machine learning techniques Who This Book Is For This book is for anyone interested in entering the data science stream with machine learning. Basic familiarity with Python is assumed. What You Will Learn Exploit the power of Python to handle data extraction, manipulation, and exploration techniques Use Python to visualize data spread across multiple dimensions and extract useful features Dive deep into the world of analytics to predict situations correctly Implement machine learning classification and regression algorithms from scratch in Python Be amazed to see the algorithms in action Evaluate the performance of a machine learning model and optimize it Solve interesting real-world problems using machine learning and Python as the journey unfolds In Detail Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal. Style and approach This book is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem, and involves hands-on work—giving you a deep insight into the world of machine learning. With simple yet rich language—Python—you will understand and be able to implement the examples with ease.
Posted in Computers

Large Scale Machine Learning with Python

Author: Bastiaan Sjardin,Luca Massaron,Alberto Boschetti

Publisher: Packt Publishing Ltd

ISBN: 1785888021

Category: Computers

Page: 420

View: 1878

Learn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. What You Will Learn Apply the most scalable machine learning algorithms Work with modern state-of-the-art large-scale machine learning techniques Increase predictive accuracy with deep learning and scalable data-handling techniques Improve your work by combining the MapReduce framework with Spark Build powerful ensembles at scale Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine In Detail Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. Style and Approach This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly. Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.
Posted in Computers

Advanced Machine Learning with Python

Author: John Hearty

Publisher: Packt Publishing Ltd

ISBN: 1784393835

Category: Computers

Page: 278

View: 4205

Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.
Posted in Computers

Machine Learning With Go

Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

Author: Daniel Whitenack

Publisher: Packt Publishing Ltd

ISBN: 1785883909

Category: Computers

Page: 304

View: 9223

Build simple, maintainable, and easy to deploy machine learning applications. About This Book Build simple, but powerful, machine learning applications that leverage Go's standard library along with popular Go packages. Learn the statistics, algorithms, and techniques needed to successfully implement machine learning in Go Understand when and how to integrate certain types of machine learning model in Go applications. Who This Book Is For This book is for Go developers who are familiar with the Go syntax and can develop, build, and run basic Go programs. If you want to explore the field of machine learning and you love Go, then this book is for you! Machine Learning with Go will give readers the practical skills to perform the most common machine learning tasks with Go. Familiarity with some statistics and math topics is necessary. What You Will Learn Learn about data gathering, organization, parsing, and cleaning. Explore matrices, linear algebra, statistics, and probability. See how to evaluate and validate models. Look at regression, classification, clustering. Learn about neural networks and deep learning Utilize times series models and anomaly detection. Get to grip with techniques for deploying and distributing analyses and models. Optimize machine learning workflow techniques In Detail The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios. Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages. Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations. Style and approach This book connects the fundamental, theoretical concepts behind Machine Learning to practical implementations using the Go programming language.
Posted in Computers

Deep Learning with Python

Author: Francois Chollet

Publisher: Manning Publications

ISBN: 9781617294433

Category: Machine learning

Page: 384

View: 6934

Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning--a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author Fran�ois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Posted in Machine learning

Kali Linux Cookbook

Author: Willie L. Pritchett,David De Smet

Publisher: Packt Publishing Ltd

ISBN: 1783289600

Category: Computers

Page: 336

View: 9965

A practical, cookbook style with numerous chapters and recipes explaining the penetration testing. The cookbook-style recipes allow you to go directly to your topic of interest if you are an expert using this book as a reference, or to follow topics throughout a chapter to gain in-depth knowledge if you are a beginner.This book is ideal for anyone who wants to get up to speed with Kali Linux. It would also be an ideal book to use as a reference for seasoned penetration testers.
Posted in Computers

Python Machine Learning

Author: Sebastian Raschka

Publisher: Packt Publishing Ltd

ISBN: 1783555149

Category: Computers

Page: 454

View: 7274

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Posted in Computers

Machine Learning in Action

Author: Peter Harrington

Publisher: Manning Publications

ISBN: 9781617290183

Category: Computers

Page: 354

View: 9462

Provides information on the concepts of machine theory, covering such topics as statistical data processing, data visualization, and forecasting.
Posted in Computers

Building Machine Learning Systems with Python - Second Edition

Author: Luis Pedro Coelho,Willi Richert

Publisher: Packt Publishing Ltd

ISBN: 178439288X

Category: Computers

Page: 326

View: 7024

This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.
Posted in Computers

Python: Deeper Insights into Machine Learning

Author: Sebastian Raschka,David Julian,John Hearty

Publisher: Packt Publishing Ltd

ISBN: 1787128547

Category: Computers

Page: 901

View: 6639

Leverage benefits of machine learning techniques using Python About This Book Improve and optimise machine learning systems using effective strategies. Develop a strategy to deal with a large amount of data. Use of Python code for implementing a range of machine learning algorithms and techniques. Who This Book Is For This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts. What You Will Learn Learn to write clean and elegant Python code that will optimize the strength of your algorithms Uncover hidden patterns and structures in data with clustering Improve accuracy and consistency of results using powerful feature engineering techniques Gain practical and theoretical understanding of cutting-edge deep learning algorithms Solve unique tasks by building models Get grips on the machine learning design process In Detail Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems. The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it's time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering. Style and approach This course includes all the resources that will help you jump into the data science field with Python. The aim is to walk through the elements of Python covering powerful machine learning libraries. This course will explain important machine learning models in a step-by-step manner. Each topic is well explained with real-world applications with detailed guidance.Through this comprehensive guide, you will be able to explore machine learning techniques.
Posted in Computers

Node.js 6.x Blueprints

Author: Fernando Monteiro

Publisher: Packt Publishing Ltd

ISBN: 1785887920

Category: Computers

Page: 376

View: 310

Create stunning web applications and Restful APIs from start to finish with Express, Loopback, MongoDB, and MySQL using this definitive guide About This Book Create stunning applications with Node.js from scratch, no matter the goal Discover a wide variety of applications you can effectively integrate third-party libraries and other front-end frameworks with Node.js Leverage all the new features introduced in Node.js 6.X Who This Book Is For This book caters to developers who are looking to build projects right from a simple website to more complex one such as a blog, chat application, a photography site, and a lot more. A Basic knowledge of JavaScript and Node.js would be extremely beneficial. What You Will Learn Explore MVC design pattern in Node.js Build solid architectures by following test-driven development Look beyond web applications and create your own desktop app with Node.js Develop single page applications using Node.js with the RESTful APIs, MongoDB, and ORM Master the Express framework and build a complete application with a real database Create a real-time and fully functional online chat application with Socket.IO In Detail Node.js is the most popular framework to create server-side applications today. Be it web, desktop, or mobile, Node.js comes to your rescue to create stunning real-time applications. Node.js 6.x Blueprints will teach you to build these types of projects in an easy-to-understand manner. The key to any Node.js project is a strong foundation on the concepts that will be a part of every project. The book will first teach you the MVC design pattern while developing a Twitter-like application using Express.js. In the next chapters, you will learn to create a website and applications such as streaming, photography, and a store locator using MongoDB, MySQL, and Firebase. Once you're warmed up, we'll move on to more complex projects such as a consumer feedback app, a real-time chat app, and a blog using Node.js with frameworks such as loopback.io and socket.io. Finally, we'll explore front-end build processes, Docker, and continuous delivery. By the end of book, you will be comfortable working with Node.js applications and will know the best tools and frameworks to build highly scalable desktop and cloud applications. Style and approach This is an easy-to-follow guide full of practical examples. This book takes a project-based approach and each chapter contains step-by-step instructions to create stunning applications across different application domains from scratch.
Posted in Computers

Algorithms of the Intelligent Web

Author: Douglas G McIlwraith,Haralambos Marmanis,Dmitry Babenko

Publisher: Manning Publications

ISBN: 9781617292583

Category: Computers

Page: 240

View: 8074

Summary Algorithms of the Intelligent Web, Second Edition teaches the most important approaches to algorithmic web data analysis, enabling you to create your own machine learning applications that crunch, munge, and wrangle data collected from users, web applications, sensors and website logs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Valuable insights are buried in the tracks web users leave as they navigate pages and applications. You can uncover them by using intelligent algorithms like the ones that have earned Facebook, Google, and Twitter a place among the giants of web data pattern extraction. About the Book Algorithms of the Intelligent Web, Second Edition teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs. In this totally revised edition, you'll look at intelligent algorithms that extract real value from data. Key machine learning concepts are explained with code examples in Python's scikit-learn. This book guides you through algorithms to capture, store, and structure data streams coming from the web. You'll explore recommendation engines and dive into classification via statistical algorithms, neural networks, and deep learning. What's Inside Introduction to machine learning Extracting structure from data Deep learning and neural networks How recommendation engines work About the Reader Knowledge of Python is assumed. About the Authors Douglas McIlwraith is a machine learning expert and data science practitioner in the field of online advertising. Dr. Haralambos Marmanis is a pioneer in the adoption of machine learning techniques for industrial solutions. Dmitry Babenko designs applications for banking, insurance, and supply-chain management. Foreword by Yike Guo. Table of Contents Building applications for the intelligent web Extracting structure from data: clustering and transforming your data Recommending relevant content Classification: placing things where they belong Case study: click prediction for online advertising Deep learning and neural networks Making the right choice The future of the intelligent web Appendix - Capturing data on the web
Posted in Computers

The Master Algorithm

How the Quest for the Ultimate Learning Machine Will Remake Our World

Author: Pedro Domingos

Publisher: Basic Books

ISBN: 0465061923

Category: Computers

Page: 352

View: 5404

"Wonderfully erudite, humorous, and easy to read." --KDNuggets In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner-the Master Algorithm-and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
Posted in Computers

Machine Learning for OpenCV

Author: Michael Beyeler

Publisher: Packt Publishing Ltd

ISBN: 178398029X

Category: Computers

Page: 382

View: 1680

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book Load, store, edit, and visualize data using OpenCV and Python Grasp the fundamental concepts of classification, regression, and clustering Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide Evaluate, compare, and choose the right algorithm for any task Who This Book Is For This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks. What You Will Learn Explore and make effective use of OpenCV's machine learning module Learn deep learning for computer vision with Python Master linear regression and regularization techniques Classify objects such as flower species, handwritten digits, and pedestrians Explore the effective use of support vector machines, boosted decision trees, and random forests Get acquainted with neural networks and Deep Learning to address real-world problems Discover hidden structures in your data using k-means clustering Get to grips with data pre-processing and feature engineering In Detail Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google's DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch! Style and approach OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.
Posted in Computers

R Data Mining Blueprints

Author: Pradeepta Mishra

Publisher: Packt Publishing Ltd

ISBN: 1783989696

Category: Computers

Page: 260

View: 507

Learn about data mining with real-world datasets About This Book Diverse real-world datasets to teach data mining techniques Practical and focused on real-world data mining cases, this book covers concepts such as spatial data mining, text mining, social media mining, and web mining Real-world case studies illustrate various data mining techniques, taking you from novice to intermediate Who This Book Is For Data analysts from beginner to intermediate level who need a step-by-step helping hand in developing complex data mining projects are the ideal audience for this book. They should have prior knowledge of basic statistics and little bit of programming language experience in any tool or platform. What You Will Learn Make use of statistics and programming to learn data mining concepts and its applications Use R Programming to apply statistical models on data Create predictive models to be applied for performing classification, prediction and recommendation Use of various libraries available on R CRAN (comprehensive R archives network) in data mining Apply data management steps in handling large datasets Learn various data visualization libraries available in R for representing data Implement various dimension reduction techniques to handle large datasets Acquire knowledge about neural network concept drawn from computer science and its applications in data mining In Detail The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis. It enables you to create high-level graphics and offers an interface to other languages. This means R is best suited to produce data and visual analytics through customization scripts and commands, instead of the typical statistical tools that provide tick boxes and drop-down menus for users. This book explores data mining techniques and shows you how to apply different mining concepts to various statistical and data applications in a wide range of fields. We will teach you about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. It will help you complete complex data mining cases and guide you through handling issues you might encounter during projects. Style and approach This fast-paced guide will help you solve predictive modeling problems using the most popular data mining algorithms through simple, practical cases.
Posted in Computers

Learning OpenCV 3

Computer Vision in C++ with the OpenCV Library

Author: Adrian Kaehler,Gary Bradski

Publisher: "O'Reilly Media, Inc."

ISBN: 1491937963

Category: COMPUTERS

Page: 1024

View: 3513

Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You’ll learn what it takes to build applications that enable computers to "see" and make decisions based on that data. With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you’ve learned. This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision. Learn OpenCV data types, array types, and array operations Capture and store still and video images with HighGUI Transform images to stretch, shrink, warp, remap, and repair Explore pattern recognition, including face detection Track objects and motion through the visual field Reconstruct 3D images from stereo vision Discover basic and advanced machine learning techniques in OpenCV
Posted in COMPUTERS

Data Analytics and Decision Support for Cybersecurity

Trends, Methodologies and Applications

Author: Iván Palomares Carrascosa,Harsha Kumara Kalutarage,Yan Huang

Publisher: Springer

ISBN: 3319594397

Category: Computers

Page: 270

View: 9133

The book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science methods, has resulted in a higher demand for effective data-driven models that support decision-making at a strategic level. This motivates the need for defining novel data analytics and decision support approaches in a myriad of real-life scenarios and problems, with Cybersecurity-related domains being no exception. This contributed volume comprises nine chapters, written by leading international researchers, covering a compilation of recent advances in Cybersecurity-related applications of data analytics and decision support approaches. In addition to theoretical studies and overviews of existing relevant literature, this book comprises a selection of application-oriented research contributions. The investigations undertaken across these chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection, Run-Time Malware Detection, Intrusion Detection, E-Learning, Online Examinations, Cybersecurity noisy data removal, Secure Smart Power Systems, Security Visualization and Monitoring. Researchers and professionals alike will find the chapters an essential read for further research on the topic.
Posted in Computers

The Art of Game Design

A Book of Lenses, Second Edition

Author: Jesse Schell

Publisher: CRC Press

ISBN: 1498759564

Category: Computers

Page: 600

View: 5542

Good game design happens when you view your game from as many perspectives as possible. Written by one of the world's top game designers, The Art of Game Design presents 100+ sets of questions, or different lenses, for viewing a game’s design, encompassing diverse fields such as psychology, architecture, music, visual design, film, software engineering, theme park design, mathematics, puzzle design, and anthropology. This Second Edition of a Game Developer Front Line Award winner: Describes the deepest and most fundamental principles of game design Demonstrates how tactics used in board, card, and athletic games also work in top-quality video games Contains valuable insight from Jesse Schell, the former chair of the International Game Developers Association and award-winning designer of Disney online games The Art of Game Design, Second Edition gives readers useful perspectives on how to make better game designs faster. It provides practical instruction on creating world-class games that will be played again and again.
Posted in Computers

Learning Alteryx

A beginner's guide to using Alteryx for self-service analytics and business intelligence

Author: Renato Baruti

Publisher: Packt Publishing Ltd

ISBN: 1788398688

Category: Computers

Page: 228

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Implement your Business Intelligence solutions without any coding - by leveraging the power of the Alteryx platform About This Book Experience the power of codeless analytics using Alteryx, a leading Business Intelligence tool Uncover hidden trends and valuable insights from your data across different sources and make accurate predictions Includes real-world examples to put your understanding of the features in Alteryx to practical use Who This Book Is For This book is for aspiring data professionals who want to learn and implement self-service analytics from scratch, without any coding. Those who have some experience with Alteryx and want to gain more proficiency will also find this book to be useful. A basic understanding of the data science concepts is all you need to get started with this book. What You Will Learn Create efficient workflows with Alteryx to answer complex business questions Learn how to speed up the cleansing, data preparing, and shaping process Blend and join data into a single dataset for self-service analysis Write advanced expressions in Alteryx leading to an optimal workflow for efficient processing of huge data Develop high-quality, data-driven reports to improve consistency in reporting and analysis Explore the flexibility of macros by automating analytic processes Apply predictive analytics from spatial, demographic, and behavioral analysis and quickly publish, schedule Share your workflows and insights with relevant stakeholders In Detail Alteryx, as a leading data blending and advanced data analytics platform, has taken self-service data analytics to the next level. Companies worldwide often find themselves struggling to prepare and blend massive datasets that are time-consuming for analysts. Alteryx solves these problems with a repeatable workflow designed to quickly clean, prepare, blend, and join your data in a seamless manner. This book will set you on a self-service data analytics journey that will help you create efficient workflows using Alteryx, without any coding involved. It will empower you and your organization to take well-informed decisions with the help of deeper business insights from the data.Starting with the fundamentals of using Alteryx such as data preparation and blending, you will delve into the more advanced concepts such as performing predictive analytics. You will also learn how to use Alteryx's features to share the insights gained with the relevant decision makers. To ensure consistency, we will be using data from the Healthcare domain throughout this book. The knowledge you gain from this book will guide you to solve real-life problems related to Business Intelligence confidently. Whether you are a novice with Alteryx or an experienced data analyst keen to explore Alteryx's self-service analytics features, this book will be the perfect companion for you. Style and approach Comprehensive, step by step guide filled with real-world examples to step through the complex business questions using one of the leading data analytics platform.
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