Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they don't teach you which charts to use according to the type of the data. In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems. Next, we provide more than 200 practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics. With this book, you 'll learn: - How to quickly create beautiful graphics using ggplot2 packages - How to properly customize and annotate the plots - Type of graphics for visualizing categorical and continuous variables - How to add automatically p-values to box plots, bar plots and alternatives - How to add marginal density plots and correlation coefficients to scatter plots - Key methods for analyzing and visualizing multivariate data - R functions and packages for plotting time series data - How to combine multiple plots on one page to create production-quality figures.

Title: R Machine Learning Essentials Author: Michele Usuelli Length: 218 pages Edition: 1 Language: English Publisher: Packt Publishing Publication Date: 2014-11-25 ISBN-10: 178398774X ISBN-13: 9781783987740 Gain quick access to the machine learning concepts and practical applications using the R development environment About This Book Build machine learning algorithms using the most powerful tools in R Identify business problems and solve them by developing effective solutions Hands-on tutorial explaining the concepts through lots of practical examples, tips and tricks Who This Book Is For If you want to learn how to develop effective machine learning solutions to your business problems in R, this book is for you. It would be helpful to have a bit of familiarity with basic object-oriented programming concepts, but no prior experience is required. In Detail R Machine Learning Essentials provides you with an introduction to machine learning with R. Machine learning finds its applications in speech recognition, search-based operations, and artificial intelligence, among other things. You will start off by getting an introduction to what machine learning is, along with some examples to demonstrate the importance in understanding the basic ideas of machine learning. This book will then introduce you to R and you will see that it is an influential programming language that aids effective machine learning. You will learn the three steps to build an effective machine learning solution, which are exploring the data, building the solution, and validating the results. The book will demonstrate each step, highlighting their purpose and explaining techniques related to them. By the end of this book, you will be able to use the machine learning techniques effectively, identify business problems, and solve them by applying appropriate solutions. Table of Contents Chapter 1. Transforming Data into Actions Chapter 2. R – A Powerful Tool for Developing Machine Learning Algorith

概率论国外教材，主要是针对概率的一些底层理论，Sigma－代数，可数，有界集合的概率，不可数无界集合概率。

CherryPy Essentials - Rapid Python Web Application Development[Packt](200703)

vr多人在线

学习Cocos2d-x的一本很好的教材，目前只有英文版 Cocos2d-x is the C++ port of the most popular open source 2D framework in the world. Cocos2d-x is an amazing open source game engine that allows developers to exploit their existing C++, Lua, and JavaScript knowledge for cross-platform deployment into iOS, Android, Windows Phone, BlackBerry, Tizen, and HTML5, saving you time, effort, and cost. You will learn the essentials of Cocos2d-x through examples, by creating a game from scratch. The book starts with adding scenes and menus to the game. Furthermore, you can learn more about the varied features such as scene transitions, game sprites, moving the spaceship, and adding audio to make your game livelier. By the end of the book, you will be able to solve problems that may arise during the entire game development phase.

普朗特的流体力学概论，英文版，大师之作，高清版本，值得珍藏！

学习Python requests（丢掉urllib2吧）最好最新的书，含pdf， epub两个格式，电脑手机上同时学习用

Essentials of Computer Architecture, 2nd Edition

Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring data sets, as well as, for building predictive models. The main parts of the book include: Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. Model validation and evaluation techniques for measuring the performance of a predictive model. Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: Covers machine learning algorithm and implementation Key mathematical concepts are presented Short, self-contained cha