Materias: “…R (Computer program language)…”

Tabla de Contenidos: “…""Feature selection""""Regularization""; ""Ridge regression""; ""Least absolute shrinkage and selection operator (lasso)""; ""Implementing regularization in R""; ""Summary""; ""Chapter 3 : Logistic Regression""; ""Classifying with linear regression""; ""Logistic regression""; ""Generalized linear models""; ""Interpreting coefficients in logistic regression""; ""Assumptions of logistic regression""; ""Maximum likelihood estimation""; ""Predicting heart disease""; ""Assessing logistic regression models""; ""Model deviance""; ""Test set performance""; ""Regularization with the lasso""…”

Materias: “…R (Computer program language)…”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Introducing the Probability Theory -- Probability distributions -- Conditional probability -- Bayesian theorem -- Marginal distribution -- Expectations and covariance -- Binomial distribution -- Beta distribution -- Gamma distribution -- Dirichlet distribution -- Wishart distribution -- Exercises -- References -- Summary -- Chapter 2: The R Environment -- Setting up the R environment and packages -- Installing R and RStudio -- Your first R program -- Managing data in R -- Data Types in R -- Data structures in R -- Importing data into R -- Slicing and dicing datasets -- Vectorized operations -- Writing R programs -- Control structures -- Functions -- Scoping rules -- Loop functions -- lapply -- sapply -- mapply -- apply -- tapply -- Data visualization -- High-level plotting functions -- Low-level plotting commands -- Interactive graphics functions -- Sampling -- Random uniform sampling from an interval -- Sampling from normal distribution -- Exercises -- References -- Summary -- Chapter 3: Introducing Bayesian Inference -- Bayesian view of uncertainty -- Choosing the right prior distribution -- Non-informative priors -- Subjective priors -- Conjugate priors -- Hierarchical priors -- Estimation of posterior distribution -- Maximum a posteriori estimation -- Laplace approximation -- Monte Carlo simulations -- Variational approximation -- Prediction of future observations -- Exercises -- References -- Summary -- Chapter 4: Machine Learning Using Bayesian Inference -- Why Bayesian inference for machine learning? …”

Materias:

Tabla de Contenidos: “…Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This All-in-One -- Book 1: Introducing R -- Book 2: Describing Data -- Book 3: Analyzing Data -- Book 4: Learning from Data -- Book 5: Harnessing R: Some Projects to Keep You Busy -- What You Can Safely Skip -- Foolish Assumptions -- Icons Used in This Book -- Beyond This Book -- Where to Go from Here -- 1 Introducing R -- Chapter 1 R: What It Does and How It Does It -- The Statistical (and Related) Ideas You Just Have to Know -- Samples and populations -- Variables: Dependent and independent -- Types of data -- A little probability -- Inferential statistics: Testing hypotheses -- Null and alternative hypotheses -- Two types of error -- Getting R -- Getting RStudio -- A Session with R -- The working directory -- Getting started -- R Functions -- User-Defined Functions -- Comments -- R Structures -- Vectors -- Numerical vectors -- Matrices -- Lists -- Data frames -- for Loops and if Statements -- Chapter 2 Working with Packages, Importing, and Exporting -- Installing Packages -- Examining Data -- Heads and tails -- Missing data -- Subsets -- R Formulas -- More Packages -- Exploring the tidyverse -- Importing and Exporting -- Spreadsheets -- CSV files -- Text files -- 2 Describing Data -- Chapter 1 Getting Graphic -- Finding Patterns -- Graphing a distribution -- Bar-hopping -- Slicing the pie -- The plot of scatter -- Of boxes and whiskers -- Doing the Basics: Base R Graphics, That Is -- Histograms -- Graph features -- Bar plots -- Pie graphs -- Dot charts -- Bar plots revisited -- Scatter plots -- A plot twist -- Scatter plot matrix -- Box plots -- Kicking It Up a Notch to ggplot2 -- Histograms -- Bar plots -- Dot charts -- Bar plots re-revisited -- Scatter plots -- About that plot twist . . . -- Scatter plot matrix -- Box plots -- Putting a Bow On It…”

Materias:

Tabla de Contenidos: “…3.4 XML Extensions and Technologies 3.5 XML and R in Practice 3.6 A Short Example JSON Document 3.7 JSON Syntax Rules 3.8 JSON and R in Practice Summary Further Reading Problems 4 XPath 4.1 XPath - a Querying Language for Web Documents 4.2 Identifying Node Sets with XPath 4.3 Extracting Node Elements Summary Further Reading Problems 5 HTTP 5.1 HTTP Fundamentals 5.2 Advanced Features of HTTP 5.3 Protocols beyond HTTP 5.4 HTTP in Action Summary Further Reading Problems 6 AJAX 6.1 JavaScript 6.2 XHR 6.3 Exploring AJAX with Web Developer Tools Summary Further Reading Problems 7 SQL and Relational Databases 7.1 Overview and Terminology 7.2 Relational Databases 7.3 SQL: a Language to Communicate with Databases 7.4 Databases in Action Summary Further Reading Problems 8 Regular Expressions and String Functions 8.1 Regular Expressions 8.2 String Processing 8.3 A Word on Character Encodings Summary Further Reading Problems Part Two A Practical Toolbox for Web Scraping and Text Mining 9 Scraping the Web 9.1 Retrieval Scenarios 9.2 Extraction Strategies 9.3 Web Scraping: Good Practice 9.4 Valuable Sources of Inspiration Summary Further Reading Problems 10 Statistical Text Processing 10.1 The running example: classifying press releases of the British government 10.2 Processing Textual Data 10.3 Supervised Learning Techniques 10.4 Unsupervised Learning Techniques Summary Further reading 11 Managing Data Projects 11.1 Interacting with the File System 11.2 Processing Multiple Documents/Links 11.3 Organizing Scraping Procedures 11.4 Executing R Scripts on a Regular Basis Part Three A Bag of Case Studies 12 Collaboration Networks in the U.S. …”

Materias:

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Dedication -- Contents -- List of Figures -- List of Tables -- Preface -- Acknowledgments -- Part I The Preliminaries -- Chapter 1 Why R? -- 1.1 Why R? -- 1.2 R Installation -- 1.3 There is Nothing such as PRACTICALS -- 1.4 Datasets in R and Internet -- 1.4.1 List of Web-sites containing DATASETS -- 1.4.2 Antique Datasets -- 1.5 http://cran.r-project.org -- 1.5.1 http://r-project.org -- 1.5.2 http://www.cran.r-project.org/web/views/ -- 1.5.3 Is subscribing to R-Mailing List useful? …”

Tabla de Contenidos: “…Using R in corpus linguistics : case studies -- 6. Next steps…”

Materias:

Materias: “…Lacordaire, R.P Biografia…”