deep-learning How to create a sequential model in Keras for R This tutorial will introduce the Deep Learning classification task with Keras. With focus on one-hot encoding, layer shapes, train & model evaluation.

machine learning Sample size and class balance on model performance Analyzing the relationship between the sample size and how it impacts on the accuracy in a classification model

bookdown How to self publish a book: customizing Bookdown tl;dr: This post is related to How to self-publish a book: A handy list of resources. It's centered around Bookdown and some non-standard customizations I found useful to create the Data Science

bookdown How to self-publish a book: A handy list of resources tl;dr: A list of useful resources aimed to self-publish a book on Amazon using Bookdown.

exploratory data analysis Exploratory Data Analysis in R (introduction) Exploratory data analysis (EDA) the very first step in a data project. We will create a code-template to achieve this with one function.

rstats Tutorial instalación R y RStudio Este tutorial tiene como propósito hacer el set-up inicial para empezar a desarrollar modelos machine learning en increíble lenguaje R.

machine learning Introduction to Machine Learning for non-developers About Machine Learning We all know that machine learning is about handling data, but it also can be seen as: The art of finding order in data by browsing its inner information. Some

learning "I hate math!" - Education and Artificial Intelligence to find a meaning in what we do Well, what you hate is the way that math was taught to you. That soup of equations, abstractions, and solutions to problems that we don’t know, It's hard to enjoy the things

data-science-live-book Data Science Live Book available at Amazon! Hi there! tl;dr: The Data Science Live Book is now available at Amazon! Kindle & Paperback versions! 🚀 👉 See at Amazon 📗! Link to the black & white version, also available on full-color. It

rstats Exploratory Data Analysis & Data Preparation with 'funModeling' funModeling quick-start This package contains a set of functions related to exploratory data analysis, data preparation, and model performance. It is used by people coming from business, research, and teaching (professors and students)

rstats Data discretization made easy with funModeling tl;dr: Convert numerical variables into categorical, as it is shown in the next image. ⏳ Reading time ~ 6 min. Let's start! The package funModeling (from version > 1.6.6) introduces two functions—

data science Data Science Live Book (open source) ~ new big release! 200-pages Well after some time, and +300 commits, this is the biggest release of the Data Science Live Book! (open source), after the first publication more than 1 year ago :) tl;dr: Hi there!

clustering Playing with dimensions: from Clustering, PCA, t-SNE... to Carl Sagan! Playing with dimensions Hi there! This post is an experiment combining the result of t-SNE with two well known clustering techniques: k-means and hierarchical. This will be the practical section, in R. But

data science Model Performance in Data Science Live Book Hi there! I decided to almost re-write the model validation section since it didn't reflect real case scenarios. Hopefully in the two new chapters you will gain a deeper knowledge on methodological aspects

data science Data Science Live Book - Scoring, Model Performance & profiling - Update! This update contains a new chapter -scoring- which is related to model performance and model deployment, used when predicting a binary outcome. Link to the scoring chapter. Important: To use following updates please

R Time Series Analysis Using Max/Min... and some Neuroscience. Introduction Time series have maximum and minimum points as general patterns. Sometimes the noise present on it causes problems to spot general behavior. In this post, we will smooth time series -reducing noise-

R Anomaly Detection in R Introduction Inspired by this Netflix post, I decided to write a post based on this topic using R. There are several nice packages to achieve this goal, the one we´re going to

R Text Mining Analysis: some theory and practice in R Introduction Big Data help us to analyze unstructred data (aka "text" ), with many techniques, in this post it is presented one: Cosine Similarity. There are also other analysts work, who scraped

R Recommendation Systems in R These systems are used in cross-selling industries, and they measure correlated items as well as their user rate. This last point wasn't included the apriori algorithm (or association rules), used in market basket

R {Long Vs. Wide} Data Frames Introduction This is an excellent resource to understand 2 types of data frame format: Long and Wide. Just take a look at figure 1 inside the article Long format: ggplot2 needs in certain

R Introduction to automatic machine learning Introduction "I want to develop a model that automatically learns over time", a really challenging objective. We'll develop in this post a procedure that loads data, build a model, make predictions

R Data Science - Short lesson on cluster analysis Introduction In clustering you let data to be grouped according to their similarity. A cluster model is a group of segments -clusters- containing cases (such as clients, patients, cars, etc.). Once a cluster

EU Life Quality Geo Report Living longer, living better? It's equally important to measure the longer living as well as its quality. Analyzing data from [eurostat](http://ec.europa.eu/eurostat/publications/recently-published?p_auth=ZKofrOKp&p_p_

R Dynamic analysis on outliers Treating outliers Introduction Outliers are the extreme values that a variable has, depending on the model or requirement, it could be necessary to treat them, either transforming or deleting. Variable “Income”