## t-Distributed Stochastic Neighbor Embedding

Visualising high-dimensional datasets

Visualising high-dimensional datasets

Summarising data using fewer features

Are you Frequently A / B testing?

The game of life

How to show off your random forest

The dangers of cherry picking evidence

How to tidy up multiple if and else if statements

Easy date aggregations

The prize winning, scalable, portable and distributed Gradient Boosting algorithm

Back to basics with Artificial Neural Networks

A supervised learning method motivated by an analogy to biological evolution

Letting testthat do the heavy lifting

Advanced Regression techniques for predicting house prices

Using Spark and R for regression of concrete strength

Quick and easy classification

Forecasting regime changes in market turbulence

Complex decisions made simple

Mapping obesity in the UK

My data science journey is a Markov Chain

Combining objects as data_frames in purrr

A simple non-linear classifier using nearest-neighbour averaging

Predicting survival of passengers on the Titanic

Linear models and least squares

Working with time series data and basic forecasting

Student attainment prediction with neural networks

What happens when Hadley reads your blog

Using dplyr, broom, and purrr to make life easy

Evaluating performance of supervised learning tools

Predicting forest fire scale using support vector machines

Predicting student end of year performance using logistic regression

Using character matching for quick lookups

Predicting student performance using CART

A year's data collected with a simple LDR based light sensor

Setting up minibian

A low cost Wi-Fi serial module

A year of measurements

Home monitoring with Raspberry Pis

A simple implementation

Writing papers for peer review with knitr

Making bubble maps in R

Using classes in R

Using R package glmnet for regularisation

Adding temperature monitoring to the equation

Using an arduino for environmental sensing

Adding regularisation to vectorised linear regression

Do Londoners and New Yorkers disagree?

Some lessons learned the hard way

Simple multiclass classification

Implementing regularisation and feature mapping

Comparing vectorised methods with general linear models

Feature scaling and gradient descent

Linear regression the machine learning way

Implementing gradient decsent in R

Blogger, WordPress, Jekyll?

ggvis workshop at LondonR by Aimee Gott

Using the Google routing API via ggmap

Gender differences in the New York cycle hire data