GeoPandas gets more and more exciting as the day wears on :). I've just discovered how ridiculously easy it is to take a set of cities and relate them to their corresponding districts, states or provinces and then plot the outcomes. Fanfare for... the marvellous GeoPandas spatial join! Here's a quick how-to guide to setting... Continue Reading →

# GeoPandas Basics

Today's assignment - learn how GeoPandas can help me with data visualizations. Having fallen in love with Pandas this really did seem like the next logical step, and once you understand the principles behind it - which are actually quite nicely documented, then things flow quite logically. This is just the basics of course, but... Continue Reading →

# Data mapping challenge

For the past month or so I've been working away at something that in hindsight looks deceptively simple - but nonetheless taught me loads of new techniques along the way: I wanted to plot data on a map in such a way that it would reflect ratings by province, taking into account the number of... Continue Reading →

# Multivariate regression

So: with linear regression (aka simple linear regression) we have one feature which we are using to predict a dependent value (for example number of rooms as a predictor of house price). With multivariate regression (aka multiple linear regression) we simply have multiple features which could be used to predict that dependent value (for example... Continue Reading →

# Polynomial regression

Polynomial regression is a considered a special case of linear regression where higher order powers (x2, x3, etc.) of an independent variable are included. It's appropriate where your data may best be fitted to some sort of curve rather than a simple straight line. The polynomial module of numpy is easily used to explore fitting the best... Continue Reading →

# Co-variance, Correlation & Linear Regression

Typically we have 2 sets of values and we want to find out if these 2 sets of values are related, and if so how, and by how much? Could height be indicative of weight? Could hours of practice be related to how many errors are made in a mathematical test paper? Co-variance is a... Continue Reading →

# Perspectives after a no-screens break

Having just returned from one of those "no screens" holidays, I'm feeling super-balanced about life again (and also a little afraid that I may have forgotten everything I've been learning)! Being in the Addo Elephant Park reminded me keenly of my original inspirations for starting this journey - if I could do some good in... Continue Reading →

# Thoughts on strengthening machine learning in Africa

Yesterday's Deep Learning Indaba X in Cape Town was so stimulating - there is such a diversity of activity going on in this field in South Africa - wow! The final debate on what we can do to strengthen machine learning in Africa really got me thinking about the role of EDUCATION and I have... Continue Reading →

# Linear algebra – check!

It's been a quiet few weeks on this blog as I spent nearly every spare moment wrestling with the challenges of linear algebra - and I'm in a celebratory mood this afternoon having passed everything within time! I must say that, having some tentative foundational skills, I'm left with 2 main feelings: A tremendous sense of... Continue Reading →