Introduction to Gradient Descent

Published: Nov 17, 2017
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Introduction to Gradient Descent

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For only a few algorithms an analytical solution exists. For example, we can use the Normal Equation to solve a linear regression problem directly.

However, for most algorithms we rely cannot solve the problem analytically; usually because it’s impossible to solve the equation. So instead we have to try something else.

Published: Nov 16, 2017
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Correlating Data

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Correlations between features are bad because you are effectively telling the model that this information is twice more important than everything else. You’re feeding the model the same data twice.

Technically it’s known as multicollinear, which is the generalisation to any number of features that could be correlated.

Generally correlating features will decrease the performance of your model, so we need to find them and remove them.

Published: Nov 16, 2017
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Introduction to Python and Jupyter Notebooks

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This workshop is a quick introduction to using Python and Jupyter Notebooks.

Python

For most Data Science tasks there are two competing Open Source languages. R is favoured more by those with a mathematical background. Python is preferred by those with a programming background; all of my workshops are currently in Python.

Published: Nov 16, 2017
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Information and Entropy

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Remember the goal of data science. The goal is to make a decision based upon some data. The quality of that decision depends on our information. If we have good, clear information then we can make well informed decisions. If we have bad, messy data then our decisions will be poor.

Published: Nov 16, 2017
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Histograms and Inverting Skewed Data

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When we first receive some data, it can be in a mess. If we tried to force that data into a model it is more than likely that the results will be useless.

So we need to spend a significant amount of time cleaning the data. This workshop is all about bad data.

Published: Nov 16, 2017
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Entropy Based Feature Selection

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One simple way to evaluate the importance of features (something we will deal with later) is to calculate the entropy for prospective splits.

In this example, we will look at a real dataset called the “mushroom dataset”. It is a large collection of data about poisonous and edible mushrooms.

Published: Nov 16, 2017
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Data Cleaning Example - Loan Data

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A huge amount of time is spent cleaning, removing, scaling data. All in an effort to squeeze a bit more performance out of the model.

The data we are using is from Kaggle, and is available in raw from from here. You will need to sign into kaggle if you want to download the full data. I’ve included just a small sample.

Published: Nov 11, 2017
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Root Cause Analysis: The 5-Whys

Deciding what problem you should try and solve is one of the hardest steps to get right in Data Science. If you get it wrong, then you’ll spend significant amounts of time free wheeling around the rest of the data science process and end up with something that nobody wants or cares about. There is nothing worse that someone suggesting that your work has no value. The solution is to get the correct problem defined at the start.

Published: Oct 29, 2017
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Probability Distributions

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This workshop is about another way of presenting data. We can plot how frequent observations are to better characterise the data.

Imagine you had some data. For sake of example, imagine that is a measure of peoples’ height. If you measured 10 people, then you would see 10 different heights. The heights are said to be distributed along the height axis.

Published: Oct 27, 2017
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Why do we use Standard Deviation and is it Right?

It’s a fundamental question and it has knock on effects for all algorithms used within data science. But what is interesting is that there is a history. People haven’t always used variance and standard deviation as the defacto measure of spread. But first, what is it?

Standard Deviation

The Standard Deviation is used throughout statistics and data science as a measure of “spread” or “dispersion” of a feature. The standard deviation of a population is:

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