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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.
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Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos.
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.
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Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos.
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.
Read moreDeciding 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.
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Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos.
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.
Read moreIt’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?
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:
Read moreWelcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos.
This workshop is about two fundamental measures of data. I want to you start thinking about how you can best describe or summarise data. How can we best take a set of data and describe that data in as few variables as possible? These are called summary statistics because they summarise statistical data. In other words, this is your first model!
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Have you ever wondered which parts of your job you love or hate? Chances are that like most developers you love learning and new problems to solve. You hate monotony and bureaucracy.
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If you ask anyone what they think AI is, they’re probably going to talk about sci-fi. We sometimes even need to set the record straight during our AI consulting projects. Science fiction has been greatly influenced by the field of artificial intelligence, or A.I.
Probably the two most famous books about A.I. are I, Robot, released in 1950 by Isaac Asimov and 2001: A Space Odyssy, released in 1968 by Arthur C. Clarke.
I, Robot introduced the three laws of robotics. 1) A robot must not injure a human being, 2) a robot must obay the orders, except where the orders would conflict with the First Law and 3) a robot must protect its own existance as long as such protection does not conflict with the First or Second Laws.
2001: A Space Odyssey is a story about a psychopathic A.I. called HAL 9000 that intentionally tries to kill the humans on board a space station to save it’s own skin, in a sense.
But the history of AI stems back much further…
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The Hitchhiker’s Guide says the meaning of life is 42. Considering that the field of Data Science is going through a period of exponential growth it too could soon find that the meaning of an artificial life is also 42. But if you are not involved on a day-to-day basis, the expansion can seem bewildering. The story of how disparate disciplines have combined to produce Data Science is fascinating.
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