AI, Machine Learning, Reinforcement Learning, and MLOps Articles

Learn more about AI, machine learning, reinforcement learning, and MLOps with our insight-packed articles. Our AI blog delves into industrial use of AI, the machine learning blog is more technical, the reinforcement learning blog is industrially renowned, and our mlops blog discusses operational ML.

Logistic Regression

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Logistic Regression Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. I find the name logistic regression annoying. We don’t normally use logistic regression for anything other than classification; but statistics coined the name long ago. Despite the name, logistic regression is incredibly useful. Instead of optimising the error of the distance like we did in standard linear regression, we can frame the problem probabilistically.

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Linear Classification

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Linear Classification Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. We learnt that we can use a linear model (and possibly gradient descent) to fit a straight line to some data. To do this we minimised the mean-squared-error (often known as the optimisation/loss/cost function) between our prediction and the data. It’s also possible to slightly change the optimisation function to fit the line to separate classes.

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Regression: Dealing With Outliers

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Regression: Dealing with Outliers Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. Outliers are observations that are spurious. You can usually spot outliers visually; they are often far away from the rest of the observations. Sometimes they are caused by a measurement error, sometimes noise and occasionally they can be observations of interest (e.g. fraud detection). But outliers skew the estimates of the mean and standard deviation and therefore affect linear models that use error measures that assume normality (e.

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Linear Regression

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Linear Regression Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. Regression is a traditional task from statistics that attempts to fit model to some input data to predict the numerical value of an output. The data is assumed to be continuous. The goal is to be able to take a new observation and predict the output with minmal error. Some examples might be “what will next quater’s profits be?

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Introduction to Gradient Descent

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Introduction to Gradient Descent Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. 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.

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Why Correlating Data is Bad and What to do About it

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Correlating Data Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. 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.

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Introduction to Python and Jupyter Notebooks

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Introduction to Python and Jupyter Notebooks Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. 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.

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Information and Entropy

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Information and Entropy Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. 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.

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Histograms and Skewed Data

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Histograms and Inverting Skewed Data Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. 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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Entropy Based Feature Selection

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Entropy Based Feature Selection 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. Attribute Information: (classes: edible=e, poisonous=p) 1.

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