What do you mean by monitoring? Why do you need it? What are the real needs and are you monitoring them? Ask yourself these questions. Can you answer them? If not, you’re probably doing monitoring wrong.
This post asks the basic question. What is monitoring? How does it compare to logging and tracing? Let’s find out.
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Underfitting and Overfitting Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. Imagine you had developed a model that predicts some output. The goal of any model is to generate a correct prediction and avoid incorrect predictions. But how can we be sure that predictions are as good as they can possibly be? Now constrain your imagining to a classification task (other tasks have similar properties but I find classification easiest to reason about).
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Support Vector Machines Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. If you remember from the video training, SVMs are classifiers that attemt to maximise the separation between classes, no matter what the distribution of the data. This means that they can sometimes fit noise more than they fit the data. But because they are aiming to separate classes, they do a really good job at optimising for accuracy.
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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 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 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 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 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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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.
Read moreIntroduction 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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