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Visualising Underfitting and Overfitting in High Dimensional Data

Wed Dec 20, 2017, in Machine Learning, Workshop

Visualising Underfitting and Overfitting in High Dimensional Data Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. In the previous workshop we plotted the decision boundary for under and overfitting classifiers. This is great, but very often it is impossible to visualise the data, usually because there are too many dimensions in the dataset. In thise case we need to visualise performance in another way.

Overfitting and Underfitting

Sun Nov 26, 2017, in Machine Learning, Workshop

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).

Support Vector Machines

Fri Nov 24, 2017, in Machine Learning, Workshop

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.

Linear Classification

Wed Nov 22, 2017, in Machine Learning, Workshop

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.

Logistic Regression

Wed Nov 22, 2017, in Machine Learning, Workshop

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.

Introduction to Gradient Descent

Fri Nov 17, 2017, in Machine Learning, Workshop

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.

Linear Regression

Fri Nov 17, 2017, in Machine Learning, Workshop

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?

Regression: Dealing With Outliers

Fri Nov 17, 2017, in Machine Learning, Workshop

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.

Data Cleaning Example - Loan Data

Thu Nov 16, 2017, in Machine Learning, Workshop

Data Cleaning Example - Loan Data 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.

Entropy Based Feature Selection

Thu Nov 16, 2017, in Machine Learning, Workshop

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.