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Machine Learning with R: Logistic Regression
Logistic regression in R programming is a classification algorithm used to find the probably of event success and event failure. This workshop will cover an introduction to logistic regression, followed by hands-on training in how to conduct a logistic regression in R, model training, testing accuracy, and how to interpret and visualize results.
Machine Learning with R: Random Forest Classification Approach
The Random Forest is a powerful algorithm used for classification in the industry. The classification algorithm consists of many decision trees to get more accurate predictions. This workshop will go over the theoretical part of Random Forest, then provide attendees with hands-on training on conducting Random Forest classification, training the data, testing accuracy, and working with tuning parameters.
Machine Learning with R: SVM Classification
Support Vector Machine (SVM) is a supervised machine learning algorithm used for both classification and regression. This workshop will only cover the theoretical and practical parts of SVM classification as SVM is mostly used in classification problems. After the introduction, participants will receive hands-on training in the implementation of SVM classification in R, as well as training and testing datasets and setting up tuning and kernal parameters.
Maintaining Your Digital Privacy
This website doesn’t track you. But most do. Learn how to maintain your privacy on a hostile internet at this workshop.
Making the Move from ArcMap to ArcGIS Pro
Are you an ArcGIS Desktop user who is curious about the differences between ArcMap and ArcGIS Pro? This workshop is geared towards existing ArcMap users who are interested in making the switch to ArcGIS Pro and will cover topics such as: the timeline of support for ArcMap, licensing and access, differences and similarities between the two programs, and how to import files from ArcMap and share your content.
Named Entity Recognition
Names are a common feature of interest when working with texts at scale. We can use a search function to locate names that we expect to find, but how do we go about searching for all names in the text – even those we do not know to look for?
The natural language processing technique of named entity recognition (NER) identifies words that may be names, places, or organizations within unstructured text. In this workshop, we will explore how NER works and apply it to a text corpus using a Python library named SpaCy.