Machine Learning with Python
ODSC West
By completing this hands-on workshop, you will develop an understanding of machine learning concepts and methodologies and learn how to fit, tune, and evaluate the predictive performance of a variety of parametric and non-parametric models for classification and regression. You will become familiar with how to preprocess data, build, tune, and cross-validate predictive models, and make predictions with the models in Python.
Visualization in Bayesian Workflow
IEEE VIS
Visualization can be a powerful tool to help you build better statistical models. In this tutorial, you will learn how to create and interpret visualizations that are useful in each step of a Bayesian workflow for three common regression models, linear, logistic, and multilevel.
Visualizing and Analyzing Networks
ODSC West
By completing this tutorial, you will develop an understanding of some basic properties of social networks, including how to calculate network statistics, how to visualize networks, and how to incorporate network characteristics into your statistical models. You will become familiar with how to compute network statistics and perform statistical modeling in Python or R, as well as how to create interactive visualizations of networks in Javascript.
Machine Learning with Python
ML Week
By completing this hands-on workshop, you will develop an understanding of machine learning concepts and methodologies and learn how to fit, tune, and evaluate the predictive performance of a variety of parametric and non-parametric models for classification and regression. You will become familiar with how to preprocess data, build, tune, and cross-validate predictive models, and make predictions with the models in Python.