Hands-on Introduction to Machine Learning using Watson Studio
Description:
Work with IBM's Watson Studio in this workshop to build, train, and test machine learning/deep learning models. Participants will be led through the following nine hands-on labs. Note, the first lab is a prerequisite for the other labs. Once Lab-1 is completed, the other labs can be done in any order.
- Lab-1 - This lab will set up the environment for the subsequent labs.
- Lab-2 - This lab will feature the Watson Studio Data Refinery to demonstrate data profiling, visualization, and data preparation.
- Lab-3 - This lab will feature the Watson Studio SPSS modeler to demonstrate visual drag and drop creation of a machine learning model.
- Lab-4 - This lab will demonstrate the exciting AutoAI capability to build and deploy an optimized model based on the Titanic data set.
- Lab-5 - This lab will use a Jupyter Notebook and the XGBoost library to apply machine learning to a classification problem in the healthcare profession. The Watson Machine Learning API will then be used to save and deploy the model.
- Lab-6 - This lab will feature Watson OpenScale. IBM Watson OpenScale is an open platform that helps remove barriers to enterprise-scale AI by supporting bias mitigation, accuracy, and explainability of outcomes among other features.
- Lab-7 - This lab will feature the Watson Studio Neural Network modeler, and Experiment Assistant to build, train, and test a Convolutional Neural Network to classify images of handwritten digits.
- Lab-8 - This lab will feature IBM's Adversarial Robustness Toolbox (ART). ART is a library dedicated to adversarial machine learning. Its purpose is to allow rapid crafting and analysis of attacks and defense methods for machine learning models. ART provides an implementation for many state-of-the-art methods for attacking and defending classifiers.