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SUMMARY:Machine Learning with Python and Scikit - Part 2
DESCRIPTION:Register to attend this workshop at the bottom of this page. Zoom link will be emailed after filling out the registration form. \nMachine learning (ML) methods are widely adopted by the academia and industry for applications in science\, engineering\, healthcare\, and humanities. One of the greatest advantages of ML is that they are pretty general and applicable for predictive analytics in many fields. For example\, if someone learned how to apply ML methods to classify animal images\, they can apply the same set of protocols in classifying birds or cars or motorboats. To apply ML for a specific problem\, the practitioners don’t have to go through all the complex mathematics or a lot of statistics but they need to learn the best practices to train and validate the models. \nIn this workshop\, after a brief overview\, we will focus on doing hands-on training in applying ML models on various data types including image\, text\, and time series. We will work through the use cases of classification and regression problems and discuss where to apply supervised or unsupervised methods. \nObjectives of the workshop \n\nUnderstand supervised and unsupervised methods\nDefine metrics for classification vs regression\nFind out which features are important in a given dataset\nLearn to apply ML models such as Decision Trees\, Random Forest\, and Support Vector Machines\nPerform clustering and dimensionality reductions (PCA\, t-sne\, K-means\, etc.)\nSearch the parameter space – hyperparameter optimization\n\nIf you have questions or need help\, please email Bala Desinghu. \nAmarel account: Apply here as soon as possible. You must have an Amarel account set up before the workshop. \nVPN setup: You have to be connected on Rutgers’ network or be on VPN to access Amarel resources. \nSSH setup: Windows users must install an SSH client like PuTTY or MobaXterm. Alternatively\, Windows 10 users can install the complete Windows Subsystem for Linux. \nRegister to attend the workshop:\n	Notice: JavaScript is required for this content.
URL:https://oarc.rutgers.edu/event/machine-learning-with-python-and-scikit-12-9-22/
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