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Western Users of SAS Software Virtual Training (WUSS Virtual) 2024

WUSS Virtual Training Machine Learning with Ryan Paul Lafler & Anna Wade

Ryan Paul Lafler and Anna Wade are giving Virtual Training on Training Applied Supervised & Unsupervised Machine Learning Algorithms on July 17th, 2024. The training seminar is 4-hours in duration from 10:00am to 2:00pm PST.

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This Virtual seminar covers supervised algorithms including Decision Trees and Random Forest for Regression and Multi-Class Classification and unsupervised algorithms geared towards Clustering, Anomaly Detection, and Dimensionality Reduction. Algorithms are developed and trained entirely in Python using its many Open-Source libraries and packages.

 

Attendees can register for the Machine Learning Virtual Training Seminar using the button below!

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Virtual Training
(July 17th, 2024)

Applying Machine Learning Algorithms to Real-World Data with Python: Programming by Example
Ryan Paul Lafler; Anna Wade
2024

This virtual half-day course is open to all data scientists, statistical programmers, software engineers, researchers, project managers, and Machine Learning enthusiasts searching for an example-oriented training seminar incorporating supervised and unsupervised Machine Learning algorithms to: ~ Confidently work with both labeled (tagged) and unlabeled (raw) data, ~ Automate classification and regression tasks for Artificial Intelligence workflows, ~ Mine real-world data to uncover relationships between features, ~ Perform clustering and dimensionality reduction on unlabeled data, ~ Optimize, evaluate, and measure the performance of Machine Learning algorithms using Python’s Scikit-Learn library. Several supervised and unsupervised Machine Learning algorithms will be thoroughly discussed, programmed, and fine-tuned using Python, including: ~ Decision Trees for Multi-Class Classification and Non-Linear Regression, ~ Random Forest and Gradient-Boosting Ensemble Methods, ~ Clustering strategies for Observation Segmentation and Anomaly Detection, ~ Dimensionality Reduction (and Manifold Learning) techniques to reduce the complexity of Big Data. By enrolling in this course, attendees receive the documented Python code, their personal copies of the PDF version of the slides, and the confidence to implement supervised and unsupervised Machine Learning algorithms in their organizations.

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