ABOUT THIS COURSE
This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).
Difficulty Level: INTERMEDIATE
Estimated Learning Time: 1 hours
SKILLS YOU WILL GAIN:
Applied Machine Learning
Data Analysis
Deep Learning
Machine Learning
Machine Learning Algorithms
INSTRUCTORS
Lawrence Carin
James L. Meriam Professor of Electrical and Computer Engineering
Electrical and Computer Engineering
David Carlson
Assistant Professor of Civil and Environmental Engineering
Civil and Environmental Engineering/Biostatistics and Bioinformatics
Timothy Dunn
Postdoctoral Associate
Department of Statistical Science; Department of Neurosurgery
Kevin Liang
PhD Candidate
Electrical and Computer Engineering Department