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Learn practical advice and strategies for building effective machine learning systems in this comprehensive lecture that serves as a culmination of machine learning coursework. Discover essential tips, best practices, and real-world considerations for implementing ML-based solutions, drawing from extensive industry experience and academic research. Gain valuable insights into system architecture, model selection, data preprocessing, and deployment strategies that can help bridge the gap between theoretical knowledge and practical application development.
Syllabus
Machine Learning: Lecture 28: Practical advice for building machine learning applications
Taught by
UofU Data Science