Automating Supervised Machine Learning Pipeline Development

Explore the methodologies for automating supervised machine learning pipeline development in this comprehensive 59-minute video presentation. Learn about the essence of supervised machine learning, feature handling techniques including missing value treatment, data cleaning, encoding, and normalization. Discover various correlation methods, feature engineering approaches, and the importance of human oversight in the process. Gain insights from industry experts Thom Ives and Ghaith Sankari as they break down the complete pipeline, from data collection to model deployment. Enhance your understanding of machine learning workflows and best practices to streamline your data science projects.

Syllabus

– Introduction
– The highest level
– Supervised machine learning essence
– Features - Missing values
– Features - Data cleaning perception Vs reality
– Features - Encoding
– Features - Normalize
– Correlation - Methods
– Engineering features
– Human oversight
– Complete pipeline