ML and AI products aren't done when they're launched - model lifecycle management is critical to maintain ML systems in production....

Understanding machine learning system design considerations, technology selection criteria, and common tools used in the field. What's...

Getting enough relevant, cleaned data is crucial for ML models to work (although I've heard running more cycles of training and using...

We revisit the CRISP-DM process in more detail, with a deeper understanding of the roles and Agile processes that drive projects forward....

Welcome to Managing Machine Learning Projects! This begins the 2nd out of 3 courses in the AI Product Management Specialization series....

We take everything we've learned in Machine Learning for Product Managers and apply them to a real-world scenario: Building a model to...

Technical terms: Neural Networks Deep Neural Networks Deep Learning Perceptron Threshold Function Logistic Regression Activation Function...

Technical terms: Decision tree Splits Information Gain (IG) Node Leaf Depth Ensemble Model Aggregation function Bootstrap aggregating...

Technical terms: Parametric algorithm Non-parametric algorithms Linear Regression Simple linear regression Multiple linear regression...

Technical terms: Outcomes vs Outputs Mean Squared Error (MSE) Mean Absolute Error (MAE) Mean Absolute Percentage Error (MAPE) R-squared...

Technical terms covered: CRISP-DM process Features Parametric and Non-parametric Algorithms Datasets Training datasets Validation...