Christian Moscardi walks you through developing a machine learning pipeline, from prototyping to production, with the Jupyter platform, exploring data cleaning, feature engineering, model building and evaluation, and deployment in an industry-focused setting. Along the way, you’ll learn Jupyter best practices and the Jupyter settings and libraries that enable great visualizations.
Day 1: Recommendation engine
Overview of data and its wrangling
Item-item correlations and finding similar items
User similarity and predicting user ratings
Evaluating model performance
Day 2: Anomaly detection
Data format and goal
Limitations of time series data
Detrending and seasonality
Windowing and local scores
Setting thresholds for classification
Christian Moscardi is director of technology for the Data Incubator. Previously, Christian developed a CMS for food blogs, worked for Google, and researched and taught at Columbia. He organizes with BetaNYC, New York’s civic tech organization, and contributes to various civic data projects. His extracurricular activities include cooking, playing the piano, and exploring New York.
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