Hands-on data science with Python (Day 2)

Zachary Glassman demonstrates how to build intelligent business applications using machine learning, taking you through each step in developing a machine learning pipeline, from prototyping to production. You’ll explore data cleaning, feature engineering, model building and evaluation, and deployment and extend your knowledge by building two applications from real-world datasets. All work will be done in Python.

Outline

Day 1

• Recommendation engines
• Overview of data and its wrangling
• Item-item correlations and finding similar items
• User similarity and predicting user ratings
• Collaborative filtering
• 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
• Online learning