In this sequel to An Introduction to Machine Learning with Web Data, bit.ly lead scientist Hilary Mason shows you how to solve real-world problems with machine learning. Using real data from an actual ecommerce website, you will apply production quality algorithms to understand all the issues that arise when working in a live environment.

Learn how to apply best practices to common types of machine learning problems, extract quantifiable data, and explore several open source tools and how to use them.

Segments include:

• Introduction: Discover what the course covers, and what you'll learn.
• Classification Part 1: Techniques and best practices to learn from your data.
• Classification Part 2: Learning which attributes maximize desired behavior.
• Clustering: How to explore and visualize unstructured data when your data is a mess and there's no known structure.
• Learning from Data: Best practices for offline vs stream analysis.
• Conclusions: Asking the right questions is hard. Once you've formulated the question you'll know whether your task is easy or hard.