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.
- 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.