Central Time CT
Prerequisite: None
During the final day of the TDWI Data Science Bootcamp, you will learn how advanced modeling techniques can be layered on top of the foundational machine learning algorithms (covered on day two), and how model interpretation is extended accordingly. Demonstrations will show how to build and interpret these advanced models using actual examples.
An extensive overview of model ensembles will cover principles and practices and a variety of specific techniques. You will also be exposed to algorithms that are frequent winners of international modeling competitions such as random forests and gradient boosting machines. You will learn about sampling and re-sampling strategies, interpretation of complex models, randomization experiments to build and interpret models, and advanced feature creation approaches.
You Will Learn
- How ensembles build on foundational machine learning algorithms
- Ensemble modeling principles and best practices
- Ensemble techniques including bagging, boosting, random forests, gradient boosting machines
- An overview of deep learning techniques
- Interpretation strategies for complex supervised and unsupervised learning models
- Advanced feature creation
Geared To
- Analytics practitioners
- Data scientists
- Business analysts
- Data engineers and data pipeline developers
- Project leaders, technical managers, and engineers