Prerequisite: R programming knowledge, statistics, and exposure to the machine learning process/algorithms
Python is the one of the most popular machine learning tools in use today. This course focuses on taking concepts in machine learning and applying them in practical ways. Common algorithms such as regression, clustering, and classification are explained, applied, and evaluated using Python. Participants will complete exercises to solidify understanding and build skills with the intent of finishing the course with a toolkit that can be leveraged in building Python machine learning skills.
Attend all three days of the bootcamp to earn a TDWI certificate.
You Will Learn
- How to use linear models such as linear regression and logistic regression
- How to choose features and complete feature reduction
- How to use clustering models such as k-means
- How to do time series forecasting
- How to use classification models such as decision trees and k-nearest neighbor
- How to use ensemble methods
- How do to model validation
- Those that have foundational knowledge of Python and machine learning and are looking to expand their knowledge and apply best practices