Prerequisites
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 used to build Python machine learning skills.
R programming knowledge, statistics, and exposure to the machine learning process/algorithms.
Those that have foundation knowledge of Python and machine learning looking to expand their knowledge and apply best practices.
- Use linear models such as linear regression and logistic regression
- Choose features and complete feature reduction
- Use clustering models such as K-means
- Do time series forecasting
- Use classification models such as decision trees and K Nearest Neighbor
- Use ensemble methods
- Perform model validation