Applied Machine Learning in Python (NEW)
Duration: One Day Course
Prerequisite: R programming knowledge, statistics, and exposure to the machine learning process/algorithms. You will need a laptop computer with specific software installed prior to the session. When you register for the class, you will receive detailed instructions for software download and installation.
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.
You Will Learn How To
- 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
- Those that have foundation knowledge of Python and machine learning looking to expand their knowledge and apply best practices