Prerequisite: See below
This course can also be delivered using R.
Any team can employ machine learning to analyze data and discover powerful insights into the business. This two-day intensive course is designed to springboard teams with foundational skills in Python into applying machine learning to their business data.
The curriculum is designed specifically for any professional and does not require any previous background in advanced mathematics or statistics. Attendees build practical, actionable skills via hands-on labs using free, open-source software.
Attendees will receive a thorough introduction to state-of-the-art machine learning techniques, including how algorithms work, how to engineer features for the best predictive models, and how to tune models for optimal predictive performance.
Your Team Will Learn
- The different types of machine learning
- The two forms of supervised learning – classification and regression
- The CART classification tree algorithm
- The mathematics of classification trees
- Overfitting – the bugbear of machine learning
- The bias-variance tradeoff
- Tuning CART classification tree models
- Measuring the accuracy of your classification tree models
- Engineering predictive features for your decision tree models
- The CART regression tree algorithm
- The mathematics of regression trees
- The random forest algorithm
- Why the random forest is state-of-the-art for Production systems
- Tuning random forest models
- Additional resources for honing machine learning skills
- Business and data analysts
- BI and analytics developers and managers
- Business users
- Data Scientists
- Anyone interested in using machine learning to analyze business data
No prior skills in programming are required.
Prerequisite: Students must be familiar with Python and Jupyter notebooks or complete the pre-recorded course “Python Quick Start” prior to the class. This pre-recorded course will be made available in advance to any students who need it.
Attendees will need a laptop computer with specific software installed before the session. In advance of the class, attendees will receive detailed software download and installation instructions.