The Advanced Machine Learning training course builds upon Introduction to Machine Learning in Python to further the student's understanding of Machine Learning (ML).
The course begins by evaluating advanced techniques for managing data. Next, students will evaluate and tune models and use ensemble methods. The course concludes by examining time-series data.
This course is meant for students that have taken an introductory level ML course or possess the requisite knowledge.
Purpose
|
Learn advanced techniques for managing data and tuning models. |
Audience
|
Developers and developer teams looking to learn advanced ML techniques. |
Role
| Data Engineer - Data Scientist - Software Developer |
Skill Level
| Advanced |
Style
| Fast Track - Targeted Topic - Workshops |
Duration
| 2 Days |
Related Technologies
| Python |
Productivity Objectives
- Identify how to deal with dirty data, outliers, and time-series data.
- Evaluate, optimize, and tune your models.
- Use ensemble methods such as Random Forests, Bagging, and Boosting, as well as create your own.
- Gain additional experience with "productizing" ML models.