Combine knowledge of data and project work gained in the first course Structuring Data for Machine Learning, with a new understanding of supervised, unsupervised, and semi-supervised learning to solve real-world problems. Learn to focus on solving real-world problems utilizing machine learning. Create a small portfolio of machine learning models to take back to your organization.

All tools and data utilized will be open source and freely available for use before, during, and after the course.

What You Will Learn:

• What is machine learning
• Where to find common open-source tools that can be leveraged for machine learning
• Supervised machine learning and how to apply supervised learning techniques
• Unsupervised machine learning and how to apply unsupervised learning techniques
• Semi-supervised machine learning and how to apply semi-supervised learning techniques
• Overfitting and underfitting
• Bias and variance trade-off
• Tune model performance

This course is included in the Applied Machine Learning Certificate.

Earn 1.4 Continuing Education Units (CEUs).