From Concepts to Code: Build Machine Learning Models That Solve Real Problems

Advance your machine learning journey by transforming structured data into real-world solutions. In this practical, hands-on course, you'll apply the foundational skills gained in Structuring Data for Machine Learning to develop, refine, and deploy working machine learning models. By exploring supervised, unsupervised, and semi-supervised learning techniques, you'll learn how to choose the right approach for different types of challenges and data scenarios.

This course focuses on solving actual business and organizational problems using open-source tools and publicly available datasets. You'll not only sharpen your technical skills but also build a small, professional portfolio of machine learning models you can take back to your workplace or use to showcase your capabilities.

As the second course in the Applied Machine Learning Certificate, this course equips you with the tools and confidence to move from experimentation to implementation-and to create models that drive measurable impact.

All tools and data utilized in this course are open source and freely available for use before, during, and after the course.

What You Will Learn:

• What machine learning is and how it works

• Where to find common open-source tools for machine learning

• Supervised learning and how to apply supervised learning techniques

• Unsupervised learning and how to apply unsupervised learning techniques

• Semi-supervised learning and how to apply semi-supervised learning techniques

• Understanding and avoiding overfitting and underfitting

• Bias and variance trade-off

• How to tune model performance

Earn 1.4 Continuing Education Units (CEUs) and walk away with portfolio-ready models and real-world machine learning experience.