Artificial Intelligence for Beginners: Tools to Learn Machine Learning

What really is “artificial intelligence”? “Machine learning”? Let’s cut through the meaningless buzzwords, and talk the real talk.

This class covers must-know topics for AI product managers, ML practitioners, and anyone in between that’s curious about these emerging fields. We’ll cover many topics and takeaways:

• Build a Face Emotion Classifier, to practice using ML concepts.
• What artificial intelligence is and how it relates to machine learning
• How to learn machine learning, with plenty of practice
• Practice breaking down AI products into ML problems. Great for understanding ML news.
• Practice breaking down ML problems. Great as interview practice.
• Understand taxonomies of ML problems
• ML concepts like linear regression, bias-variance, classification, regression, featurization etc.

This class involves minimal amounts of code but does not require any technical knowledge. Regardless of your background, you’ll walk away with the fundamentals for discussing, learning, and practicing machine learning.

Interested in more machine learning? Try my Computer Vision 101 (Applied ML) classes.

Interested in learning how to code? Check out my Coding 101 (Python), OOP 101 (Python), or VR101 (HTML) class.

Interested in data science? Check out my SQL 101 (Database Design) or Data 101 (Analytics) class.

Acknowledgments: B-roll in introduction from shvets productions on pexels.com

• Introduction
• Project
• What is AI? ML?
• How to Learn Machine Learning
• World's Simplest Model
• Build a Face Emotion Classifier
• How to Improve Your Model
• Better Face Emotion Classifier
• Practice: Dissecting ML Problems
• Taxonomy of ML Topics
• Practice: Dissecting AI Products
• Next Steps