ML Foundations for AI Engineers

This 35-minute video tutorial provides essential machine learning foundations for AI engineers who need to overcome technical barriers in their development work. Learn the three fundamental ways computers can learn: traditional machine learning (including training and inference phases), deep learning (with detailed explanations of neural networks and their training), and reinforcement learning (exploring its promise and mechanisms). The content progresses logically through each approach, emphasizing the critical importance of data in all AI systems, and concludes with practical takeaways for implementation. Access additional resources including "30 AI Projects You Can Build This Weekend" through the provided link, and find more in-depth information in the accompanying Medium article. Perfect for engineers who want to understand the technical underpinnings of modern AI without getting lost in theoretical complexities.

Syllabus

Introduction - 0:00
Intelligence & Models - 0:40
3 Ways Computers Can Learn - 1:50
Way 1: Machine Learning - 2:47
Inference Phase 2 - 3:36
Training Phase 1 - 4:27
More ML Techniques - 9:07
Way 2: Deep Learning - 10:43
Neural Networks - 12:06
Training Neural Nets - 15:29
Way 3: Reinforcement Learning RL - 21:56
The Promise of RL - 23:25
How RL Works - 25:16
Data most important part! - 30:30
Key Takeaways - 33:32