About the Instructor
Andrew Ng
Founder of deeplearning.ai and Stanford CS229. Former Head of AI at Baidu and Google Brain. One of the most influential voices in AI education with 5M+ students worldwide.
First Principles
Supervised learning works on a simple principle: given examples of input-output pairs, find the function that maps inputs to outputs. This covers regression (predicting continuous values), classification (predicting categories), and clustering.
AI Ecosystem Fit
Layer 4 (Models) — The foundation of all predictive AI. Before building agents, you need prediction: churn prediction, demand forecasting, anomaly detection. This specialization is the baseline for all production ML systems.
Prerequisites
What Paid Projects This Unlocks
Build predictive agents: sales forecasting, customer churn prediction, anomaly detection systems. Deploy supervised learning agents.
3 Client Projects You Can Get Paid For
Real businesses have this problem right now. Build it, pitch it, charge for it.
Stack This With → (Unlocks Higher-Paying Projects)
Apply StatQuest for deeper intuition on specific algorithms
Move to Hugging Face for NLP applications
Deploy on AWS or build with LangChain
Why This Matters
Predictive models power 90% of SMB automation. Knowing how to evaluate, train, and tune models is non-negotiable for AI engineers.