Welcome to Week 2 of our AI/ML study group!
This week, we're building a solid base by reviewing the Core Concepts of AI/ML.
We'll cover the essential concepts that power how models learn, including Regression and Classification (making predictions), Loss Functions (measuring model error), and the fundamentals of Optimization Algorithms (how models improve over time). These topics form the bedrock of understanding how we train machine learning models.
Don't worry if it seems like a lot; the focus is on making progress and learning together.
2.1 Regression and Classification
2.1.1 Logistic Regression | ML-005 Lecture 6 | Stanford University | Andrew Ng 01 Classification
https://www.youtube.com/watch?v=4u81xU7BIOc
2.1.2 ML Lecture 1: Regression - Case Study, Prof. Hung-Yi Lee, Optional, In Chinese
https://www.youtube.com/watch?v=fegAeph9UaA
2.1.3 ML Lecture 4: Classification Prof. Hung-Yi Lee, Optional, In Chinese
https://www.youtube.com/watch?v=fZAZUYEeIMg&list=PLJV_el3uVTsPy9oCRY30oBPNLCo89yu49&index=9&pp=iAQB
2.2 Loss Functions (Mean Squared Error, Cross-Entropy, etc)
2.2.1 Cost Function by Andrew Ng
https://www.youtube.com/watch?v=0DqnDGV_p9c
2..2.2 IBM Loss Function
IBM Topic on Loss Function Tutorials
2.3 Optimization Algorithms