This is Day1 of my Machine Learning 10 Day Sprint.
Today’s topic: Classical Machine Learning

Core Concept: Moving from “Explicit Programming” (if/else) to “Pattern Recognition” (weights/biases).

Today’s Reading Material:

Machine Learning is Fun! Part 1 by Adam Geitgey.

Why?

It’s a legendary primer that builds the intuition of “estimation” and “cost functions” without drowning you in calculus.

Deep Dive Question:

Many developers view Classical ML as “outdated” compared to LLMs. However, they share the same DNA: Optimiazation.
As you read, focus on this: If Linear Regression is a “Global” learner (trying to fix one rule to all data) and KNN is a “Local” learner (looking only at immediate neighbors), in what specific real-world scenarios would a “Global” model fail where a “Local” one succeeds?

Notes from the Post

• Cost Function (Math that tells the model is wrong.)
• Optimizer (Math that tells how to get better.)

Takeaway from the Post

• Briefly define the difference between Regression & Classification.
• Answer the question to the Deep Dive Question above.
• Bias-Variance Tradeoff