↓ Skip to main content
Notes

Sunday Lesson

·1 min
Illustration of a person in a blanket with a coffee mug watching a machine learning dashboard by a snowy window

After finishing the assignment late Saturday night, I took a moment to reflect on my progress. It was a valuable opportunity to document the key concepts and insights I gained from the experience.

The Machine learning journey: From Raw to Predictions
#

The two quests, or rather two parallel journeys I embarked on; while trying to solve the assignment.

  1. Classification: Predict if someone earns > 5-K ( Yes/No) [A binary decison]
  2. Regression: Predict exact property values; a continuous number.

Look at the blog I wrote here.

Act 1: Gathering the data
#

It started off with using UCIML Repo, to get the adult dataset.

from ucimlrepo import fetch_ucirepo
adult = fetch_ucirepo(name="adult")

fetch_ucirepo() is your librarian, fetching the exact dataset you need

Sample data showed the contents besides what I could gather from the official documentation.

Author
Samarthya
Engineer, learner, tinkerer. This is my trophy shelf of everyday work and leisure.

Related

Language Model

Think of a language model as a really sophisticated autocomplete system. You know how your phone suggests the next word when you’re texting? A language model works on that same principle, but at a much more advanced level.

Two Students, Two Talents: The Story of How AI Learns Language

·5 mins
Remember how we compared a language model to an advanced autocomplete system? Well, here’s where the story gets interesting. Not all language models learn the same way. Imagine two students sitting in the same classroom, both brilliant, but each taught to master language through completely different methods. One becomes an incredible writer, the other becomes an exceptional reader. This is the tale of auto-regressive and auto-encoding models.