Supervised Machine Learning: Concepts I Finally Understand

#machinelearning #python #coursera #ai

Hi, I'm Anshika — a B.Tech student diving into the world of AI and Machine Learning.

I just completed Andrew Ng’s Supervised Machine Learning course on Coursera (the first in the ML Specialization by DeepLearning.AI), and I wanted to document my learnings, struggles, and next steps as I begin my ML journey.

What is Supervised Machine Learning?

Supervised ML is about teaching machines using labeled data.

You provide inputs (features) along with the correct outputs (labels), and the model learns to predict outputs for new, unseen inputs.

There are two key types:

Regression → Predict continuous values (e.g., house price, traffic speed)
Classification → Predict categories (e.g., spam vs. not spam)

Key Concepts I Learned

Linear Regression (with one and multiple variables)
Gradient Descent – how the model "learns"
Cost Function (Mean Squared Error) – measuring how wrong the model is
Logistic Regression – used for binary classification problems
Overfitting vs. Underfitting – finding the balance between simplicity and accuracy
Regularization (L2) – prevents the model from overfitting the training data