ML Simplified

How to Explain Each Core Machine Learning Model in an Interview

From Regression to Clustering to CNNs: A Brief Guide to 25+ Machine Learning Models

Photo by Google DeepMind on Unsplash

Machine learning is at the core of modern AI, powering everything from recommendation systems to self-driving cars. But behind every intelligent application lies foundational models that make it all possible. This article provides a concise yet comprehensive breakdown of key machine learning models to help you confidently ace your technical interviews.

Linear Regression

Linear Regression tries to find a relationship between independent and dependent variables by finding a “best-fitted line” that has minimal distance from all the data points using the least square method. The least square method finds a linear equation that minimizes the sum of squared residuals(SSR).

For example, the green line below is a better fit than the blue line because it has a minimal distance from all data points.

Figure 1: An Image Created By Author Using Canva.com

Lasso Regression (L1)

Lasso regression is a regularization technique to reduce overfitting by introducing some amount of bias in the model. It does this by minimizing the squared difference of residual with the addition of a penalty, where the penalty is equal to lambda times the absolute value of the slope. Lambda refers to the severity of the penalty. It works as a hyperparameter that can be changed to reduce overfitting and produce a better fit.

L1 Regularization is a preferred choice when we have a large number of features because it ignores all the variables where the slope value is much less.

Figure 3: Graph Showing Effect of Regularization On Overfitted Regression Line

Ridge Regression (L2)

Ridge regression is similar to lasso regression. The only difference between the two is the calculation of the penalty term. It adds a penalty term…