Data Science 🐍 Regression

The video tutorial covers various regression models, starting with linear regression using Numpy and stats models, followed by polynomial and nonlinear regression techniques. It introduces machine learning models like K nearest neighbors and support vector regressors using Scikit-learn. The tutorial also explores deep learning with Gecko and neural networks. A hands-on activity with TCC lab demonstrates practical regression analysis.

What is the basic form of a linear regression model?

How do you minimize the sum of squared errors in a linear model?

What are some common types of models used in machine learning?

What is the significance of the R-squared value in regression analysis?

Explain the difference between linear regression and polynomial regression.

What are the implications of overfitting in polynomial regression?

What is the purpose of using the Scipy optimize curve fit in nonlinear regression?

What are some common errors encountered when performing nonlinear regression?

Describe how a neural network can be used for regression tasks.

How can you evaluate the performance of a regression model?