Regression: A Journey Towards Truth

1. 1Apply linear regression to a sample dataset using scikit-learn to understand the concept of Ordinary Least Squares (OLS)
2. 2Run gradient descent optimization algorithm on a regression model to minimize the loss function
3. 3Configure and compare different loss functions, such as Mean Squared Error (MSE) and Mean Absolute Error (MAE), to evaluate their impact on regression models
4. 4Test the performance of a regression model using metrics like R-squared and coefficient of determination
5. 5Build a simple regression model from scratch using Python and NumPy to understand the underlying mathematics

Data scientists and machine learning engineers can benefit from this article to improve their understanding of regression and its applications

💡 Regression is a fundamental concept in machine learning, and understanding loss functions, OLS, and gradient descent is crucial for building accurate models

📈 Learn regression basics and improve your ML skills