Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
Machine Learning: Algorithms & Applications
·
Module 3
Linear Regression and Core Concepts
1
Understanding Linear Regression: Intuition, Cost Function, and Assumptions
Explain the intuition behind linear regression, including its cost function (MSE), ordinary least squares, and key assumptions.
2
Gradient Descent: Optimizing Machine Learning Models
Explain gradient descent as a general optimization method for fitting machine learning models.
3
Evaluating Regression Models with Key Metrics
Evaluate regression models using key metrics such as MSE, RMSE, MAE, and R-squared.
4
K-Fold and Stratified K-Fold Cross-Validation
Explain k-fold and stratified k-fold cross-validation as robust model evaluation strategies.
5
Bias-Variance Tradeoff: Underfitting and Overfitting
Explain the bias-variance tradeoff and how it relates to underfitting and overfitting.
6
Linear Regression: Real-World Application and Evaluation
Apply linear regression to a real-world dataset and evaluate its performance.
7
Interpreting Linear Regression Coefficients
Interpret the coefficients of a trained linear regression model to explain feature-target relationships.
Previous module
Feature Engineering and Preprocessing
Next module
Regularized Linear Models