Linear Regression

Summary

Predicting a continuous number as a weighted sum of input features — simple, interpretable, and still a strong baseline model before reaching for anything more complex.

How to Learn This

1Fit a linear regression model in scikit-learn on a real dataset.
2Learn to interpret each coefficient in plain business language.
3Check the model's assumptions (linearity, residual patterns) rather than trusting it blindly.

Cross-Validation

Logistic Regression

More topics in Machine Learning Fundamentals

Supervised vs Unsupervised vs Reinforcement Learning Train / Test / Validation Split Overfitting & Underfitting Bias-Variance Tradeoff Cross-Validation Logistic Regression Decision Trees Random Forests Gradient Boosting (XGBoost, LightGBM)K-Nearest Neighbors Support Vector Machines Naive Bayes

Stuck on this topic? Ask an Insider

Get 1:1 guidance from people who've walked this exact path — free on the InsideEdge app.

Download