Linear Regression

Summary

The simplest predictive model — fitting a straight line (or hyperplane) to predict a continuous value — and still the right first baseline for any regression problem.

How to Learn This

1Implement linear regression from scratch using gradient descent.
2Then fit the same problem with scikit-learn's LinearRegression to compare.
3Learn to interpret coefficients and R² for a fitted model.

Supervised vs Unsupervised Learning

Logistic Regression

More topics in Machine Learning Fundamentals

Supervised vs Unsupervised Learning Logistic Regression Decision Trees Random Forests K-Nearest Neighbors K-Means Clustering Support Vector Machines Naive Bayes scikit-learn Basics Train/Test Split Cross-Validation

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