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Module

Machine Learning Fundamentals

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4 / 20 pages

Lesson 1: What is Machine Learning?

Lesson 2: Linear Regression from Scratch

Lesson 3: Visualizing the Loss Landscape

Lesson 4: Logistic Regression (Classification)

Lesson 5: K-Nearest Neighbors (Distance)

Lesson 6: Evaluation Metrics (From Scratch)

Lesson 7: Unsupervised Learning & K-Means

Lesson 8: Dimensionality Reduction with PCA

Lesson 9: Decision Trees & Splits

Lesson 10: Regularization (L1 & L2)

Lesson 11: K-Fold Cross Validation

Lesson 12: Naive Bayes — Probabilistic Classifier

Lesson 13: Support Vector Machines (SVM)

Lesson 14: Gradient Boosting & AdaBoost

Lesson 15: DBSCAN — Density-Based Clustering

Lesson 16: Gaussian Mixture Models (GMM)

Lesson 17: Ensemble Methods — Combine Multiple Models

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Linear Regression from Scratch · Page 2 of 2

Visualizing the Training Process

Visualizing the Training Loop

Seeing the math is good, but seeing the line move is how you truly understand Gradient Descent.

Notice how the line starts completely wrong, but gradually pivots to fit the data perfectly as the MSE decreases.

Underfitting: The line is too straight (high bias).
Overfitting: The line wiggles to hit every single point perfectly (high variance) — bad for new data.
Good Fit: Captures the general trend.

main.py

OUTPUT

▶Click "Run Code" to execute…