🤖 Machine Learning Regression Algorithms from Scratch

Supervised vs Unsupervised Learning

15 min

Train / Validation / Test Split

12 min

The Bias-Variance Tradeoff

13 min

Overfitting & Underfitting

14 min

Cross-Validation

18 min

Feature Scaling

17 min

Linear Regression (Revisited)

19 min

Polynomial Regression

13 min

Regularization (Ridge & Lasso)

18 min

Decision Tree Regression

15 min

Random Forest Regression

16 min

Gradient Boosting (XGBoost)

14 min

Logistic Regression

14 min

K-Nearest Neighbors (KNN)

17 min

Decision Trees & Random Forests (for Classification)

18 min

Support Vector Machines (SVM)

12 min

Naive Bayes

15 min

Gradient Boosting for Classification

15 min

What Unsupervised Learning Actually Solves

16 min

K-Means Clustering

19 min

Hierarchical Clustering

12 min

DBSCAN (Density-Based Clustering)

18 min

Principal Component Analysis (PCA)

19 min

Visualizing High Dimensions (t-SNE & UMAP)

13 min

Classification Metrics (Accuracy, Precision, Recall, F1)

19 min

Confusion Matrix, ROC & AUC

12 min

Regression Metrics (MAE, RMSE, R²)

18 min

Common Evaluation Pitfalls (Data Leakage & Friends)

15 min

Hyperparameter Tuning (Grid & Random Search)

18 min

Smarter Tuning (Bayesian Optimization)

16 min

Handling Imbalanced Data (SMOTE & Class Weights)

19 min

Missing & Messy Data

13 min

Feature Engineering & Selection

15 min

Building an ML Pipeline

15 min

The End-to-End ML Workflow

12 min

Deploying & Monitoring Your Model

15 min