Mastering Machine Learning from Scratch

Learn to derive the best fit line in linear regression by minimizing the sum of a square error using derivatives and gradient descent intuition for beta naught and beta 1.

Train a linear regression model in two lines of code, fit it on scaled data, and interpret intercept and coefficients to predict and evaluate with MSE, RMSE, and R2.

This lecture explains L2 regularization, or ridge regression, which adds a squared-coefficient penalty to shrink coefficients without forcing them to zero, useful for correlated features and preventing overfitting.

Explore cross-validation, including fourfold splits, to train on 75% and test on 25% of data, and address overfitting and underfitting with L1/L2 and elastic net regularization and hyperparameter tuning.

Explore the shift from regression to classification, focusing on binary classification with logistic regression and the sigmoid output. Apply thresholding to convert probabilities into labels and learn with binary cross-entropy.

Master logistic regression from theory to practice by learning the sigmoid s(theta), cost minimization via gradient descent, and evaluating with confusion matrix, accuracy, precision, recall, f1, and ROC AUC.

Implement logistic regression to classify churn on a real-world dataset, transforming categorical features with label and one-hot encoding, scaling with min-max, and evaluating with accuracy, confusion matrix, and ROC-AUC.

Apply a practical decision tree to the loan df dataset using label-encoded features. Explore data cleaning, correlation heat map, and hyperparameter tuning, then evaluate with ROC AUC and confusion matrix.

Explore ensemble learning, a technique that combines multiple models to improve accuracy and robustness, reduce overfitting, and diversify predictions through bagging, boosting, and stacking.

Explore how random forest, a bagging ensemble of multiple decision trees, achieves high accuracy in classification and regression via random feature selection, bootstrap sampling, and majority voting or averaging.

Mastering machine learning from scratch introduces the silhouette score to determine the optimal number of clusters in k-means by balancing cohesion and separation, complementing the elbow method.

Explore hierarchical clustering, an unsupervised technique that builds a dendrogram to reveal nested data groups and determine optimal clusters using agglomerative and divisive approaches.

Apply k-means and agglomerative clustering to unsupervised learning, using scaling, elbow and silhouette analysis to determine optimal clusters, and compare cluster quality with dendrograms.