10 posts · page 1 of 1
A practical tour of modern recommendation systems: collaborative filtering, content-based methods, hybrid stacks, and how AI ranking models fit on top of candidate generation pipelines.
The bias variance tradeoff explained with intuition, examples, and practical guidance on how to diagnose and reduce each component of error in your ML models.
How decision trees work, why a single tree overfits, and how random forests solve that problem by averaging many trees trained on different data.
An intuitive and practical explanation of gradient descent, the workhorse optimization algorithm behind nearly all modern machine learning models.
A practical comparison of hyperparameter tuning strategies including grid search, random search, Bayesian optimization, and Hyperband, with guidance on when to use each.
An intuitive walkthrough of support vector machines, the kernel trick, and when SVMs still make sense in a world dominated by gradient boosted trees and neural networks.
A working introduction to decision trees in scikit-learn covering splitting criteria, overfitting, max_depth tuning, visualization, and the path to random forests.
Practical feature engineering for tabular machine learning, covering encoding, scaling, missing value handling, interaction features, and how to avoid data leakage.
A practical introduction to linear regression with scikit-learn covering OLS, evaluation with R-squared and MAE, and the assumptions that make or break the model.
Learn how logistic regression turns a linear score into a probability, how to train it with scikit-learn, and how to evaluate binary classifiers using ROC-AUC.