Elevator Pitch UMAP is a powerful dimensionality reduction technique that helps visualize and understand complex, high-dimensional data in two or three dimensions. It preserves both the local and global structure of data, making it an excellent tool for uncovering patterns, relationships, and clusters that traditional methods might miss. UMAP is widely used in modern machine…

Elevator Pitch LightGBM (Light Gradient Boosting Machine) is Microsoft’s highly efficient gradient boosting framework that builds decision trees leaf-wise instead of level-wise. The result? It’s much faster, uses less memory, and delivers state of the art accuracy especially on large datasets with lots of features. Category Type: Supervised LearningTask: Classification and RegressionFamily: Ensemble Methods (Gradient…

Elevator Pitch t-SNE, short for t-distributed Stochastic Neighbor Embedding, is a visualization technique that turns complex, high-dimensional data into intuitive two or three-dimensional plots. It helps uncover clusters, relationships, and hidden structures that are impossible to see in large feature spaces. While it is not a predictive model, t-SNE is one of the most powerful…

Introduction Browser automation has become an essential tool for developers, whether you are testing web applications, scraping data, or automating repetitive tasks. Stagehand is a modern browser automation framework built on the Chrome DevTools Protocol, designed to make these tasks simpler and faster. Unlike some other frameworks that rely on heavy dependencies, Stagehand provides a…

Elevator Pitch Principal Component Analysis (PCA) is a foundational technique for simplifying complex data without losing its essence. It transforms high-dimensional data into a smaller set of uncorrelated variables called principal components, capturing the directions of maximum variance. PCA is the go-to tool for visualization, noise reduction, and feature compression that helps teams make sense…

I recently led a 45 minute session called The ReFocus Loop with our engineers, product, QA, and operations teams. The goal was simple yet powerful. I wanted every engineer to start with one customer outcome, translate it into measurable success criteria at both the business and engineering levels, and finish with one demo that a…

Elevator Pitch CatBoost is a high-performance gradient boosting algorithm built by Yandex, designed to handle categorical features natively without heavy preprocessing. It eliminates the need for one-hot encoding, reduces overfitting, and offers state-of-the-art accuracy with minimal tuning. Think of it as the “plug-and-play” solution for structured data problems where category-heavy features dominate. Category Type: Supervised…

Elevator Pitch XGBoost (Extreme Gradient Boosting) is a high-performance implementation of gradient boosted trees designed for speed, scalability, and accuracy. It uses clever optimization tricks like regularization, parallel processing, and tree pruning to deliver state-of-the-art results in structured (tabular) data problems. If Gradient Boosted Trees are a powerful sports car, XGBoost is the finely tuned…

Elevator Pitch Gradient Boosted Trees (GBM) are one of the most powerful and versatile machine learning methods in use today. Instead of building one perfect model, GBM builds many imperfect ones where each new tree learns from the mistakes of the previous ones. The result is a strong, highly accurate model that can handle complex…

Elevator Pitch Support Vector Machines (SVMs) are powerful supervised learning models that find the best possible boundary to separate data into classes. Instead of just drawing any line, they look for the one with the maximum margin – the widest possible gap between classes – which helps improve generalization and robustness. They’re especially effective when…