Elevator Pitch Random Forests combine many decision trees into a single “forest” to improve accuracy, reduce overfitting, and handle complex datasets. They’re one of the most versatile, reliable ML algorithms used across industries from fraud detection to underwriting to recommendation systems. Category Intuition Instead of trusting a single decision tree (which may overfit), Random Forests…

Elevator Pitch Decision Trees split data into branches based on feature values until they reach a decision. They’re one of the most intuitive ML models: you can literally draw them on a whiteboard and walk your exec team through the predictions. Category Intuition Imagine playing “20 Questions.” Each question narrows the possibilities until you identify…

Elevator Pitch k-Nearest Neighbors (kNN) is a simple, non-parametric algorithm that classifies new data points based on the “majority vote” of its neighbors. For regression, it predicts the average of the neighbors’ values. It’s intuitive, requires no training, and works well when decision boundaries are irregular. Category Intuition Imagine you want to predict whether a…

Elevator Pitch Naive Bayes is a fast, simple, and surprisingly powerful classification algorithm based on Bayes’ Theorem. It assumes that features are independent (“naive” assumption), but despite this simplification, it performs extremely well in real-world tasks like spam filtering, sentiment analysis, and text classification. Category Intuition At its core, Naive Bayes applies Bayes’ Theorem to…

Introduction Networking has always been central to professional life, but the way we connect with others has changed dramatically. What once meant exchanging business cards at conferences now spans LinkedIn messages, virtual communities, and even AI-powered introductions. For professionals and entrepreneurs alike, building a network is no longer just a nice-to-have. It is one of…

The first time I presented to a board, I made the classic mistake: I came in armed with charts on system performance, uptime percentages, and the roadmap for a new architecture. Within ten minutes, I realized I’d lost them. Not because they didn’t care, but because I was answering questions nobody in the room was…

Elevator Pitch Despite its name, logistic regression is not used for regression but for classification. It predicts the probability that an input belongs to a particular class (yes/no, churn/stay, fraud/not fraud). Simple, interpretable, and scalable, logistic regression remains one of the most trusted models for classification problems. Category Intuition Linear regression outputs a straight line…

Elevator Pitch Linear Regression is one of the simplest ML models, but it’s still a workhorse in finance, healthcare, and real estate. As a CTO, I often encourage teams to start here. It’s interpretable, reliable, and a great baseline before scaling into more complex models. Category Supervised Learning → Regression Intuition Executives like clear answers.…

Over the past year, we’ve seen computer use agents also called web agents go from research experiments to real-world productivity tools. At ReFocus AI, we’ve been using BrowserUse (a Y Combinator-backed platform) to power our Intelliagent product, which automates quoting for insurance agents. And we’ve tested a range of other tools, from Stagehand to Browserbase.…

Introduction: Why Pain Comes First A lot of AI projects sound great on paper. They start with good intentions, promising features, and excitement around the possibilities. But then something happens. The feature ships, adoption is low, the ROI is unclear, and slowly, quietly, the initiative loses steam. This is more common than you might think.…