1. Introduction: Why SHAP Matters for Categorical Features “All models are wrong, but some are useful.” – George Box I’ve worked with machine learning models long enough to know one thing: explainability can make or break your model’s impact. If people don’t trust the predictions, it doesn’t matter how good your accuracy is. That’s where … Read more
SHAP Values for Categorical Features Read More »
1. Introduction Motivation & Relevance You ever find yourself staring at a massive dataset, wondering, How do I make sense of this mess? Yeah, I’ve been there too. When I first started working with clustering algorithms, K-Means quickly became my go-to tool. It’s simple yet surprisingly powerful—one of those rare algorithms that just works in … Read more
K-Means Clustering: In-Depth Pseudocode, Implementation, and Best Practices Read More »
1. Introduction: Why Linear Regression for House Prices? When I first started working on house price prediction, I assumed that complex models like XGBoost or deep learning would always outperform traditional methods. But experience has taught me that sometimes, simpler is better—and that’s exactly where linear regression shines. Linear regression is a workhorse in real … Read more
Linear Regression for House Price Prediction Read More »
1️⃣ Introduction “The only way to learn mathematics is to do mathematics.” — Paul Halmos If you’ve ever worked with machine learning, you already know this: linear regression is everywhere. From predicting house prices to understanding marketing trends, it’s often the first model we reach for. But here’s something most tutorials won’t tell you—knowing the … Read more
Linear Regression Practice Problems Read More »
1. Introduction Why Explainability Matters in Multiclass Models “If machine learning is a black box, then explainability is the flashlight.” I’ve worked with a lot of machine learning models, and if there’s one thing that always comes up—especially in high-stakes applications—it’s the question of “Why did the model make this prediction?” With binary classification, it’s … Read more
SHAP Values for Multiclass Classification Read More »
1. Introduction: Why Feature Interpretability Matters in Machine Learning “A machine learning model is only as useful as our ability to understand it.” I’ve seen this firsthand while working with complex models. You train a high-performing Random Forest, get impressive accuracy, and then—boom!—someone asks, “Why did the model make this prediction?” Suddenly, the black-box nature … Read more
SHAP Values for Random Forest Read More »
1. Introduction Why Interpretability Matters in Binary Classification “A machine learning model is only as good as your ability to trust it.” I’ve worked with enough ML models to know that a high accuracy score means nothing if I can’t explain why a model makes certain predictions. This becomes critical in high-stakes fields like finance, … Read more
SHAP Values for Binary Classification Read More »
1. Introduction “Clustering isn’t just an algorithm—it’s a way of seeing patterns the human eye would never catch.” When I first got into clustering, I thought, “How hard can it be? Just group similar things together.” But once you start working with real-world data, you quickly realize that clustering is both an art and a … Read more
K-Means Clustering for Image and Data Analysis Read More »
Introduction If you’re here, chances are you already love deep learning and want to build serious projects with TensorFlow. I get it—I’ve been there myself, experimenting, failing, optimizing, and eventually deploying real-world models. Over the years, TensorFlow has become my go-to deep learning framework for one reason: it’s not just about building models—it’s about taking … Read more
Deep Learning Projects Using TensorFlow Read More »
1. Introduction “If you tell me how you reward me, I’ll tell you how I’ll behave.” – This applies to both humans and reinforcement learning agents. When I first started working with RL models, I assumed the reward function was just a simple scoring mechanism—higher rewards mean better learning, right? Wrong. A poorly designed reward … Read more
Reward Function in Reinforcement Learning Read More »
