Esto "ven" los algoritmos de ML [ningún curso te lo había enseñado]

👉 Use the online app here: https://datascienceconpython.com/ 👇👇 WANT TO DOWNLOAD IT FOR LOCAL USE? 👇👇 💬 Leave a comment on the video, take a screenshot, and send it ⚠️ with the subject line: Algorithm to [email protected] 📩 You will receive the download URL to run all these simulations on your computer. 🤔 Do you really know what happens inside an algorithm when it trains on your data? Most data scientists just run model.fit and that's it. But they don't understand why one algorithm performs better than another in a specific case. And that, in times when AI is already writing the code for you, is exactly what separates you from: ✅ being a professional ❌ or just a simple integrator. 🎥 In this video, I'll teach you Machine Learning in a way it's rarely explained: as geometry. Using an interactive simulator, you'll see for yourself 👀 how each algorithm employs completely different strategies to classify data: 📊 Logistic Regression 📊 KNN 📊 Decision Trees 📊 Random Forest 📊 XGBoost 📊 Neural Networks 👉 All compared, on the same data, in real time. 🧠 What you'll understand after this video: ✨ Why a variable in your dataset = a geometric dimension (and why that changes everything) ✨ What a decision boundary is and how each algorithm draws it differently ✨ Why trees overfit more than Random Forest (you'll see it visually) ✨ When to use linear vs. nonlinear algorithms based on your data pattern ✨ Why neural networks dominate with unstructured data and trees win with tabular data 🚫 No formulas. 🚫 No matrices. ✨ Just geometric intuition that will change the way you design your Machine Learning projects.