When I teach #DeepLearning and #MachineLearning, I like to build the models with my students piece by piece. For example, we construct a simple artificial #NeuralNetwork from scratch and then train it using only fundamental mathematics: - linear algebra - derivatives - the chain rule Once the mathematics clicks, we bring it to life with an interactive @Matplotlib dashboard so students can see how the network learns. I've shared the notebook on #GitHub: github.com/GeostatsGuy/DataS…

Bayesian updating is one of the most powerful ideas in data science 🔄📊 ... and most people still underuse it! You start with a belief (prior)… then reality shows up (data)… and you update your mind (posterior)! 🚀 That’s it. That’s the magic. But here’s where it gets interesting 👇 Weak prior vs strong prior Little data vs lots of data Wrong assumptions vs robust inference Watch uncertainty move in real time! I built an interactive #Python @matplotlib dashboard so you can experience it: 👉 github.com/GeostatsGuy/DataS… Play with it, break it, rebuild it 🧠⚡Stoked!

Tree-based gradient boosting is an ensemble method that builds a powerful prediction model by sequentially adding weak learners (often decision tree stumps). 🌲📈 To help my students really get it, I built a #Python @matplotlib interactive dashboard 🚀🐍 Step through each stump and watch the model update as the residuals and error evolve in real time! 🔥🎯 #MachineLearning #DataScience #AI #Python

I’m stoked to teach density-based cluster analysis to my students! 🔥 Agglomerative clustering is a bottom-up hierarchical method: each point starts as its own cluster, then pairs of clusters are repeatedly merged based on similarity until one tree of clusters (a dendrogram) is formed 🌳 Very intuitive—it mirrors how we naturally perceive structure—like pointillism 🎨, where meaning emerges by progressively merging nearby points of similarity into coherent patterns! To help my students visualize density-based cluster analysis, I built an interactive #Python dashboard with @matplotlib 📊🚀