Principal Component Analysis Explained This is how we take messy, high-dimensional data and turn it into a few clean, meaningful patterns. PCA finds the directions where the data varies the most and rewrites each data point using those directions instead of raw features. That’s how we reduce noise, avoid scale problems, and make models easier to visualize and train. It’s not magic - it’s just a smarter way to look at the same data. #machinelearning #ml #python #code #techtok

We built a tiny ML model that uses first downs to predict points - yes, it’s that simple! Trained on 10 examples, got predictions from the test set, and judged with mean squared error. Ask it for 10 first downs and it returns ~6.5 points. Now you can explain the line, not just draw it. #machinelearning #datascience #python #nfl #superbowl

Understanding Machine Learning vs. Rule-Based Logic

How to Build Your First Machine Learning Model 👩‍💻 We built a tiny ML model that uses apartment size to predict rent - yes, it’s that simple! Trained on 10 examples, got predictions from the test set, and judged with mean squared error. Ask it for a predicted based on 950 square feet and it returns ~2,325 dollars. Now you can explain the line, not just draw it. #machinelearning #datascience #python #ml #code