Practical, plain-English writing on machine learning, statistics, and building things with Python.
You just got handed a new dataset. Your boss wants results by end of day. You could spend hours exploring the data, testing algorithms, and tuning hyperparameters — but honestly, you've got three other meetings this afternoon.
You've been there. You dropped your data into AutoGluon, walked away for lunch, came back to a 0.96 accuracy score, and felt like a genius. You deployed the model.
You know the feeling. You've spent hours tweaking hyperparameters — adjusting the learning rate, changing the number of trees, trying different kernels.
You've been there. You have a massive table in BigQuery — millions of rows, terabytes of data — and you want to train a simple logistic regression.
You've finally decided to let AutoML handle the grunt work. You've read about what it automates and what it doesn't. You're sold on the idea. Now your boss comes by your desk and says, "Great, we're using AutoML.
Picture this: You've just spent two weeks tuning a gradient boosting model for a customer churn prediction. You tried different learning rates, max depths, and subsample ratios. You ran grid searches overnight.
Learn why 99% accuracy can mislead and how to pick the right metric for your model, with a plain-English guide to precision, recall, F1, and AUC-ROC in Python.
Learn when to use Pearson, Spearman, or Kendall correlation in Python — and why a weak score may mean you simply chose the wrong tool for your data.