The Python, AI & Data series goes from collecting and cleaning data to machine learning, deep learning, Vietnamese language processing, LLMs, RAG, agents and running models in production, with runnable Python code ...
On an online store, a "you may also like" strip under each product is a chance to raise order value, but only if the suggestions are close enough to what the shopper wants. A recommender system answer...
A store needs to know its sales for the next 28 days to order stock and schedule staff. The data is a single column of daily sales over three years. This is a time series forecasting problem. What set...
Among 60,300 transactions from an online store, only 300 (0.5%) are anomalous: a large amount at midnight, an order shipped very far from the usual address, dozens of purchases packed into 24 hours. N...
One-time buyers, regular customers and VIP customers need different messages, but nobody wants to split them up by gut feeling. Clustering solves exactly this problem: an algorithm groups similar cust...
A credit-scoring model rejects a customer's loan application. The customer asks why, the risk team asks whether the model relies on sensible data, and the regulator asks for evidence. With gradient bo...
On tabular data, meaning tables whose rows are customers, orders or transactions and whose columns are attributes, the model that wins many competitions and runs in many real scoring systems is usuall...
Two models with the same algorithm can differ by anywhere from a few points to dozens of AUC points purely because of how the input data is represented, as the experiments below show. That is why feat...
A fraud-detection model with 99.13% accuracy sounds impressive, until you learn that a program that prints "not fraud" for every transaction scores 99.12%. That is why model evaluation for classifiers...
If you have ever standardized a whole table before splitting it into train and test sets, picked hyperparameters by watching the score on the test set, or forgotten to apply the right preprocessing wh...
Almost every modern machine learning model, from linear regression to neural networks with billions of parameters, is trained with the same idea: measure the error, compute the slope, take a small ste...
Many machine learning projects fail not because the algorithm is weak, but because the team picked the wrong problem, trained on unrepresentative data, or measured results with the wrong yardstick. Th...
A pure Python for loop over a million elements takes about a hundred milliseconds; the same computation written with NumPy arrays takes under one millisecond. That two-orders-of-magnitude gap is why a...
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