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Missing data is a message: patterns, mechanisms, and honest imputation
Missing values are often treated as an inconvenience: fill them, drop them, move on.
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Matrix multiplication is all you need: shapes, batching, and why GPUs love it
If you learn one operation in modern machine learning, make it matrix multiplication (often shortened to matmul ).
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Backpropagation demystified: hand-compute the gradients, then verify with autograd
Backpropagation is the engine behind modern deep learning. Whether you’re training a tiny multilayer perceptron or a frontier-scale language model, every optimization step depends on efficiently compu
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Ship the rule-based baseline first: why 20 lines of if/else gate every good ML project
When people start an ML project, the instinct is often to train a model immediately. Open a notebook, split the data, fit a classifier, and compare metrics.
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Encoders shootout: one-hot, ordinal, target, and hashing — when each wins and when it leaks
Categorical features are everywhere in tabular machine learning:
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Parquet and Arrow: why columnar formats make your queries 100x faster
Large analytical datasets often start life as CSV files. They are easy to create, easy to open, and supported almost everywhere.
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From pickle to production: wrapping a model in a real API that won’t fall over
Training a machine learning model is only half the job. The other half is making it available reliably, safely, and predictably.
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Linear regression is not boring: assumptions, diagnostics, and when it beats deep learning
If you’ve been around machine learning for a while, you’ve probably seen linear regression dismissed as the “hello world” of ML. That sells it short.
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First hour with a new dataset: a repeatable EDA checklist in pandas
A new dataset is not a blank canvas. It is the result of a pipeline: databases, APIs, spreadsheets, sensors, human input, and business processes. Every one of those sources can introduce problems.