From Zero to Production ML

Every mistake, kept in.

Most machine learning books are written by people who have forgotten which part was hard. This one was written from the other side.

It reconstructs a complete learning path-twenty-nine working sessions-taking a practicing engineer from no formal machine learning background to building, evaluating, deploying, and monitoring production models. Every concept was worked through with real code, real datasets, and real errors.

The errors are the point. Twenty-two specific misconceptions are recorded in full, each with the correction that fixed it and an explanation of why the confusion happens at all.

Nothing here is asserted without having been run. Where a model lost a comparison it was expected to win-a neural network beaten by gradient boosting on tabular data, machine learning beaten by a classical statistical model on a short series-the loss is reported and explained rather than quietly omitted.

INSIDE:

The mathematics that is genuinely load-bearing-and no more
Trees, ensembles, and boosting, with honest error comparisons
Precision, recall, and AUC-and how to choose between them
A neuron built by hand in NumPy, then backpropagation, then PyTorch
Convolutional networks, LSTMs, transformers, and transfer learning
Prompting, LoRA fine-tuning, retrieval-augmented generation, agents
Drift detection, model serving, deployment, and retraining pipelines
Time series forecasting and clustering, with what actually worked

For backend engineers, data engineers, analysts, and technical leads moving into machine learning.