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Advancedml-engineeringproduction-mlevaluationtraining-systems

ML Engineer Track

27 problems·~15h·4 stages

The production-minded sequence: solid fundamentals, training dynamics, evaluation, and the systems math behind serving models. You will move from standard scalers and loss functions to backprop, model diagnostics, and memory accounting. Built for engineers who ship models, not just notebooks.

Your progress

0/27 · 0%

Path readiness 0%Easy 0% · Medium 0% · Hard 0%

Continue path · Mean Squared Error

What you will learn

  • Build clean training data with scaling, encoding, and split strategies
  • Train models with the right losses and optimizer steps
  • Diagnose models with validation, regularization, and importance
  • Reason about neural training and serving costs

Before you start

1Data Hygiene

0/8

Reproducible splits and features: MSE, train/test splits, shuffled and stratified sampling, standardization, one-hot encoding, distances, and k-fold indices.

Stage checkpoint

0/2 to pass

A checkpoint is a short retrieval test drawn from this stage: solve 2 of 3 from memory to mark the stage complete. The stage stays open either way.

Tried this set and did not pass? Record it — the next set swaps in fresh problems from the same stage.
LabRecommended next stepLogistic Regression, From ScratchEasy

2Losses and Optimization

0/7

What training actually minimizes: MSE and BCE gradients, gradient-descent steps, SGD updates, Xavier initialization, bootstrap sampling, and a full Adam update.

Stage checkpoint

0/2 to pass

A checkpoint is a short retrieval test drawn from this stage: solve 2 of 3 from memory to mark the stage complete. The stage stays open either way.

Tried this set and did not pass? Record it — the next set swaps in fresh problems from the same stage.
LabRecommended next stepLogistic Regression, From ScratchEasy

3Models and Diagnostics

0/6

Fit and interrogate models: linear and ridge regression, logistic gradients, Adam steps, batch-norm forward passes, and permutation importance.

Stage checkpoint

0/2 to pass

A checkpoint is a short retrieval test drawn from this stage: solve 2 of 3 from memory to mark the stage complete. The stage stays open either way.

Tried this set and did not pass? Record it — the next set swaps in fresh problems from the same stage.
LabRecommended next stepLogistic Regression, From ScratchEasy

4Neural Systems and Cost

0/6

The deep-learning end of engineering: vocabulary mapping, backprop, softmax cross-entropy backward, a transformer encoder block, transformer parameter counts, and inference FLOP estimates.

Stage checkpoint

0/2 to pass

A checkpoint is a short retrieval test drawn from this stage: solve 2 of 3 from memory to mark the stage complete. The stage stays open either way.

Tried this set and did not pass? Record it — the next set swaps in fresh problems from the same stage.
LabRecommended next stepXOR BoundaryHard

Capstone

Collection

Close the path with one real artifact from the catalog.

Numerical Stability in Practice

Log-sum-exp, stable softmax, and clipping: the tricks behind training runs that do not blow up.