# Machine Learning Diagnostic

> ML preparation set.

- Stable ID: `machine-learning-diagnostic`
- Area: machine-learning
- Kind: diagnostic
- Timebox: 60 minutes

Sample objectives, updates, model evaluation, representation, networks, and clustering.

## Instructions

1. Show the objective or computational graph before calculating.
2. Separate formula recall from interpretation.

## Ordered items

1. [Linear regression objective and gradient](https://mlprep.iwase.dev/item/original-ml-regression/?set=machine-learning-diagnostic) — `original-ml-regression` (8 min)
2. [Logistic probability and cross-entropy](https://mlprep.iwase.dev/item/original-ml-logistic/?set=machine-learning-diagnostic) — `original-ml-logistic` (6 min)
3. [Gradient descent on a quadratic](https://mlprep.iwase.dev/item/original-ml-gd/?set=machine-learning-diagnostic) — `original-ml-gd` (7 min)
4. [PCA direction and explained variance](https://mlprep.iwase.dev/item/original-ml-pca/?set=machine-learning-diagnostic) — `original-ml-pca` (7 min)
5. [Forward pass and backpropagation](https://mlprep.iwase.dev/item/original-ml-forward-backprop/?set=machine-learning-diagnostic) — `original-ml-forward-backprop` (8 min)
6. [Convolution output shape and parameters](https://mlprep.iwase.dev/item/original-ml-convolution/?set=machine-learning-diagnostic) — `original-ml-convolution` (6 min)
7. [Diagnose train and validation error](https://mlprep.iwase.dev/item/original-ml-theory/?set=machine-learning-diagnostic) — `original-ml-theory` (6 min)
8. [One k-means assignment and update](https://mlprep.iwase.dev/item/original-ml-kmeans/?set=machine-learning-diagnostic) — `original-ml-kmeans` (7 min)

## Completion

Use missed skills to choose the corresponding topic practice set.

## Next recommended practice

- [Regression Practice](https://mlprep.iwase.dev/sets/regression-practice.md)
- [Classification Practice](https://mlprep.iwase.dev/sets/classification-practice.md)
- [Gradient Descent Practice](https://mlprep.iwase.dev/sets/gradient-descent-practice.md)
- [Dimensionality Reduction Practice](https://mlprep.iwase.dev/sets/dimensionality-reduction-practice.md)
- [Neural Networks Practice](https://mlprep.iwase.dev/sets/neural-networks-practice.md)
- [Convolution Practice](https://mlprep.iwase.dev/sets/convolution-practice.md)
- [Optimization Practice](https://mlprep.iwase.dev/sets/optimization-practice.md)
- [Theory Practice](https://mlprep.iwase.dev/sets/theory-practice.md)
- [Unsupervised Learning Practice](https://mlprep.iwase.dev/sets/unsupervised-learning-practice.md)