Exercises

This page lists the exercises in Machine Learning Crash Course.

The majority of the Programming Exercises use the California housing data set.

Programming exercises run directly in your browser (no setup required!) using the Colaboratory platform. Colaboratory is supported on most major browsers, and is most thoroughly tested on desktop versions of Chrome and Firefox. If you'd prefer to download and run the exercises offline, see these instructions for setting up a local environment.

All

Preliminaries

Framing

Descending into ML

Reducing Loss

First Steps with TensorFlow

Training and Test Sets

Validation

Representation

Feature Crosses

Regularization for Simplicity

Classification

Regularization for Sparsity

Intro to Neural Nets

Training Neural Nets

Multi-Class Neural Nets

Embeddings

Static vs. Dynamic Training

Static vs. Dynamic Inference

Data Dependencies

Programming

Preliminaries

Framing

Descending into ML

Reducing Loss

First Steps with TensorFlow

Training and Test Sets

Validation

Representation

Feature Crosses

Regularization for Simplicity

Classification

Regularization for Sparsity

Intro to Neural Nets

Training Neural Nets

Multi-Class Neural Nets

Embeddings

Static vs. Dynamic Training

Static vs. Dynamic Inference

Data Dependencies

Check Your Understanding

Preliminaries

Framing

Descending into ML

Reducing Loss

First Steps with TensorFlow

Training and Test Sets

Validation

Representation

Feature Crosses

Regularization for Simplicity

Classification

Regularization for Sparsity

Intro to Neural Nets

Training Neural Nets

Multi-Class Neural Nets

Embeddings

Static vs. Dynamic Training

Static vs. Dynamic Inference

Data Dependencies

Playground

Preliminaries

Framing

Descending into ML

Reducing Loss

First Steps with TensorFlow

Training and Test Sets

Validation

Representation

Feature Crosses

Regularization for Simplicity

Classification

Regularization for Sparsity

Intro to Neural Nets

Training Neural Nets

Multi-Class Neural Nets

Embeddings

Static vs. Dynamic Training

Static vs. Dynamic Inference

Data Dependencies

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Machine Learning Crash Course