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
- Programming Exercise: (TensorFlow) Hello World
- Programming Exercise: TensorFlow Programming Concepts
- Programming Exercise: Creating and Manipulating Tensors
- Programming Exercise: Quick Introduction to pandas
Framing
Descending into ML
Reducing Loss
- Optimizing Learning Rate
- Check Your Understanding: Batch Size
- Playground: Learning Rate and Convergence
First Steps with TensorFlow
- Programming Exercise: First Steps with TensorFlow
- Programming Exercise: Synthetic Features and Outliers
Training and Test Sets
Validation
Representation
Feature Crosses
- Playground: Introducing Feature Crosses, More Complex Feature Crosses
- Check Your Understanding: Feature Crosses
- Programming Exercise: Feature Crosses
Regularization for Simplicity
- Playground: Overcrossing?
- Check Your Understanding: L_{2} Regularization, L_{2} Regularization and Correlated Features
- Playground: Examining L_{2} Regularization
Classification
- Check Your Understanding: Accuracy, Precision, Recall, Precision and Recall
- Check Your Understanding: ROC and AUC
- Programming Exercise: Logistic Regression
Regularization for Sparsity
- Check Your Understanding: L_{1} Regularization, L_{1} vs. L_{2} Regularization
- Playground: Examining L_{1} Regularization
- Programming Exercise: Sparsity and L_{1} Regularization
Intro to Neural Nets
- Playground: A First Neural Network, Neural Net Initialization, Neural Net Spiral
- Programming Exercise: Intro to Neural Nets
Training Neural Nets
Multi-Class Neural Nets
Embeddings
Static vs. Dynamic Training
Static vs. Dynamic Inference
Data Dependencies
Programming
Preliminaries
- Programming Exercise: (TensorFlow) Hello World
- Programming Exercise: TensorFlow Programming Concepts
- Programming Exercise: Creating and Manipulating Tensors
- Programming Exercise: Quick Introduction to pandas
Framing
Descending into ML
Reducing Loss
- Optimizing Learning Rate
- Check Your Understanding: Batch Size
- Playground: Learning Rate and Convergence
First Steps with TensorFlow
- Programming Exercise: First Steps with TensorFlow
- Programming Exercise: Synthetic Features and Outliers
Training and Test Sets
Validation
Representation
Feature Crosses
- Playground: Introducing Feature Crosses, More Complex Feature Crosses
- Check Your Understanding: Feature Crosses
- Programming Exercise: Feature Crosses
Regularization for Simplicity
- Playground: Overcrossing?
- Check Your Understanding: L_{2} Regularization, L_{2} Regularization and Correlated Features
- Playground: Examining L_{2} Regularization
Classification
- Check Your Understanding: Accuracy, Precision, Recall, Precision and Recall
- Check Your Understanding: ROC and AUC
- Programming Exercise: Logistic Regression
Regularization for Sparsity
- Check Your Understanding: L_{1} Regularization, L_{1} vs. L_{2} Regularization
- Playground: Examining L_{1} Regularization
- Programming Exercise: Sparsity and L_{1} Regularization
Intro to Neural Nets
- Playground: A First Neural Network, Neural Net Initialization, Neural Net Spiral
- Programming Exercise: 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
- Programming Exercise: (TensorFlow) Hello World
- Programming Exercise: TensorFlow Programming Concepts
- Programming Exercise: Creating and Manipulating Tensors
- Programming Exercise: Quick Introduction to pandas
Framing
Descending into ML
Reducing Loss
- Optimizing Learning Rate
- Check Your Understanding: Batch Size
- Playground: Learning Rate and Convergence
First Steps with TensorFlow
- Programming Exercise: First Steps with TensorFlow
- Programming Exercise: Synthetic Features and Outliers
Training and Test Sets
Validation
Representation
Feature Crosses
- Playground: Introducing Feature Crosses, More Complex Feature Crosses
- Check Your Understanding: Feature Crosses
- Programming Exercise: Feature Crosses
Regularization for Simplicity
- Playground: Overcrossing?
- Check Your Understanding: L_{2} Regularization, L_{2} Regularization and Correlated Features
- Playground: Examining L_{2} Regularization
Classification
- Check Your Understanding: Accuracy, Precision, Recall, Precision and Recall
- Check Your Understanding: ROC and AUC
- Programming Exercise: Logistic Regression
Regularization for Sparsity
- Check Your Understanding: L_{1} Regularization, L_{1} vs. L_{2} Regularization
- Playground: Examining L_{1} Regularization
- Programming Exercise: Sparsity and L_{1} Regularization
Intro to Neural Nets
- Playground: A First Neural Network, Neural Net Initialization, Neural Net Spiral
- Programming Exercise: Intro to Neural Nets
Training Neural Nets
Multi-Class Neural Nets
Embeddings
Static vs. Dynamic Training
Static vs. Dynamic Inference
Data Dependencies
Playground
Preliminaries
- Programming Exercise: (TensorFlow) Hello World
- Programming Exercise: TensorFlow Programming Concepts
- Programming Exercise: Creating and Manipulating Tensors
- Programming Exercise: Quick Introduction to pandas
Framing
Descending into ML
Reducing Loss
- Optimizing Learning Rate
- Check Your Understanding: Batch Size
- Playground: Learning Rate and Convergence
First Steps with TensorFlow
- Programming Exercise: First Steps with TensorFlow
- Programming Exercise: Synthetic Features and Outliers
Training and Test Sets
Validation
Representation
Feature Crosses
- Playground: Introducing Feature Crosses, More Complex Feature Crosses
- Check Your Understanding: Feature Crosses
- Programming Exercise: Feature Crosses
Regularization for Simplicity
- Playground: Overcrossing?
- Check Your Understanding: L_{2} Regularization, L_{2} Regularization and Correlated Features
- Playground: Examining L_{2} Regularization
Classification
- Check Your Understanding: Accuracy, Precision, Recall, Precision and Recall
- Check Your Understanding: ROC and AUC
- Programming Exercise: Logistic Regression
Regularization for Sparsity
- Check Your Understanding: L_{1} Regularization, L_{1} vs. L_{2} Regularization
- Playground: Examining L_{1} Regularization
- Programming Exercise: Sparsity and L_{1} Regularization
Intro to Neural Nets
- Playground: A First Neural Network, Neural Net Initialization, Neural Net Spiral
- Programming Exercise: Intro to Neural Nets