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
In March, 2020, this course began using Programming Exercises coded with tf.keras. If you'd prefer to use the legacy Estimators Programming Exercises, you can find them on GitHub.
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: NumPy Ultraquick Tutorial
- Programming Exercise: pandas UltraQuick Tutorial
- Programming Exericse: Linear Regression with Synthetic Data
- Programming Exercise: Linear Regression with a Real Dataset
Training and Test Sets
Validation
Feature Crosses
- Playground: Introducing Feature Crosses, More Complex Feature Crosses
- Check Your Understanding: Feature Crosses
- Programming Exercise: Representation with 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: Binary Classification
Regularization for Sparsity
- Check Your Understanding: L_{1} Regularization, L_{1} vs. L_{2} Regularization
- Playground: Examining L_{1} Regularization
Intro to Neural Nets
- Playground: A First Neural Network, Neural Net Initialization, Neural Net Spiral
- Programming Exercise: Intro to Neural Networks
Training Neural Nets
Multi-Class Neural Nets
Fairness
Static vs. Dynamic Training
Static vs. Dynamic Inference
Data Dependencies
Programming
In March, 2020, this course began using Programming Exercises coded with tf.keras. If you'd prefer to use the legacy Estimators Programming Exercises, you can find them on GitHub.
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: NumPy Ultraquick Tutorial
- Programming Exercise: pandas UltraQuick Tutorial
- Programming Exericse: Linear Regression with Synthetic Data
- Programming Exercise: Linear Regression with a Real Dataset
Training and Test Sets
Validation
Feature Crosses
- Playground: Introducing Feature Crosses, More Complex Feature Crosses
- Check Your Understanding: Feature Crosses
- Programming Exercise: Representation with 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: Binary Classification
Regularization for Sparsity
- Check Your Understanding: L_{1} Regularization, L_{1} vs. L_{2} Regularization
- Playground: Examining L_{1} Regularization
Intro to Neural Nets
- Playground: A First Neural Network, Neural Net Initialization, Neural Net Spiral
- Programming Exercise: Intro to Neural Networks
Training Neural Nets
Multi-Class Neural Nets
Fairness
Static vs. Dynamic Training
Static vs. Dynamic Inference
Data Dependencies
Check Your Understanding
In March, 2020, this course began using Programming Exercises coded with tf.keras. If you'd prefer to use the legacy Estimators Programming Exercises, you can find them on GitHub.
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: NumPy Ultraquick Tutorial
- Programming Exercise: pandas UltraQuick Tutorial
- Programming Exericse: Linear Regression with Synthetic Data
- Programming Exercise: Linear Regression with a Real Dataset
Training and Test Sets
Validation
Feature Crosses
- Playground: Introducing Feature Crosses, More Complex Feature Crosses
- Check Your Understanding: Feature Crosses
- Programming Exercise: Representation with 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: Binary Classification
Regularization for Sparsity
- Check Your Understanding: L_{1} Regularization, L_{1} vs. L_{2} Regularization
- Playground: Examining L_{1} Regularization
Intro to Neural Nets
- Playground: A First Neural Network, Neural Net Initialization, Neural Net Spiral
- Programming Exercise: Intro to Neural Networks
Training Neural Nets
Multi-Class Neural Nets
Fairness
Static vs. Dynamic Training
Static vs. Dynamic Inference
Data Dependencies
Playground
In March, 2020, this course began using Programming Exercises coded with tf.keras. If you'd prefer to use the legacy Estimators Programming Exercises, you can find them on GitHub.
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: NumPy Ultraquick Tutorial
- Programming Exercise: pandas UltraQuick Tutorial
- Programming Exericse: Linear Regression with Synthetic Data
- Programming Exercise: Linear Regression with a Real Dataset
Training and Test Sets
Validation
Feature Crosses
- Playground: Introducing Feature Crosses, More Complex Feature Crosses
- Check Your Understanding: Feature Crosses
- Programming Exercise: Representation with 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: Binary Classification
Regularization for Sparsity
- Check Your Understanding: L_{1} Regularization, L_{1} vs. L_{2} Regularization
- Playground: Examining L_{1} Regularization
Intro to Neural Nets
- Playground: A First Neural Network, Neural Net Initialization, Neural Net Spiral
- Programming Exercise: Intro to Neural Networks