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

• Programming Exercise: (TensorFlow) Hello World
• Programming Exercise: TensorFlow Programming Concepts
• Programming Exercise: Creating and Manipulating Tensors
• Programming Exercise: Quick Introduction to pandas

Framing

• Check Your Understanding: Supervised Learning, Features and Labels

Descending into ML

• Check Your Understanding: Mean Squared Error

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

• Playground: Training Sets and Test Sets

Validation

• Check Your Intuition: Validation
• Programming Exercise: Validation

Representation

• Programming Exercise: Feature Sets

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: L₂ Regularization, L₂ Regularization and Correlated Features
• Playground: Examining L₂ 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: L₁ Regularization, L₁ vs. L₂ Regularization
• Playground: Examining L₁ Regularization
• Programming Exercise: Sparsity and L₁ 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

• Backpropagation algorithm visual explanation
• Programming Exercise: Improving Neural Net Performance

Multi-Class Neural Nets

• Programming Exercise: Classifying Handwritten Digits with Neural Networks

Embeddings

• Programming Exercise: Intro to Sparse Data and Embeddings

Fairness

• Check Your Understanding: Fairness
• Programming Exercise: Intro to Fairness

Static vs. Dynamic Training

• Check Your Understanding: Online Training, Offline Training

Static vs. Dynamic Inference

• Check Your Understanding: Offline Inference, Online Inference

Data Dependencies

• Check Your Understanding: 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

• Check Your Understanding: Supervised Learning, Features and Labels

Descending into ML

• Check Your Understanding: Mean Squared Error

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

• Playground: Training Sets and Test Sets

Validation

• Check Your Intuition: Validation
• Programming Exercise: Validation

Representation

• Programming Exercise: Feature Sets

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: L₂ Regularization, L₂ Regularization and Correlated Features
• Playground: Examining L₂ 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: L₁ Regularization, L₁ vs. L₂ Regularization
• Playground: Examining L₁ Regularization
• Programming Exercise: Sparsity and L₁ 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

• Backpropagation algorithm visual explanation
• Programming Exercise: Improving Neural Net Performance

Multi-Class Neural Nets

• Programming Exercise: Classifying Handwritten Digits with Neural Networks

Embeddings

• Programming Exercise: Intro to Sparse Data and Embeddings

Fairness

• Check Your Understanding: Fairness
• Programming Exercise: Intro to Fairness

Static vs. Dynamic Training

• Check Your Understanding: Online Training, Offline Training

Static vs. Dynamic Inference

• Check Your Understanding: Offline Inference, Online Inference

Data Dependencies

• Check Your Understanding: 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

• Check Your Understanding: Supervised Learning, Features and Labels

Descending into ML

• Check Your Understanding: Mean Squared Error

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

• Playground: Training Sets and Test Sets

Validation

• Check Your Intuition: Validation
• Programming Exercise: Validation

Representation

• Programming Exercise: Feature Sets

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: L₂ Regularization, L₂ Regularization and Correlated Features
• Playground: Examining L₂ 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: L₁ Regularization, L₁ vs. L₂ Regularization
• Playground: Examining L₁ Regularization
• Programming Exercise: Sparsity and L₁ 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

• Backpropagation algorithm visual explanation
• Programming Exercise: Improving Neural Net Performance

Multi-Class Neural Nets

• Programming Exercise: Classifying Handwritten Digits with Neural Networks

Embeddings

• Programming Exercise: Intro to Sparse Data and Embeddings

Fairness

• Check Your Understanding: Fairness
• Programming Exercise: Intro to Fairness

Static vs. Dynamic Training

• Check Your Understanding: Online Training, Offline Training

Static vs. Dynamic Inference

• Check Your Understanding: Offline Inference, Online Inference

Data Dependencies

• Check Your Understanding: 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

• Check Your Understanding: Supervised Learning, Features and Labels

Descending into ML

• Check Your Understanding: Mean Squared Error

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

• Playground: Training Sets and Test Sets

Validation

• Check Your Intuition: Validation
• Programming Exercise: Validation

Representation

• Programming Exercise: Feature Sets

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: L₂ Regularization, L₂ Regularization and Correlated Features
• Playground: Examining L₂ 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: L₁ Regularization, L₁ vs. L₂ Regularization
• Playground: Examining L₁ Regularization
• Programming Exercise: Sparsity and L₁ 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

• Backpropagation algorithm visual explanation
• Programming Exercise: Improving Neural Net Performance

Multi-Class Neural Nets

• Programming Exercise: Classifying Handwritten Digits with Neural Networks

Embeddings

• Programming Exercise: Intro to Sparse Data and Embeddings

Fairness

• Check Your Understanding: Fairness
• Programming Exercise: Intro to Fairness

Static vs. Dynamic Training

• Check Your Understanding: Online Training, Offline Training

Static vs. Dynamic Inference

• Check Your Understanding: Offline Inference, Online Inference

Data Dependencies

• Check Your Understanding: Data Dependencies