Google ML Course Notebook

Table of Contents

ML Concepts

• Framing
• Descending into ML
• Reducing Loss
• First Steps with Tensorflow
• Generalization
• Training and Test Sets
• Validation
• Representation
• Feature Crosses
• Regularization: Simplicity
• Logistic Regression
• Classification
• Regularization: Sparsity
• Introduction to Naural Networks
• Multi-Class Neural Networks
• Embeddings

ML Engineering

• TBD

ML Real World Examples

• TBD