Machine Learning Crash Course with TensorFlow APIs

A self-study guide for aspiring machine learning practitioners. Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.

Some of the questions answered in this course:

• Learn best practices from Google experts on key machine learning concepts.
• How does machine learning differ from traditional programming?
• What is loss, and how do I measure it?
• How does gradient descent work?
• How do I determine whether my model is effective?
• How do I represent my data so that a program can learn from it?
• How do I build a deep neural network?

ML Concepts

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

ML Engineering

• Production ML Systems
• Static vs Dynamic Training
• Static vs Dynamic Inference
• Data Dependencies

ML Real World Examples

• Cancer Prediction
• 18th Century Literature
• Real-World Guidelines

Conclusion

• Next Steps