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Google Cloud Platform for Machine Learning Essential Training


Introduction
Build complete solutions with machine learning services
What you should know
About using cloud services

1. Machine Learning on Google Cloud Platform
Business scenarios for machine learning
Which algorithm should you use?
GCP AI servers vs. platforms
Enable GCP ML APIs
Data preparation with Cloud Dataflow and Cloud Dataprep
An ML notebook in action: Colaboratory
An ML notebook in action: Set up Cloud Datalab
An ML notebook in action: Use Cloud Datalab

2. Machine Learning API Services
Overview of GCP ML APIs
Predict via the Cloud Vision API for images
Predict via the Cloud Video Intelligence API for video
Predict via the Natural Language API for NLP
Predict via the Text-to-Speech API
Predict via the Speech-to-Text API
Predict via the Cloud Translation API
Predict via BigQuery ML

3. Machine Learning with AutoML
Understand Cloud AutoML services
Understand AutoML Vision
Prepare data and labels for AutoML Vision
Train model for AutoML Vision
Evaluate model with AutoML Vision
Predict using a trained AutoML Vision model

4. Advanced Machine Learning
Why build custom ML models?
Using containers to host ML models
Use Cloud ML Engine
Evaluate Cloud ML Engine output
Scale custom ML models
Understanding deep learning
Work with TensorBoard
Work with Keras for TensorFlow
GPUs and TPUs for TensorFlow
TensorFlow for JavaScript and mobile

5. Machine Learning Architectures
Chatbot with ML
Image search with Cloud Vision and Cloud ML
GCP serverless machine learning architecture
GCP machine learning with structured data
GCP ML service for IoT apps

Conclusion
Next steps