Learn machine learning with Google Cloud. Real-world experimentation with end-to-end ML
What you’ll learn
Use Vertex AI AutoML and BigQuery ML to build, train, and deploy ML models
Implement machine learning models using Keras and TensorFlow 2.x
Implement machine learning in the enterprise best practices
Describe how to perform exploratory data analysis and improve data quality
Skills you’ll gain
- Vertex AI
- Machine Learning
- Feature Engineering
- Cloud Computing
What is machine learning, and what kinds of problems can it solve? How can you build, train, and deploy machine learning models at scale without writing a single line of code? When should you use automated machine learning or custom training?
This course teaches you how to build Vertex AI AutoML models without writing a single line of code; build BigQuery ML models knowing basic SQL; create Vertex AI custom training jobs you deploy using containers (with little knowledge of Docker); use Feature Store for data management and governance; use feature engineering for model improvement; determine the appropriate data preprocessing options for your use case; use Vertex Vizier hyperparameter tuning to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems, write distributed ML models that scale in TensorFlow; and leverage best practices to implement machine learning on Google Cloud.
Last Updated 9/2023