Machine Learning on Google Cloud

Completed by Gant Laborde

December 30, 2018

Approximately 15 weeks to complete.

Course Certificates Completed

Feature Engineering

Machine Learning in the Enterprise

Launching into Machine Learning

TensorFlow on Google Cloud

How Google does Machine Learning

View certificate for Gant Laborde, Machine Learning on Google Cloud, offered through Coursera. In this five-course specialization, participants learn to build, train, and deploy Vertex AI AutoML and BigQuery ML models; create Vertex AI custom training jobs and deploy using Docker containers; use Feature Store; perform feature engineering; and choose data preprocessing options. They also learn to use Vertex Vizier hyperparameter tuning to add the right mix of parameters that yields accurate, generalized models and the theory to solve specific types of ML problems. They learn to write distributed ML models that scale in TensorFlow; and leverage best practices to implement machine learning on Google Cloud. They experimented with end-to-end ML — building an ML-focused strategy, then model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.

Course Certificates

Earned after completing each course in the Specialization

Feature Engineering

Google Cloud

Taught by: Google Cloud Training

Completed by: Gant Laborde by December 27, 2018

2 weeks of study, 5-7 hours per week

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Machine Learning in the Enterprise

Google Cloud

Taught by: Google Cloud Training

Completed by: Gant Laborde by December 30, 2018

3 weeks of study, 5-7 hours per week

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Launching into Machine Learning

Google Cloud

Taught by: Google Cloud Training

Completed by: Gant Laborde by December 22, 2018

5 - 7 hours per week

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TensorFlow on Google Cloud

Google Cloud

Taught by: Google Cloud Training

Completed by: Gant Laborde by December 24, 2018

4 weeks of study, 8-10 hours/week

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How Google does Machine Learning

Google Cloud

Taught by: Google Cloud Training

Completed by: Gant Laborde by December 16, 2018

1 week of study, 8-10 hours/week

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