Instructor-led (classroom)
Advanced
13 hours
English
In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization (https://www.coursera.org/specializations/machine-learning-tensorflow-gcp).
One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform
A 360-degree learning approach that you can adapt to your learning style
Engage and learn more with these live and highly-interactive classes alongside your peers
24/7 Teaching Assistance Keep engaged with integrated teaching
Projects provide you with sample work to show prospective employers.
Real-world projects relevant to what you’re learning throughout the program
A support team focused on helping you succeed alongside a peer community
Welcome to the Course
We’ll give you an overview of this course and reveal the upcoming Advanced Machine Learning with TensorFlow on GCP specialization.
Machine Learning (ML) on Google Cloud Platform (GCP)
This module reviews the steps of deploying machine learning in a production environment
Explore the Data
This module explores a large dataset using Datalab and BigQuery.
Create the dataset
This modules shows how to use Pandas in Datalab and sample a dataset for local development.
Build the Model
This module let’s you develop a machine learning model in Tensorflow.
Operationalize the model
This module explains how to preprocess data at scale for machine learning and lets you train a machine learning model at scale on Cloud AI Platform.
Summary
This module reviews what you’ve learned in this course.
A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer is proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation and needs familiarity with application development, infrastructure management, data engineering, and security.
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