Coursera - Machine Learning on Google Cloud Specialization

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
Tensorflow
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.

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Applied Learning Project

This specialization incorporates hands-on labs using our Qwiklabs platform.

These hands on components will let you apply the skills you learn in the video lectures. Projects will incorporate topics such as Google Cloud Platform products, which are used and configured within Qwiklabs. You can expect to gain practical hands-on experience with the concepts explained throughout the modules.

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