How Google Does Machine Learning
How Google Does Machine Learning
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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
Course Info
Objectives
- Describe Vertex AI Platform and how it's used to quickly build, train, and deploy AutoML machine learning models without writing a single line of code
- Describe best practices for implementing machine learning on Google Cloud
- Leverage Google Cloud tools and environment to do ML
- Articulate responsible AI best practices
Prerequisites
• Some familiarity with basic machine learning concepts
• Basic proficiency with a scripting language: Python preferred
Audience
• Aspiring machine learning data scientists and engineers
• Machine learning scientists, data scientists, and data analysts
• Data engineers
Available languages
English, español (Latinoamérica), 日本語, français, 한국어, and português (Brasil)
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After finishing this course, you can explore additional content in your learning path or browse the catalog.
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Interested in taking this course with one of our authorized on-demand partners?
Explore Google Cloud content on Coursera and Pluralsight.
Prefer learning with an instructor?
View the public classroom schedule here.
Can I take this course for free?
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