Launching into Machine Learning
Launching into Machine Learning
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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
Course Info
Objectives
- Describe how to improve data quality and perform exploratory data analysis
- Build and train AutoML Models using Vertex AI and BigQuery ML
- Optimize and evaluate models using loss functions and performance metrics
- Create repeatable and scalable training, evaluation, and test datasets
Prerequisites
Familiarity with Python or other programming languages
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, português (Brasil), and italiano
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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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