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Vertex AI for tabular data basics

Track :

Artificial Intelligence

Lessons no : 27

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What will you learn in this course?
  • Master data preprocessing and feature engineering techniques for tabular datasets using Vertex AI tools
  • Implement machine learning algorithms optimized for structured data in Vertex AI environments
  • Utilize Vertex AI for automating machine learning workflows and model training processes effectively
  • Deploy, monitor, and scale machine learning models for tabular data in production environments with Vertex AI
  • Apply best practices for data cleaning, normalization, and handling missing values in structured datasets
  • Evaluate model performance using Vertex AI's evaluation tools and interpret results accurately
  • Optimize machine learning models for accuracy, efficiency, and scalability on tabular data
  • Manage and organize large structured datasets efficiently within Google Cloud's Vertex AI platform
  • Leverage Vertex AI's automation features to streamline model development and deployment cycles
  • Develop practical skills for building end-to-end machine learning solutions for tabular data

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  • Watch All Lessons
  • Watch at least 50% of Lesson Duration
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Lessons | 27


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Related Courses

Vertex AI for tabular data course, in this course we will learn about Vertex AI for Tabular Data, focusing on the essential techniques and tools to manage and analyze structured datasets using Google's Vertex AI platform. Vertex AI is a powerful machine learning service that enables users to build, deploy, and scale models efficiently, particularly when working with tabular data. The course covers key topics such as data preprocessing, feature engineering, and selecting appropriate machine learning algorithms tailored for tabular datasets. You'll explore how to use Vertex AI's tools for training and evaluating models, automating machine learning workflows, and deploying models to production environments. Additionally, the course will guide you through best practices for data management, including data cleaning, normalization, and dealing with missing values. You'll also learn how to optimize models for better performance and interpret the results effectively.