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Pluralsight

Launching into Machine Learning

via Pluralsight

Overview

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.

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.

Syllabus

  • Introduction 0mins
  • Introduction 0mins
  • Get to Know Your Data: Improve Data through Exploratory Data Analysis 56mins
  • Get to Know Your Data: Improve Data through Exploratory Data Analysis 52mins
  • Machine Learning in Practice 45mins
  • Machine Learning in Practice 45mins
  • Training AutoML Models Using Vertex AI 30mins
  • Training AutoML Models Using Vertex AI 30mins
  • BigQuery Machine Learning: Develop ML Models Where Your Data Lives 30mins
  • BigQuery Machine Learning: Develop ML Models Where Your Data Lives 30mins
  • Optimization 57mins
  • Optimization 57mins
  • Generalization and Sampling 28mins
  • Generalization and Sampling 28mins
  • Summary 0mins
  • Summary 0mins

Taught by

Pluralsight

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