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Applying Machine Learning to your Data with GCP

Google Cloud and Google via Coursera

Overview

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In this module, we define what Machine Learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.

PREREQUISITES
To get the most out of this course, participants must complete the prior courses in this specialization:
• Exploring and Preparing your Data
• Storing and Visualizing your Data
• Architecture and Performance

>>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service

Syllabus

  • Introduction
    • Welcome to the last course in the Data Insights specialization. In this module we will overview the course topics and the labs platform you will be using.
  • Introduction to Machine Learning
    • In this module, we define what Machine Learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels.
  • Pre-trained ML APIs
    • In this module we will dive into pre-built and pre-trained ML models that we can access (like image recognition and sentiment analysis) within Cloud Datalab.
  • Creating ​ML Datasets in BigQuery
  • Creating ML Models in BigQuery
    • In this module, you will learn how to create machine learning models directly inside of BigQuery. You will learn the new syntax and work through the phases of building, evaluating, and testing an ML model.
  • End of Course Recap
    • You've made it to the end! Let's review the lessons learned in the course and what resources are available for continued learning.

Taught by

Google Cloud Training

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