Enterprise Services: Machine Learning Implementation on Google Cloud Platform

Machine Learning    |    Intermediate
  • 14 videos | 1h 59s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 14 users Rating 4.5 of 14 users (14)
This course explores the GCP (Google Cloud Platform) machine learning (ML) tools, services, and capabilities, and different stages in the Google Cloud Platform machine learning workflow. This 14-video course demonstrates a high-level overview of different stages in Google Cloud Platform machine learning workflow. You will examine the features of BigQuery, and how to use Big Query ML to create and evaluate a binary logistic regression model using Big Query ML statement. Next, learners will observe how to use the Google AI Platform and Google Cloud AutoML components and features used for training, evaluating, and deploying ML models. You will learn to train models by using the built-in linear learner algorithm, submit jobs with GCloud and Console, create and evaluate binary logistic regression models, and set up and work with Cloud Datalab. You will learn to use AutoML Tables to work with data sets, to train machine learning models for predictions. Finally, you will work with Google Cloud AutoML Natural Language to create custom ML models for content category classification.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Describe gcp machine learning tools, services, and capabilities
    Describe the google cloud platform machine learning implementation approach and the different stages in google cloud platform machine learning workflow
    Train models using the built-in linear learner algorithm and submit jobs with gcloud and console
    Recall the essential features of bigquery along with the capabilities of bigquery ml
    Create and evaluate binary logistic regression models using bigquery ml statements
    Recognize the challenges associated with modern machine learning workflows and how you can leverage the serverless approach to eliminate those challenges
  • Set up and work with cloud datalab
    Recognize google ai platform components and features that can be used to build machine learning workflows and train machine learning models at scale
    Recall google cloud automl features and how it can be used to train, evaluate, and deploy machine learning models
    Use automl tables to work with datasets needed to train and use machine learning models
    Work with automl tables to train machine learning models using imported datasets and use the trained models for predictions
    Work with google cloud automl natural language to create custom machine learning models for content category classification
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 49s
  • 5m 14s
    After completing this video, you will be able to describe Google Cloud Platform machine learning tools, services, and capabilities. FREE ACCESS
  • Locked
    3.  Google Cloud Platform ML Capabilities
    6m
    After completing this video, you will be able to describe the Google Cloud Platform machine learning implementation approach and the different stages in the Google Cloud Platform machine learning workflow. FREE ACCESS
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    4.  Training and Job Execution with GCloud and Console
    6m 35s
    In this video, find out how to train models using the built-in linear learner algorithm and submit jobs with Google Cloud and Console. FREE ACCESS
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    5.  BigQuery and BigQuery ML Features
    3m 39s
    Upon completion of this video, you will be able to recall the essential features of BigQuery and the capabilities of BigQuery ML. FREE ACCESS
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    6.  Implementing Models with BigQuery ML
    5m 28s
    In this video, you will create and evaluate binary logistic regression models using BigQuery ML statements. FREE ACCESS
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    7.  ML Workflow Challenges and Serverless Approach
    6m 50s
    Upon completion of this video, you will be able to recognize the challenges associated with modern machine learning workflows and how you can leverage the serverless approach to eliminate those challenges. FREE ACCESS
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    8.  ML Implementation with Cloud Datalab
    7m 22s
    During this video, you will learn how to set up and work with Cloud Datalab. FREE ACCESS
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    9.  Google AI Platform Features and Components
    6m 19s
    Upon completion of this video, you will be able to recognize Google AI Platform components and features that can be used to build machine learning workflows and train machine learning models at scale. FREE ACCESS
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    10.  Google Cloud AutoML Features
    3m 21s
    After completing this video, you will be able to recall Google Cloud AutoML features and how to use it to train, evaluate, and deploy machine learning models. FREE ACCESS
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    11.  Managing Dataset Using AutoML Tables
    2m 13s
    Learn how to use AutoML Tables to work with datasets needed to train and use machine learning models. FREE ACCESS
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    12.  Training Models and Predicting with AutoML Tables
    2m 23s
    During this video, you will learn how to work with AutoML Tables to train machine learning models using imported datasets and use the trained models for predictions. FREE ACCESS
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    13.  Google Cloud AutoML Natural Language
    2m 35s
    Learn how to work with Google Cloud AutoML Natural Language to create custom machine learning models for content classification. FREE ACCESS
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    14.  Course Summary
    1m 11s

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