Enterprise Services: Machine Learning Implementation on Google Cloud Platform
Machine Learning
14 Videos | 1h 6m 59s - Includes Assessment
- Earns a Badge
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
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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
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1.Course Overview1m 49sUP NEXT
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2.GCP Machine Learning Tools and Capabilities5m 14s
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3.Google Cloud Platform ML Capabilities6m
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4.Training and Job Execution with GCloud and Console6m 35s
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5.BigQuery and BigQuery ML Features3m 39s
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6.Implementing Models with BigQuery ML5m 28s
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7.ML Workflow Challenges and Serverless Approach6m 50s
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8.ML Implementation with Cloud Datalab7m 22s
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9.Google AI Platform Features and Components6m 19s
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10.Google Cloud AutoML Features3m 21s
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11.Managing Dataset Using AutoML Tables2m 13s
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12.Training Models and Predicting with AutoML Tables2m 23s
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13.Google Cloud AutoML Natural Language2m 35s
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14.Course Summary1m 11s
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