GCP Data Engineer Pro: Google Machine Learning and AI
Google Cloud 2024
| Intermediate
- 13 videos | 1h 30m 13s
- Earns a Badge
Over time, humanity has learned from patterns, beginning with recognizing what characteristics indicate poisonous plants versus edible botanicals. In the same way, machine learning (ML) is all about recognizing the patterns in data so useful predictions can be made. In this course, explore ML systems tailored for diverse objectives, integrate ML into a data warehouse environment, different GCP ML products, and prebuilt models. Next, learn how to develop a project with a prebuilt ML model, the rationale for creating new models, and the ML data preparation process. Finally, discover how to create a custom-trained model using the Vertex AI SDK for Python and compare Vertex AI and BigQuery. This course is one of a collection that prepares learners for the Google Professional Data Engineer exam.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this courseOutline what machine learning (ml) is and is notDifferentiate machine learning systems that can be used with different goalsIdentify the standard steps associated with incorporating machine learning into a data warehouse processList the different products available for leveraging machine learningRecognize in which scenarios prebuilt models would meet a business use caseCreate a project that utilizes a prebuilt model for machine learning
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Outline the use case and purpose of developing new models and the difference between automl and custom model trainingIdentify the process and key steps involved in preparing data to be incorporated into machine learningCreate a custom-trained model using the vertex ai sdk for pythonDifferentiate vertex ai from bigquery and describe how vertex ai can be integrated with bigquery workflowsRecognize common issues that can arise with machine learning and how to troubleshoot themSummarize the key concepts covered in this course
IN THIS COURSE
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1m 16sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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11m 9sUpon completion of this video, you will be able to outline what machine learning (ML) is and is not. FREE ACCESS
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10m 5sAfter completing this video, you will be able to differentiate machine learning systems that can be used with different goals. FREE ACCESS
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6m 6sIn this video, we will identify the standard steps associated with incorporating machine learning into a data warehouse process. FREE ACCESS
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9m 3sUpon completion of this video, you will be able to list the different products available for leveraging machine learning. FREE ACCESS
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4m 50sThrough this video, you will be able to recognize in which scenarios prebuilt models would meet a business use case. FREE ACCESS
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5m 5sDiscover how to create a project that utilizes a prebuilt model for machine learning. FREE ACCESS
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9m 11sIn this video, we will outline the use case and purpose of developing new models and the difference between AutoML and custom model training. FREE ACCESS
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8m 39sAfter completing this video, you will be able to identify the process and key steps involved in preparing data to be incorporated into machine learning. FREE ACCESS
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7m 24sDuring this video, discover how to create a custom-trained model using the Vertex AI SDK for Python. FREE ACCESS
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6m 58sUpon completion of this video, you will be able to differentiate Vertex AI from BigQuery and describe how Vertex AI can be integrated with BigQuery workflows. FREE ACCESS
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9m 31sThrough this video, you will be able to recognize common issues that can arise with machine learning and how to troubleshoot them. FREE ACCESS
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58sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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