Ihre Kompetenzen in der Google Cloud Console anwenden
Amr Samra
Mitglied seit 2020
Bronze League
900 Punkte
Professional Machine Learning Engineer Study Guide
Earned Okt 28, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Feb 22, 2022 EST
Launching into Machine Learning
Earned Nov 17, 2021 EST
How Google Does Machine Learning
Earned Nov 10, 2021 EST
Daten für ML-APIs in Google Cloud vorbereiten
Earned Nov 1, 2021 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Okt 22, 2021 EDT
This course helps learners create a study plan for the PMLE (Professional Machine Learning Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
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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.
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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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Mit dem Skill-Logo zum Kurs Daten für ML-APIs in Google Cloud vorbereiten weisen Sie Grundkenntnisse in folgenden Bereichen nach: Bereinigen von Daten mit Dataprep von Trifacta, Ausführen von Datenpipelines in Dataflow, Erstellen von Clustern und Ausführen von Apache Spark-Jobs in Dataproc sowie Aufrufen von ML-APIs, einschließlich der Cloud Natural Language API, Cloud Speech-to-Text API und Video Intelligence API. Ein Skill-Logo ist ein exklusives digitales Abzeichen, das von Google Cloud ausgestellt wird und Ihre Kenntnisse über unsere Produkte und Dienste belegt. In diesem Zusammenhang wird auch die Fähigkeit bewertet, Ihr Wissen in einer interaktiven praxisnahen Geschäftssituation anzuwenden. Absolvieren Sie eine kursspezifische Aufgabenreihe und die Challenge-Lab-Prüfung, um ein Skill-Logo zu erhalten, das Sie in Ihrem Netzwerk posten können.
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
Professional Machine Learning Engineer Study Guide
Earned Okt 28, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Feb 22, 2022 EST
Launching into Machine Learning
Earned Nov 17, 2021 EST
How Google Does Machine Learning
Earned Nov 10, 2021 EST
Daten für ML-APIs in Google Cloud vorbereiten
Earned Nov 1, 2021 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Okt 22, 2021 EDT