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ريهام اشرف
Member since 2024
Gold League
36620 points
Machine Learning Operations (MLOps): Getting Started
Earned אוג 6, 2024 EDT
Recommendation Systems on Google Cloud
Earned יול 19, 2024 EDT
Machine Learning in the Enterprise
Earned יול 18, 2024 EDT
[DEPRECATED] Feature Engineering
Earned יול 14, 2024 EDT
Deploy and Manage Applications on Google App Engine
Earned יול 14, 2024 EDT
Networking in Google Cloud: Hands-On Practice
Earned יול 14, 2024 EDT
Build a Secure Google Cloud Network
Earned יול 13, 2024 EDT
DEPRECATED Networking Fundamentals on Google Cloud
Earned יול 13, 2024 EDT
Introduction to Generative AI - בעברית
Earned יול 13, 2024 EDT
Mitigate Threats and Vulnerabilities with Security Command Center
Earned יול 13, 2024 EDT
Level 3: GenAIus Travels
Earned יול 12, 2024 EDT
Level 2: Develop and Deploy
Earned יול 12, 2024 EDT
Implement Cloud Security Fundamentals on Google Cloud
Earned יול 12, 2024 EDT
DEPRECATED Build and Deploy Machine Learning Solutions on Vertex AI
Earned יול 3, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned יול 2, 2024 EDT
The Arcade Trivia June 2024 Week 4
Earned יונ 30, 2024 EDT
The Arcade Trivia June 2024 Week 3
Earned יונ 29, 2024 EDT
Natural Language Processing on Google Cloud
Earned יונ 28, 2024 EDT
The Arcade Trivia June 2024 Week 2
Earned יונ 28, 2024 EDT
The Arcade Trivia June 2024 Week 1
Earned יונ 27, 2024 EDT
How Google Does Machine Learning
Earned יונ 23, 2024 EDT
Launching into Machine Learning
Earned יונ 20, 2024 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned יונ 14, 2024 EDT
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.
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This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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Earn a Skill Badge by completing the Deploy and Manage Applications on Google App Engine course, where you learn how to use App Engine with Python, Go, and PHP.
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This course consists of a series of labs, designed to provide the learner hands-on experience performing a variety of tasks pertaining to setup and maintenance of their Google VPC networks.
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Earn a skill badge by completing the Build a Secure Google Cloud Network skill badge course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud.
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Earn a skill badge by completing the Networking Fundamentals on Google Cloud quest, where you learn how to work with VPC networks and load balancers on Google Cloud. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this Skill Badge, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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זהו קורס מבוא ממוקד שמטרתו להסביר מהי בינה מלאכותית גנרטיבית, איך משתמשים בה ובמה היא שונה משיטות מסורתיות של למידת מכונה. הוא גם כולל הסבר על הכלים של Google שיעזרו לכם לפתח אפליקציות בינה מלאכותית גנרטיבית משלכם.
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Complete the intermediate Mitigate Threats and Vulnerabilities with Security Command Center skill badge course to demonstrate skills in the following: preventing and managing environment threats, identifying and mitigating application vulnerabilities, and responding to security anomalies.
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Excited to follow your favorite soccer/football stars on their next quest? Use GenAIus Travel Guides to learn how to interact with chat applications, master prompt engineering, understand the importance of context in AI, and work with Generative AI. Earn an exclusive Google Cloud Generative AI Credential and showcase your new skills! No prior experience needed!
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Bring your ideas to life by gaining hands-on experience in application development and deployment on Google Cloud Platform. Master security, traffic management, and learn to use development frameworks while earning a Google Cloud Credential. No prior experience needed!
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Complete the intermediate Implement Cloud Security Fundamentals on Google Cloud skill badge course to demonstrate skills in the following: creating and assigning roles with Identity and Access Management (IAM); creating and managing service accounts; enabling private connectivity across virtual private cloud (VPC) networks; restricting application access using Identity-Aware Proxy; managing keys and encrypted data using Cloud Key Management Service (KMS); and creating a private Kubernetes cluster.
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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI skill badge course, where you learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models.
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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Managed Service for Apache Spark, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.
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Hey there! You're invited to game on with the Arcade Trivia for June Week 4! Play throughout the month and boost your cloud learning journey. Every week, we'll release a new set of questions to test your knowledge of Google Cloud Platform. Get started now and earn the June Trivia Week 4 badge!
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Hey there! You're invited to game on with the Arcade Trivia for June Week 3! Play throughout the month and boost your cloud learning journey. Every week, we'll release a new set of questions to test your knowledge of Google Cloud Platform. Get started now and earn the June Trivia Week 3 badge!
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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
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Hey there! You're invited to game on with the Arcade Trivia for June Week 2! Play throughout the month and boost your cloud learning journey. Every week, we'll release a new set of questions to test your knowledge of Google Cloud Platform. Get started now and earn the June Trivia Week 2 badge!
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Hey there! You're invited to game on with the Arcade Trivia for June Week 1! Play throughout the month and boost your cloud learning journey. Every week, we'll release a new set of questions to test your knowledge of Google Cloud Platform. Get started now and earn the June Trivia Week 1 badge!
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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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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 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.
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