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Ruqiya Bin Safi

Member since 2019

Silver League

4230 points
Badge for Dönüştürücü Modelleri ve BERT Modeli Dönüştürücü Modelleri ve BERT Modeli Earned Haz 24, 2023 EDT
Badge for Dikkat Mekanizması Dikkat Mekanizması Earned Haz 8, 2023 EDT
Badge for Kodlayıcı-Kod Çözücü Mimarisi Kodlayıcı-Kod Çözücü Mimarisi Earned Haz 8, 2023 EDT
Badge for Sorumlu Yapay Zeka'ya Giriş Sorumlu Yapay Zeka'ya Giriş Earned Haz 4, 2023 EDT
Badge for Büyük Dil Modellerine Giriş Büyük Dil Modellerine Giriş Earned Haz 4, 2023 EDT
Badge for Cloud Hero BQML Cloud Hero BQML Earned May 24, 2023 EDT
Badge for Üretken Yapay Zekaya Giriş Üretken Yapay Zekaya Giriş Earned May 17, 2023 EDT
Badge for Serverless Data Processing with Dataflow: Operations Serverless Data Processing with Dataflow: Operations Earned Nis 27, 2023 EDT
Badge for Serverless Data Processing with Dataflow: Develop Pipelines Serverless Data Processing with Dataflow: Develop Pipelines Earned Nis 27, 2023 EDT
Badge for Serverless Data Processing with Dataflow: Foundations Serverless Data Processing with Dataflow: Foundations Earned Nis 22, 2023 EDT
Badge for Smart Analytics, Machine Learning, and AI on Google Cloud Smart Analytics, Machine Learning, and AI on Google Cloud Earned Nis 22, 2023 EDT
Badge for Building Resilient Streaming Analytics Systems on Google Cloud Building Resilient Streaming Analytics Systems on Google Cloud Earned Nis 22, 2023 EDT
Badge for Engineer Data for Predictive Modeling with BigQuery ML Engineer Data for Predictive Modeling with BigQuery ML Earned Nis 21, 2023 EDT
Badge for Building Batch Data Pipelines on Google Cloud Building Batch Data Pipelines on Google Cloud Earned Nis 20, 2023 EDT
Badge for Build a Data Warehouse with BigQuery Build a Data Warehouse with BigQuery Earned Nis 18, 2023 EDT
Badge for Prepare Data for ML APIs on Google Cloud Prepare Data for ML APIs on Google Cloud Earned Nis 17, 2023 EDT
Badge for Google Cloud Big Data and Machine Learning Fundamentals Google Cloud Big Data and Machine Learning Fundamentals Earned Nis 14, 2023 EDT
Badge for Modernizing Data Lakes and Data Warehouses with Google Cloud Modernizing Data Lakes and Data Warehouses with Google Cloud Earned Nis 14, 2023 EDT
Badge for Preparing for your Professional Data Engineer Journey Preparing for your Professional Data Engineer Journey Earned Nis 13, 2023 EDT
Badge for DEPRECATED Creating with Google Maps DEPRECATED Creating with Google Maps Earned Oca 31, 2020 EST
Badge for Google Workspace Essentials Google Workspace Essentials Earned Kas 26, 2019 EST
Badge for Advanced ML: ML Infrastructure Advanced ML: ML Infrastructure Earned Kas 26, 2019 EST
Badge for Intermediate ML: TensorFlow on Google Cloud Intermediate ML: TensorFlow on Google Cloud Earned Kas 25, 2019 EST
Badge for Intro to ML: Language Processing Intro to ML: Language Processing Earned Tem 3, 2019 EDT
Badge for Intro to ML: Image Processing Intro to ML: Image Processing Earned Tem 3, 2019 EDT
Badge for [DEPRECATED] Data Engineering [DEPRECATED] Data Engineering Earned Haz 15, 2019 EDT
Badge for Baseline: Data, ML, AI Baseline: Data, ML, AI Earned May 23, 2019 EDT
Badge for Machine Learning APIs Machine Learning APIs Earned May 23, 2019 EDT
Badge for Google Cloud Essentials Google Cloud Essentials Earned Nis 3, 2019 EDT

Bu kurs, dönüştürücü mimarisini ve dönüştürücülerden çift yönlü kodlayıcı temsilleri (BERT - Encoder Representations from Transformers) modelini tanıtmaktadır. Kursta, öz dikkat mekanizması gibi dönüştürücü mimarisinin ana bileşenlerini ve BERT modelini oluşturmak için dönüştürücünün nasıl kullanıldığını öğreneceksiniz. Ayrıca sınıflandırma, soru yanıtlama ve doğal dil çıkarımı gibi BERT'in kullanılabileceği çeşitli görevler hakkında da bilgi sahibi olacaksınız. Kursun tahmini süresi 45 dakikadır.

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Bu kursta nöral ağların, giriş sırasının belirli bölümlerine odaklanmasına olanak tanıyan güçlü bir teknik olan dikkat mekanizması tanıtılmaktadır. Kursta, dikkat mekanizmasının çalışma şeklini ve makine öğrenimi, metin özetleme ve soru yanıtlama gibi çeşitli makine öğrenimi görevlerinin performansını artırmak için nasıl kullanılabileceğini öğreneceksiniz.

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Bu kursta, kodlayıcı-kod çözücü mimarisi özet olarak anlatılmaktadır. Bu mimari; makine çevirisi, metin özetleme ve soru yanıtlama gibi "sıradan sıraya" görevlerde yaygın olarak kullanılan, güçlü bir makine öğrenimi mimarisidir. Kursta, kodlayıcı-kod çözücü mimarisinin ana bileşenlerini ve bu modellerin nasıl eğitilip sunulacağını öğreneceksiniz. Laboratuvarın adım adım açıklamalı kılavuz bölümünde ise sıfırdan şiir üretmek için TensorFlow'da kodlayıcı-kod çözücü mimarisinin basit bir uygulamasını yazacaksınız.

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Bu kurs, sorumlu yapay zekanın ne olduğunu, neden önemli olduğunu ve Google'ın sorumlu yapay zekayı ürünlerinde nasıl uyguladığını açıklamayı amaçlayan giriş seviyesinde bir mikro öğrenme kursudur. Ayrıca Google'ın 7 yapay zeka ilkesini de tanıtır.

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Bu giriş seviyesi mikro öğrenme kursunda büyük dil modelleri (BDM) nedir, hangi kullanım durumlarında kullanılabileceği ve büyük dil modelleri performansını artırmak için nasıl istem ayarlaması yapabileceğiniz keşfedilecektir. Ayrıca kendi üretken yapay zeka uygulamalarınızı geliştirmenize yardımcı olacak Google araçları hakkında bilgi verilecektir.

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Welcome Gamers! Learn the fundamentals of BQML, all while having fun! In this game, you will learn to use the python-based command line tool for BigQuery. The hands-on labs will help you create a machine learning model, a classification model, and a forecasting model. Earn the points by completing the steps in the lab... Be sure to click "End" when you're done with each lab to get the maximum points. All players will be awarded the game badge.

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Bu, üretken yapay zekanın ne olduğunu, nasıl kullanıldığını ve geleneksel makine öğrenme yöntemlerinden nasıl farklı olduğunu açıklamayı amaçlayan giriş seviyesi bir mikro öğrenme kursudur. Ayrıca kendi üretken yapay zeka uygulamalarınızı geliştirmenize yardımcı olacak Google Araçlarını da kapsar.

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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.

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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.

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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML. 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 the skill badge course, and final assessment challenge lab, to receive a digital badge that you can share with your network.

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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

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Complete the intermediate Build a Data Warehouse with BigQuery skill badge to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery. 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 the skill badge course, and final assessment challenge lab, to receive a digital badge that you can share with your network. For practice with BigQuery fundamentals (including working with the console and command line), complete the course titled BigQuery Basics for Data Analysts.

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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 Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API. 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 course, and the final assessment challenge lab, to receive a skill badge that you can share with your network.

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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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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.

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This course helps learners create a study plan for the PDE (Professional Data 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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In this quest you will use several tools available in Google Cloud to manipulate data and create a Google Map - map location details to find subway stations or a business; use geocoding and Apps Script to send an email of a map; visualize data on a customized map; and build a server-side proxy to create a map on a mobile device.

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Workspace is Google's collaborative applications platform, delivered from Google Cloud. In this introductory-level course you will get hands-on practice with Workspace’s core applications from a user perspective. Although there are many more applications and tool components to Workspace than are covered here, you will get experience with the primary apps: Gmail, Calendar, Sheets and a handful of others. Each lab can be completed in 10-15 minutes, but extra time is provided to allow self-directed free exploration of the applications.

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Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, GCP has a tool for just about any machine learning job. In this advanced-level quest, you will get hands-on practice with machine learning at scale and how to employ the advanced ML infrastructure available on GCP.

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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.

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It’s no secret that machine learning is one of the fastest growing fields in tech, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to language processing by taking labs that will enable you to extract entities from text, and perform sentiment and syntactic analysis as well as use the Speech to Text API for transcription.

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Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.

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This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.

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Big data, machine learning, and artificial intelligence are today’s hot computing topics, but these fields are quite specialized and introductory material is hard to come by. Fortunately, Google Cloud provides user-friendly services in these areas, and with this introductory-level quest, so you can take your first steps with tools like Big Query, Cloud Speech API, and AI Platform. Want extra help? 1-minute videos walk you through key concepts for each lab.

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It's no secret that machine learning is one of the fastest growing fields in tech, and Google Cloud has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level quest, you will get hands-on practice with machine learning APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.

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In this introductory-level course, you get hands-on practice with the Google Cloud’s fundamental tools and services. Optional videos are provided to provide more context and review for the concepts covered in the labs. Google Cloud Essentials is a recommendeded first course for the Google Cloud learner - you can come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first Google Cloud project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.

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