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Apply your skills in Google Cloud console

Mohammad Khalid Mahmod Asif

Member since 2024

Diamond League

24995 points
Gemini for Data Scientists and Analysts Earned May 23, 2024 EDT
Applying Machine Learning to your Data with Google Cloud Earned May 22, 2024 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned May 21, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned May 20, 2024 EDT
Generative AI Explorer - Vertex AI Earned May 19, 2024 EDT
Intro to ML: Image Processing Earned May 19, 2024 EDT
Intro to ML: Language Processing Earned May 19, 2024 EDT
Perform Predictive Data Analysis in BigQuery Earned May 17, 2024 EDT
Working with Notebooks in Vertex AI Earned May 16, 2024 EDT
Create ML Models with BigQuery ML Earned May 14, 2024 EDT
Analyze Speech and Language with Google APIs Earned May 11, 2024 EDT
Monitor and Manage Google Cloud Resources Earned May 10, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud Earned May 7, 2024 EDT
Build Infrastructure with Terraform on Google Cloud Earned May 6, 2024 EDT
Create and Manage Cloud SQL for PostgreSQL Instances Earned May 2, 2024 EDT
Temel: Altyapı Earned Nis 29, 2024 EDT
Analyze Sentiment with Natural Language API Earned Nis 29, 2024 EDT
Analyze Images with the Cloud Vision API Earned Nis 29, 2024 EDT
Classify Images with TensorFlow on Google Cloud Earned Nis 28, 2024 EDT
Integrating Applications with Gemini 1.0 Pro on Google Cloud Earned Nis 25, 2024 EDT

In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps analyze customer data and predict product sales. You also learn how to identify, categorize, and develop new customers using customer data in BigQuery. Using hands-on labs, you experience how Gemini improves data analysis and machine learning workflows. Duet AI was renamed to Gemini, our next-generation model.

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In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.

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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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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 Generative AI Explorer - Vertex Quest is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the models in the Vertex AI PaLM API family, including text-bison, chat-bison, and textembedding-gecko. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via Vertex AI custom training and deploy it to a Vertex AI endpoint.

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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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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 course, 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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Complete the intermediate Perform Predictive Data Analysis in BigQuery skill badge course to demonstrate skills in the following: creating datasets in BigQuery by importing CSV and JSON files; harnessing the power of BigQuery with sophisticated SQL analytical concepts, including using BigQuery ML to train an expected goals model on soccer event data and evaluate the impressiveness of World Cup goals.

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This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.

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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.

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Earn a skill badge by completing the Analyze Speech and Language with Google APIs quest, where you learn how to use the Natural Language and Speech APIs in real-world settings.

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Complete the introductory Monitor and Manage Google Cloud Resources skill badge to demonstrate skills in the following: granting and revoking IAM permissions; installing monitoring and logging agents; creating, deploying, and testing an event-driven Cloud Run function.

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This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.

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Complete the intermediate Build Infrastructure with Terraform on Google Cloud skill badge to demonstrate skills in the following: Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization.

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Complete the introductory Create and Manage Cloud SQL for PostgreSQL Instances skill badge to demonstrate skills in the following: migrating, configuring, and managing Cloud SQL for PostgreSQL instances and databases.

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Bulut geliştirme konusunda yeniyseniz ve Google Cloud'un Temel Özellikleri kursunun da ötesinde uygulamalı alıştırma yapmak istiyorsanız bu kurs tam size göre. Cloud Storage'ın yanı sıra Monitoring ve Cloud Functions gibi diğer önemli uygulama hizmetlerini ayrıntılı bir şekilde inceleyen laboratuvarlarla uygulamalı deneyim kazanacaksınız. Her türlü Google Cloud girişiminde kullanabileceğiniz değerli beceriler geliştireceksiniz.

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Earn a skill badge by completing the Analyze Sentiment with Natural Language API quest, where you learn how the API derives sentiment from text.

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Earn a skill badge by completing the Analyze Images with the Cloud Vision API quest, where you discover how to leverage the Cloud Vision API for various tasks, including extracting text from images.

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Earn the intermediate Skill Badge by completing the Classify Images with TensorFlow on Google Cloud skill badge course where you learn how to use TensorFlow and Vertex AI to create and train machine learning models. You primarily interact with Vertex AI Workbench user-managed notebooks.

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This short course on integrating applications with Gemini 1.0 Pro models on Google Cloud helps you discover the Gemini API and its generative AI models. The course teaches you how to access the Gemini 1.0 Pro and Gemini 1.0 Pro Vision models from code. It lets you test the capabilities of the models with text, image, and video prompts from an app.

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