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Wiktor Kuranowski

Member since 2024

Silver League

19995 points
Responsible AI: Applying AI Principles with Google Cloud Earned يوليو 16, 2025 EDT
Introduction to Responsible AI Earned يوليو 9, 2025 EDT
Introduction to Large Language Models Earned يوليو 9, 2025 EDT
Introduction to Generative AI Earned يوليو 9, 2025 EDT
Gemini in Google Sheets Earned ديسمبر 31, 2024 EST
BigQuery for Data Analysts Earned يوليو 31, 2024 EDT
Gemini for Data Scientists and Analysts Earned يوليو 30, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud Earned يوليو 30, 2024 EDT
Understand Your Google Cloud Costs Earned يوليو 29, 2024 EDT
Forecasting using Vertex AI AutoML Earned يوليو 26, 2024 EDT
Exploring and Preparing your Data with BigQuery Earned يوليو 25, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned يوليو 12, 2024 EDT
Create ML Models with BigQuery ML Earned يوليو 11, 2024 EDT
Google Cloud Fundamentals: Core Infrastructure Earned يوليو 5, 2024 EDT

As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you. In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.

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This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 3 AI principles.

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This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps.

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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.

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Gemini for Google Workspace provides customers with generative AI features in Google Workspace. In this mini-course, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Sheets.

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This course is designed for data analysts who want to learn about using BigQuery for their data analysis needs. Through a combination of videos, labs, and demos, we cover various topics that discuss how to ingest, transform, and query your data in BigQuery to derive insights that can help in business decision making.

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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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This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.

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This Quest is most suitable for those working in a technology or finance role who are responsible for managing Google Cloud costs. You’ll learn how to set up a billing account, organize resources, and manage billing access permissions. In the hands-on labs, you'll learn how to view your invoice, track your Google Cloud costs with Billing reports, analyze your billing data with BigQuery or Google Sheets, and create custom billing dashboards with Looker Studio. References made to links in the videos can be accessed in this Additional Resources document.

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This specialized course provides data practitioners with a practical introduction to developing end-to-end forecasting solutions on Google Cloud. Learners work through hands-on labs that cover time series data ingestion into managed datasets, building AutoML forecasting models in Vertex AI, and adding forecasting workflow automation with Vertex AI Pipelines.

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In this course, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Dataprep to analyze and transform your datasets. This is the first course of the From Data to Insights with Google Cloud series. After completing this course, enroll in the Creating New BigQuery Datasets and Visualizing Insights course.

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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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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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Google Cloud Fundamentals: Core Infrastructure introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.

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