Generative AI Explorer - Vertex AI
Earned Dec 10, 2024 EST
Build Generative AI Apps with Firebase Genkit
Earned Dec 5, 2024 EST
Explore Generative AI with the Vertex AI Gemini API
Earned Nov 28, 2024 EST
Develop Serverless Apps with Firebase
Earned Nov 21, 2024 EST
Gemini for Application Developers
Earned Oct 31, 2024 EDT
Derive Insights from BigQuery Data
Earned Oct 30, 2024 EDT
Create a Streaming Data Lake on Cloud Storage
Earned Oct 30, 2024 EDT
Professional Machine Learning Engineer Study Guide
Earned Oct 28, 2024 EDT
Boost Productivity with Gemini in BigQuery
Earned Oct 25, 2024 EDT
Using BigQuery Machine Learning for inference
Earned Oct 25, 2024 EDT
Work with Gemini Models in BigQuery
Earned Oct 24, 2024 EDT
Gemini for Data Scientists and Analysts
Earned Oct 23, 2024 EDT
Analyze Sentiment with Natural Language API
Earned Oct 22, 2024 EDT
Create Generative AI Apps on Google Cloud
Earned Oct 17, 2024 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned Oct 17, 2024 EDT
Feature Engineering
Earned Oct 10, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
Earned Oct 9, 2024 EDT
BigQuery for Data Analysts
Earned Oct 8, 2024 EDT
Create ML Models with BigQuery ML
Earned Oct 5, 2024 EDT
BigQuery for Machine Learning
Earned Oct 2, 2024 EDT
Creating New BigQuery Datasets and Visualizing Insights
Earned Sep 30, 2024 EDT
Introduction to Data Analytics on Google Cloud
Earned Sep 25, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned Sep 19, 2024 EDT
Launching into Machine Learning
Earned Sep 19, 2024 EDT
Steering Bot with Conversational Agents
Earned Sep 6, 2024 EDT
Conversational AI on Vertex AI and Dialogflow CX
Earned Aug 13, 2024 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned Jun 13, 2024 EDT
Vector Search and Embeddings
Earned Jun 9, 2024 EDT
Introduction to Vertex AI Studio
Earned Jun 7, 2024 EDT
Create Image Captioning Models
Earned Jun 3, 2024 EDT
Transformer Models and BERT Model
Earned Jun 3, 2024 EDT
Encoder-Decoder Architecture
Earned Jun 1, 2024 EDT
Attention Mechanism
Earned Jun 1, 2024 EDT
Introduction to Image Generation
Earned Jun 1, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned May 31, 2024 EDT
Prompt Design in Vertex AI
Earned May 30, 2024 EDT
Introduction to Responsible AI
Earned May 28, 2024 EDT
Introduction to Large Language Models
Earned May 28, 2024 EDT
Introduction to Generative AI
Earned May 28, 2024 EDT
Gemini for end-to-end SDLC
Earned May 21, 2024 EDT
Gemini in Google Meet
Earned May 21, 2024 EDT
Gemini in Google Sheets
Earned May 21, 2024 EDT
Gemini in Google Slides
Earned May 21, 2024 EDT
Gemini in Google Docs
Earned May 21, 2024 EDT
Gemini in Gmail
Earned May 21, 2024 EDT
Introduction to Gemini for Google Workspace
Earned May 21, 2024 EDT
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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This course equips app developers with the skills to integrate generative AI features into their applications using Firebase Genkit. You learn how to leverage Firebase Genkit's capabilities for backend flows and seamless model execution, all using Node.js. The course guides you through the entire process, from prototyping to production, providing a pattern for building next-generation AI-powered applications.
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Complete the intermediate Explore Generative AI with the Vertex AI Gemini API skill badge to demonstrate skills in the following: text generation, image and video analysis for enhanced content creation, and applying function calling techniques within the Gemini API. Discover how to leverage sophisticated Gemini techniques, explore multimodal content generation, and expand the capabilities of your AI-powered projects. 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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Complete the intermediate Develop Serverless Apps with Firebase skill badge to demonstrate skills in the following: architecting and building serverless web applications with Firebase, utilizing Firestore for database management, automating deployment processes using Cloud Build, and integrating Google Assistant functionality into your applications. 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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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps developers build applications. You learn how to prompt Gemini to explain code, recommend Google Cloud services, and generate code for your applications. Using a hands-on lab, you experience how Gemini improves the application development workflow.
Duet AI was renamed to Gemini, our next-generation model.
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Complete the introductory Derive Insights from BigQuery Data skill badge to demonstrate skills in the following: write SQL queries, query public tables, load sample data into BigQuery, troubleshoot common syntax errors with the query validator in BigQuery, and create reports in Looker Studio by connecting to BigQuery data. 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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Earn a skill badge by completing the Create a Streaming Data Lake on Cloud Storage course, where you use Pub/Sub, Dataflow, and Cloud Storage together to create a streaming data lake 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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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 explores Gemini in BigQuery, a suite of AI-driven features to assist data-to-AI workflow. These features include data exploration and preparation, code generation and troubleshooting, and workflow discovery and visualization. Through conceptual explanations, a practical use case, and hands-on labs, the course empowers data practitioners to boost their productivity and expedite the development pipeline.
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Learn about BigQuery ML for Inference, why Data Analysts should use it, its use cases, and supported ML models. You will also learn how to create and manage these ML models in BigQuery.
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This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.
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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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Earn a skill badge by completing the Analyze Sentiment with Natural Language API quest, where you will learn how the API derrives sentiment from text. 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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Generative AI applications can create new user experiences that were nearly impossible before the invention of large language models (LLMs). As an application developer, how can you use generative AI to build engaging, powerful apps on Google Cloud?
In this course, you'll learn about generative AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs. You'll learn about a production-ready architecture that can be used for generative AI applications and you'll build an LLM and RAG-based chat application.
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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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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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This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production.
Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Vertex AI platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.
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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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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in the following: creating and evaluating machine learning models with BigQuery ML to make data predictions. 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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Want to build ML models in minutes instead of hours using just SQL? BigQuery ML democratizes machine learning by letting data analysts create, train, evaluate, and predict with machine learning models using existing SQL tools and skills. In this series of labs, you will experiment with different model types and learn what makes a good model. 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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This is the second course in the Data to Insights course series. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Looker Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources.
Note: Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you.
After completing this course, enroll in the Achieving Advanced Insights with BigQuery course.
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This introductory course explores the basics of data analysis, including collection, storage, exploration, visualization, and sharing. This course also introduces Google Cloud's data analytics tools and services. Through video lectures, demos, quizzes, and hands-on labs, this course demonstrates how to go from raw data to impactful visualizations and dashboards. Whether you already work with data and want to learn how to be successful on Google Cloud, or you’re looking to progress in your career, this course will help you get started.
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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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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 how to develop a simple chat agent capable of identifying the user's intent and route them appropriately to the right self service flow. Please note Dialogflow CX was recently renamed to Conversational Agents, Virtual agent renamed to Conversational agent, and CCAI Insights were renamed to Conversational Insights, and this course is in the process of being updated to reflect the new product names for Dialogflow CX, and Virtual Agent, CCAI Insights.
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In this course you will learn how to use the new generative AI features in Dialogflow CX to create virtual agents that can have more natural and engaging conversations with customers. Discover how to deploy generative fallback responses to gracefully handle errors and omissions in customer conversations, deploy generators to increase intent coverage, and structure, ingest, and manage data in a data store. And explore how to deploy and maintain generative AI agents using your data, and deploy and maintain hybrid agents in combination with existing intent-based design paradigms.
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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.
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This course introduces Vertex AI Vector Search and describes how it can be used to build a search application with large language model (LLM) APIs for embeddings. The course consists of conceptual lessons on vector search and text embeddings, practical demos on how to build vector search on Vertex AI, and a hands-on lab.
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This course introduces Vertex AI Studio, a tool for prototyping and customizing generative AI models. Through immersive lessons, engaging demos, and a hands-on lab, you'll explore the generative AI workflow and learn how to leverage Vertex AI Studio for Gemini multimodal applications, prompt design, and model tuning. The aim is to enable you to unlock the potentials of these models in your projects with Vertex AI Studio.
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This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images
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This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.
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This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.
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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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Complete the introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios. 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 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 7 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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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps you use Google products and services to develop, test, deploy, and manage applications. With help from Gemini, you learn how to develop and build a web application, fix errors in the application, develop tests, and query data. Using a hands-on lab, you experience how Gemini improves the software development lifecycle (SDLC).
Duet AI was renamed to Gemini, our next-generation model.
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Gemini for Google Workspace provides users with access to generative AI features. This course delves into the capabilities of Gemini in Google Meet. Through video lessons, hands-on activities and practical examples, you will gain a comprehensive understanding of the Gemini features in Google Meet. You learn how to use Gemini to generate background images, improve your video quality, and translate captions. By the end of this course, you'll be equipped with the knowledge and skills to confidently utilize Gemini in Google Meet to maximize the effectiveness of your video conferences.
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Gemini for Google Workspace provides users 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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Gemini for Google Workspace provides users 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 Slides.
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Gemini for Google Workspace provides users with access to generative AI features. This course delves into the capabilities of Gemini in Google Docs using video lessons, hands-on activities and practical examples. You learn how to use Gemini to generate written content based on prompts. You also explore using Gemini to edit text you’ve already written, helping you improve your overall productivity. By the end of this course, you'll be equipped with the knowledge and skills to confidently utilize Gemini in Google Docs to improve your writing.
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Gemini for Google Workspace provides users 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 Gmail.
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Gemini for Google Workspace provides users with generative AI features in Google Workspace. In this learning path, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Workspace.
Generative AI Explorer - Vertex AI
Earned Dec 10, 2024 EST
Build Generative AI Apps with Firebase Genkit
Earned Dec 5, 2024 EST
Explore Generative AI with the Vertex AI Gemini API
Earned Nov 28, 2024 EST
Develop Serverless Apps with Firebase
Earned Nov 21, 2024 EST
Gemini for Application Developers
Earned Oct 31, 2024 EDT
Derive Insights from BigQuery Data
Earned Oct 30, 2024 EDT
Create a Streaming Data Lake on Cloud Storage
Earned Oct 30, 2024 EDT
Professional Machine Learning Engineer Study Guide
Earned Oct 28, 2024 EDT
Boost Productivity with Gemini in BigQuery
Earned Oct 25, 2024 EDT
Using BigQuery Machine Learning for inference
Earned Oct 25, 2024 EDT
Work with Gemini Models in BigQuery
Earned Oct 24, 2024 EDT
Gemini for Data Scientists and Analysts
Earned Oct 23, 2024 EDT
Analyze Sentiment with Natural Language API
Earned Oct 22, 2024 EDT
Create Generative AI Apps on Google Cloud
Earned Oct 17, 2024 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned Oct 17, 2024 EDT
Feature Engineering
Earned Oct 10, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
Earned Oct 9, 2024 EDT
BigQuery for Data Analysts
Earned Oct 8, 2024 EDT
Create ML Models with BigQuery ML
Earned Oct 5, 2024 EDT
BigQuery for Machine Learning
Earned Oct 2, 2024 EDT
Creating New BigQuery Datasets and Visualizing Insights
Earned Sep 30, 2024 EDT
Introduction to Data Analytics on Google Cloud
Earned Sep 25, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned Sep 19, 2024 EDT
Launching into Machine Learning
Earned Sep 19, 2024 EDT
Steering Bot with Conversational Agents
Earned Sep 6, 2024 EDT
Conversational AI on Vertex AI and Dialogflow CX
Earned Aug 13, 2024 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned Jun 13, 2024 EDT
Vector Search and Embeddings
Earned Jun 9, 2024 EDT
Introduction to Vertex AI Studio
Earned Jun 7, 2024 EDT
Create Image Captioning Models
Earned Jun 3, 2024 EDT
Transformer Models and BERT Model
Earned Jun 3, 2024 EDT
Encoder-Decoder Architecture
Earned Jun 1, 2024 EDT
Attention Mechanism
Earned Jun 1, 2024 EDT
Introduction to Image Generation
Earned Jun 1, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned May 31, 2024 EDT
Prompt Design in Vertex AI
Earned May 30, 2024 EDT
Introduction to Responsible AI
Earned May 28, 2024 EDT
Introduction to Large Language Models
Earned May 28, 2024 EDT
Introduction to Generative AI
Earned May 28, 2024 EDT
Gemini for end-to-end SDLC
Earned May 21, 2024 EDT
Gemini in Google Meet
Earned May 21, 2024 EDT
Gemini in Google Sheets
Earned May 21, 2024 EDT
Gemini in Google Slides
Earned May 21, 2024 EDT
Gemini in Google Docs
Earned May 21, 2024 EDT
Gemini in Gmail
Earned May 21, 2024 EDT
Introduction to Gemini for Google Workspace
Earned May 21, 2024 EDT