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Ilyas Ramatullaev

成为会员时间:2023

青铜联赛

14160 积分
大型语言模型简介 Earned Nov 8, 2024 EST
[DEPRECATED] Feature Engineering Earned Apr 4, 2024 EDT
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud Earned Mar 29, 2024 EDT
Analyzing and Visualizing Data in Looker Earned Jan 22, 2024 EST
Launching into Machine Learning Earned Jan 18, 2024 EST
Google Cloud 数据分析基础知识 Earned Jan 10, 2024 EST
Google Cloud 上的 AI 和机器学习简介 Earned Jan 8, 2024 EST
生成式 AI 简介 Earned Dec 19, 2023 EST

这是一节入门级微学习课程,探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能,还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。

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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 covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

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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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了解如何在 Google Cloud 上将原始数字转化为清晰、以数据为依据的决策。通过实操实验和实际应用场景,本新手级课程将引导您在 BigQuery 中查询数据、在 Looker 中探索趋势,以及在数据洞察中制作可共享的交互式报告。

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本课程介绍 Google Cloud 的 AI 和机器学习 (ML) 能力,重点讲解如何开发生成式和预测式 AI 项目。本课程将探讨“数据到 AI”全生命周期中的多种技术、产品和工具,并通过互动练习帮助数据科学家、AI 开发者和机器学习工程师提升专业能力。

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这是一节入门级微课程,旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。

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