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Google Cloud Training
本課程介紹 Google Cloud 中的人工智慧 (AI) 和機器學習 (ML) 服務。這些服務透過 AI 基礎、開發和解決方案，支援「從資料到 AI」的生命週期。本課程討論的技術、產品和工具，可根據數據資料學家、AI 開發人員和機器學習工程師等不同使用者的目標，用於建構機器學習模型、機器學習管道和生成式 AI 專案。
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What's inside
Syllabus
簡介
本單元的內容為課程目標之一，會協助學員瞭解各項 Google Cloud AI 開發工具，並從中挑選出最合適的選項。本課程的架構是以 Google Cloud 的三層式 AI 架構為基礎，單元中也會簡要介紹課程架構。
AI 基礎
本單元著重介紹 AI 基礎，包括運算和儲存空間等雲端基礎架構，並說明 Google Cloud 中主要的資料和 AI 開發產品，最後示範如何運用 BigQuery ML 建構機器學習模型，協助您從資料轉移至 AI。
Read more
Syllabus
簡介
本單元的內容為課程目標之一，會協助學員瞭解各項 Google Cloud AI 開發工具，並從中挑選出最合適的選項。本課程的架構是以 Google Cloud 的三層式 AI 架構為基礎，單元中也會簡要介紹課程架構。
AI 基礎
本單元著重介紹 AI 基礎，包括運算和儲存空間等雲端基礎架構，並說明 Google Cloud 中主要的資料和 AI 開發產品，最後示範如何運用 BigQuery ML 建構機器學習模型，協助您從資料轉移至 AI。
Read more
AI 開發選項
本單元介紹在 Google Cloud 中開發機器學習專案的多種選項，包括現成解決方案 (如預先訓練的 API)、無需程式碼或僅需少量程式碼的解決方案 (如 AutoML)，以及以程式碼為基礎的解決方案 (例如自訂訓練)。本單元也比較這些選項的優缺點，協助您選擇最合適的開發工具。
AI 開發工作流程
本單元逐步介紹機器學習的工作流程，內容涵蓋資料準備、模型開發以及透過 Vertex AI 提供模型。本單元也說明如何運用 Vertex AI Pipelines 將工作流程轉換為自動化管線。
生成式 AI
本單元介紹生成式 AI、AI 最新進展，以及背後的技術：大型語言模型 (LLM)，並說明 Google Cloud 中的各種生成式 AI 開發工具 (例如 Generative AI Studio 和 Model Garden)，最後探討 AI 解決方案和嵌入式的生成式 AI 功能。
摘要
本單元介紹最重要的概念、工具、技術和產品，概略說明整個課程內容。
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores Google Cloud's AI and ML services, which are widely used in the industry
Taught by Google Cloud Training, renowned for their expertise in cloud computing
Provides a comprehensive foundation in AI and ML for data scientists, AI developers, and ML engineers
Offers multiple development options, including pre-built solutions, no-code/low-code solutions, and code-based solutions
Covers advanced topics such as generative AI, which is at the forefront of AI innovation
Requres learners to have prior knowledge in data science or programming
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Introduction to AI and Machine Learning on GC - 繁體中文 with these
activities:
Read: Dive into Deep Learning
Show steps
Gain a deeper understanding of deep learning principles and applications.
View
Dive into Deep Learning
on Amazon
Show steps
-
Read Chapter 1: Introduction to Deep Learning
-
Summarize key concepts and applications of deep learning
-
Identify challenges and opportunities in the field
Join the Google Cloud AI Community
Show steps
Connect with peers and experts, contribute to discussions, and share knowledge within the Google Cloud AI community.
Show steps
-
Join the Google Cloud AI Community forum
-
Attend virtual meetups or webinars
-
Participate in hackathons and challenges
-
Contribute to open source projects or documentation
Follow TensorFlow Tutorials on Image Classification
Show steps
Develop practical skills in image classification using TensorFlow.
Browse courses on
Image Classification
Show steps
-
Complete TensorFlow tutorial on image classification basics
-
Build a simple image classification model using TensorFlow
-
Interpret results and explore model accuracy
Five other activities
Expand to see all activities and additional details
Show all eight activities
Compile a Glossary of Cloud AI Terms
Show steps
Build a comprehensive understanding of key concepts and terminologies used in Cloud AI.
Browse courses on
Cloud AI
Show steps
-
Review course materials and identify key terms
-
Define and explain each term in clear and concise language
-
Organize terms into categories or alphabetical order
-
Share your glossary with others for reference
Create a Machine Learning Model Using AutoML
Show steps
Gain hands-on experience in building and evaluating machine learning models with minimal coding.
Browse courses on
AutoML
Show steps
-
Choose a dataset and define the machine learning task
-
Select and configure the AutoML model type
-
Train and evaluate the AutoML model
-
Analyze model performance and make predictions
Solve Data Science Problems on Kaggle
Show steps
Challenge yourself and refine your problem-solving skills by tackling data science problems on Kaggle.
Browse courses on
Data Science
Show steps
-
Choose a Kaggle competition or dataset
-
Explore the data and understand the problem statement
-
Develop and implement data science solutions
-
Evaluate and refine your models based on competition metrics
Build a Machine Learning Pipeline with Vertex AI
Show steps
Gain practical experience in designing and deploying end-to-end machine learning pipelines using Vertex AI.
Browse courses on
Machine Learning
Show steps
-
Define the machine learning problem and data requirements
-
Design the machine learning pipeline architecture
-
Train and evaluate machine learning models within the pipeline
-
Deploy the pipeline to Google Cloud and monitor its performance
-
Iterate and refine the pipeline based on results
Contribute to Google Cloud AI Open Source Projects
Show steps
Gain hands-on experience in open source development and contribute to the advancement of Cloud AI technologies.
Show steps
-
Explore Google Cloud AI open source repositories
-
Identify a project or issue to contribute to
-
Fork the repository and create a pull request
-
Collaborate with maintainers to refine and merge your contributions
-
Document your contributions and share your learnings with the community
Read: Dive into Deep Learning
Show steps
Gain a deeper understanding of deep learning principles and applications.
View
Dive into Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1: Introduction to Deep Learning
- Summarize key concepts and applications of deep learning
- Identify challenges and opportunities in the field
Join the Google Cloud AI Community
Show steps
Connect with peers and experts, contribute to discussions, and share knowledge within the Google Cloud AI community.
Show steps
Show steps
Show steps
- Join the Google Cloud AI Community forum
- Attend virtual meetups or webinars
- Participate in hackathons and challenges
- Contribute to open source projects or documentation
Follow TensorFlow Tutorials on Image Classification
Show steps
Develop practical skills in image classification using TensorFlow.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Complete TensorFlow tutorial on image classification basics
- Build a simple image classification model using TensorFlow
- Interpret results and explore model accuracy
Five other activities
Expand to see all activities and additional details
Show all eight activities
Compile a Glossary of Cloud AI Terms
Show steps
Build a comprehensive understanding of key concepts and terminologies used in Cloud AI.
Browse courses on
Cloud AI
Show steps
Show steps
Show steps
- Review course materials and identify key terms
- Define and explain each term in clear and concise language
- Organize terms into categories or alphabetical order
- Share your glossary with others for reference
Create a Machine Learning Model Using AutoML
Show steps
Gain hands-on experience in building and evaluating machine learning models with minimal coding.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Choose a dataset and define the machine learning task
- Select and configure the AutoML model type
- Train and evaluate the AutoML model
- Analyze model performance and make predictions
Solve Data Science Problems on Kaggle
Show steps
Challenge yourself and refine your problem-solving skills by tackling data science problems on Kaggle.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Choose a Kaggle competition or dataset
- Explore the data and understand the problem statement
- Develop and implement data science solutions
- Evaluate and refine your models based on competition metrics
Build a Machine Learning Pipeline with Vertex AI
Show steps
Gain practical experience in designing and deploying end-to-end machine learning pipelines using Vertex AI.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define the machine learning problem and data requirements
- Design the machine learning pipeline architecture
- Train and evaluate machine learning models within the pipeline
- Deploy the pipeline to Google Cloud and monitor its performance
- Iterate and refine the pipeline based on results
Contribute to Google Cloud AI Open Source Projects
Show steps
Gain hands-on experience in open source development and contribute to the advancement of Cloud AI technologies.
Show steps
Show steps
Show steps
- Explore Google Cloud AI open source repositories
- Identify a project or issue to contribute to
- Fork the repository and create a pull request
- Collaborate with maintainers to refine and merge your contributions
- Document your contributions and share your learnings with the community
Career center
Learners who complete Introduction to AI and Machine Learning on GC - 繁體中文 will develop knowledge and skills
that may be useful to these careers:
AI Researcher
AI Researcher
AI Researchers explore the frontiers of AI, which often involves the development of new algorithms and techniques. This course introduces Google Cloud services and tools that help developers bring AI solutions to life, including AutoML. AutoML allows you to build machine learning models with little to no coding. This course may help you break into the field of AI Research.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and maintain machine learning models, which may involve selecting the right tools for the job. This course can help you prepare for such a career by introducing the AI development tools available on Google Cloud. You'll learn how to compare the pros and cons of different options so that you can make informed decisions about which tools to use.
AI Developer
AI Developer
AI Developers design, develop, and implement AI applications, and this requires an understanding of AI principles and techniques. This course introduces Google Cloud services that support the data-to-AI lifecycle, including AI primitives, development, and solutions. Course topics cover the basics of artificial intelligence, the workflow for AI development, and the particulars of generative AI. This course may help you build a foundation for a career as an AI Developer.
AI Engineer
AI Engineer
AI Engineers design, develop, and deploy AI technologies within business applications. This course may help you break into this field by introducing the basics of artificial intelligence as well as the particulars of generative AI. Course topics cover the workflow for AI development using Google Cloud, including data preparation, model development, and deployment.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in various industries, including AI-powered solutions. This course introduces Google Cloud services and tools that help developers bring AI solutions to life, including Vertex AI Pipelines. Vertex AI Pipelines allows you to automate the machine learning workflow. This course may help you prepare for a career as an Operations Research Analyst.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines, and this may involve the use of artificial intelligence. This course introduces Google Cloud services that support the data-to-AI lifecycle, including AI primitives, development, and solutions. Course topics cover the basics of artificial intelligence, the workflow for AI development, and the particulars of generative AI. This course may help you build a foundation for a career as a Data Engineer.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data analysis to provide insights and recommendations to businesses, and this may involve the use of artificial intelligence. This course introduces Google Cloud services that support the data-to-AI lifecycle, including AI primitives, development, and solutions. Course topics cover the basics of artificial intelligence, the workflow for AI development, and the particulars of generative AI. This course may help you build a foundation for a career as a Business Intelligence Analyst.
Data Analyst
Data Analyst
Data Analysts uncover insights from data through analysis, and this may involve the use of machine learning. This course introduces Google Cloud services that support the data-to-AI lifecycle, including AI primitives, development, and solutions. Course topics cover the basics of artificial intelligence, the workflow for AI development, and the particulars of generative AI. This course may help you build a foundation for a career as a Data Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical modeling to analyze data and make predictions in the financial industry. This course introduces Google Cloud services that support the data-to-AI lifecycle, including AI primitives, development, and solutions. Course topics cover the basics of artificial intelligence, the workflow for AI development, and the particulars of generative AI. This course may help you prepare for a career as a Quantitative Analyst.
Data Scientist
Data Scientist
A Data Scientist is a professional who uses scientific methods to analyze data and find patterns and insights, and this requires an understanding of the capabilities and limitations of machine learning. This course introduces Google Cloud services that support the data-to-AI lifecycle, including AI primitives, development, and solutions. Course topics cover the basics of artificial intelligence, the workflow for AI development, and the particulars of generative AI. This course may help you build a foundation for a career as a Data Scientist.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases, and this may involve the use of artificial intelligence. This course introduces Google Cloud services that support the data-to-AI lifecycle, including AI primitives, development, and solutions. Course topics cover the basics of artificial intelligence, the workflow for AI development, and the particulars of generative AI. This course may help you build a foundation for a career as a Database Administrator.
Cybersecurity Analyst
Cybersecurity Analyst
Cybersecurity Analysts protect computer systems from unauthorized access and attacks, and this may involve the use of artificial intelligence. This course introduces Google Cloud services that support the data-to-AI lifecycle, including AI primitives, development, and solutions. Course topics cover the basics of artificial intelligence, the workflow for AI development, and the particulars of generative AI. This course may help you build a foundation for a career as a Cybersecurity Analyst.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems, which may involve the use of artificial intelligence. This course introduces Google Cloud services that support the data-to-AI lifecycle, including AI primitives, development, and solutions. Course topics cover the basics of artificial intelligence, the workflow for AI development, and the particulars of generative AI. This course may help you build a foundation for a career as a Software Engineer.
IT Manager
IT Manager
IT Managers plan, implement, and manage IT systems and services, and this may involve the use of artificial intelligence. This course introduces Google Cloud services that support the data-to-AI lifecycle, including AI primitives, development, and solutions. Course topics cover the basics of artificial intelligence, the workflow for AI development, and the particulars of generative AI. This course may help you build a foundation for a career as an IT Manager.
Product Manager
Product Manager
Product Managers develop and manage products, and this may involve the use of artificial intelligence. This course introduces Google Cloud services that support the data-to-AI lifecycle, including AI primitives, development, and solutions. Course topics cover the basics of artificial intelligence, the workflow for AI development, and the particulars of generative AI. This course may help you build a foundation for a career as a Product Manager.
For more career information including salaries, visit:
OpenCourser.com/course/twj1xa/introduction
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to AI and Machine Learning on GC - 繁體中文.
Save
這本書是深度學習的權威參考書，涵蓋了從基礎概念到最新進展的廣泛主題。對於想要深入了解深度學習的人來說，這是一本必備的資源。
這本書是人工智慧的經典教科書。它涵蓋人工智慧的各種方面，包括機器學習、自然語言處理和電腦視覺。它對於想要進一步瞭解人工智慧的讀者來說，一本寶貴的資源。
這本書提供了強化學習的全面介紹，涵蓋了從基礎概念到最新進展的廣泛主題。對於想要深入了解強化學習的人來說，這是一本絕佳的資源。
Practical guide to machine learning with Python, and it covers a wide range of topics, including data preprocessing, model training, and model evaluation. It valuable resource for anyone who wants to learn more about machine learning with Python, regardless of their level of experience.
Save
這本書提供了機器學習數學基礎的全面指南。它涵蓋線性代數、機率論和最佳化等主題。它對於想要深入了解機器學習數學基礎的讀者來說，是一本寶貴的資源。
這本書提供了一系列實作範例，教導讀者如何使用 Scikit-Learn、Keras 和 TensorFlow 等機器學習工具庫來建構和部署機器學習模型。對於想要獲得機器學習實作經驗的人來說，這是一本有用的資源。
Save
本書收錄了人工智慧和機器學習領域的最新研究論文，有助於讀者了解這些領域的前沿進展。
本書提供了人工智慧和機器學習技術的深入探討，包括技術規格、架構和最佳實務。
Save
Explores the potential future of humanity in the age of artificial intelligence.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/twj1xa/introduction
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Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
中文
Transcript
English
Good to know
Explores Google Cloud's AI and ML services, which are widely used in the industry
Taught by Google Cloud Training, renowned for their expertise in cloud computing
Provides a comprehensive foundation in AI and ML for data scientists, AI developers, and ML engineers
Offers multiple development options, including pre-built solutions, no-code/low-code solutions, and code-based solutions
Covers advanced topics such as generative AI, which is at the forefront of AI innovation
Requres learners to have prior knowledge in data science or programming
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