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이 과정에서는 예측 및 생성형 AI 프로젝트를 모두 빌드하는 Google Cloud 기반 AI 및 머신러닝(ML) 제품군을 소개합니다. AI 기반, 개발, 솔루션을 모두 포함하여 데이터에서 AI로 이어지는 수명 주기 전반에 걸쳐 사용할 수 있는 기술과 제품, 도구를 살펴봅니다. 이 과정의 목표는 흥미로운 학습 경험과 실제적인 실무형 실습을 통해 데이터 과학자, AI 개발자, ML 엔지니어의 기술 및 지식 역량 강화를 지원하는 것입니다.
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What's inside
Syllabus
소개
이 모듈에서 다루는 과정은 학습자가 Google Cloud의 AI 개발 도구를 살펴보도록 돕는 데 그 목적이 있습니다. 여기에서 제공되는 과정의 대략적인 구성은 AI 기반, 개발, 솔루션, 이 3가지 계층으로 이루어진 AI 프레임워크를 토대로 합니다.
Read more
Syllabus
소개
이 모듈에서 다루는 과정은 학습자가 Google Cloud의 AI 개발 도구를 살펴보도록 돕는 데 그 목적이 있습니다. 여기에서 제공되는 과정의 대략적인 구성은 AI 기반, 개발, 솔루션, 이 3가지 계층으로 이루어진 AI 프레임워크를 토대로 합니다.
Read more
AI 기반
이 모듈에서는 먼저, AI 기능을 보여주는 사용 사례를 알아봅니다. 그런 다음 컴퓨팅, 스토리지 등 클라우드 인프라를 포함한 AI 기반을 중점적으로 살펴봅니다. 또한 Google Cloud 기반의 주요 데이터 및 AI 개발 제품도 살펴봅니다. 마지막으로, 데이터를 AI로 전환해 주는 ML 모델을 빌드하기 위해 BigQuery ML을 사용하는 방법을 시연합니다.
AI 개발 옵션
이 모듈에서는 사전 학습된 API같이 즉시 사용 가능한 솔루션부터 AutoML 같은 노 코드 및 로우 코드 솔루션, 커스텀 학습 같은 코드 기반 솔루션에 이르기까지 Google Cloud에서 ML 프로젝트를 개발하는 다양한 옵션을 살펴봅니다. 적합한 개발 도구를 판단하는 데 참고할 수 있도록 각 옵션의 장점과 단점을 비교합니다.
AI 개발 워크플로
이 모듈에서는 데이터 준비, 모델 개발, Vertex AI 기반 모델 서빙에 이르는 ML 워크플로를 살펴봅니다. Vertex AI Pipelines를 사용하여 이 워크플로를 자동화된 파이프라인으로 변환하는 방법도 설명합니다.
생성형 AI
이 모듈에서는 최신 AI 기술인 생성형 AI와 생성형 AI 프로젝트를 개발하는 데 필요한 필수 툴킷을 소개합니다. 먼저 Google Cloud를 기반으로 생성형 AI 워크플로를 살펴보겠습니다. 그런 다음, Gen AI Studio와 Model Garden을 사용하여 Gemini 멀티모달에 액세스하고 프롬프트를 설계하고 모델을 조정하는 방법을 알아봅니다. 마지막으로 AI 솔루션에 내장된 생성형 AI 기능을 알아봅니다.
요약
이 모듈에서는 가장 중요한 개념, 도구, 기술, 제품을 살펴보면서 전체 과정을 요약합니다.
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Devised by Google Cloud Training, a leader in professional skill development for AI and machine learning
Structured with a clear hierarchy of AI fundamentals, development, and solutions, ensuring a systematic learning experience
Covers both predictive and generative AI, providing a comprehensive understanding of the full spectrum of AI applications
Offers hands-on practice with real-world datasets and tools, allowing learners to apply their knowledge immediately
Incorporates Google Cloud's latest AI products and services, ensuring that learners are up-to-date with industry advancements
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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 - 한국어 with these
activities:
Review Python Basics
Show steps
Revisit the basics of Python to solidify fundamental concepts and prepare for advanced topics covered in this course.
Browse courses on
Python
Show steps
-
Re-read Python documentation or tutorials on data types, operators, and control flow.
-
Solve coding challenges or practice exercises on platforms like LeetCode or HackerRank.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Gain a comprehensive understanding of machine learning concepts and techniques through a hands-on approach using popular libraries like Scikit-Learn, Keras, and TensorFlow.
Show steps
-
Read chapters 1-3, focusing on supervised learning algorithms.
-
Implement the algorithms discussed in the book using Python code.
Follow Tutorials on Natural Language Processing (NLP)
Show steps
Enhance your understanding of NLP techniques by following guided tutorials, which will provide step-by-step instructions and hands-on practice.
Browse courses on
Natural Language Processing
Show steps
-
Find tutorials on topics like text preprocessing, tokenization, and sentiment analysis.
-
Follow the tutorials, implementing the code and experimenting with different parameters.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join Study Groups or Online Forums
Show steps
Engage with peers, discuss concepts, and learn from others' experiences by participating in study groups or online forums dedicated to AI and machine learning.
Show steps
-
Find study groups or forums related to the course topics.
-
Participate in discussions, ask questions, and share your knowledge.
Solve Competitive Programming Problems
Show steps
Develop your problem-solving skills and deepen your understanding of algorithms and data structures by solving competitive programming problems.
Browse courses on
Competitive Programming
Show steps
-
Join online platforms like LeetCode or HackerRank.
-
Solve problems of varying difficulty levels.
Build a Machine Learning Model for Image Classification
Show steps
Apply your knowledge by building a machine learning model that can classify images, reinforcing your understanding of model development and evaluation.
Browse courses on
Machine Learning
Show steps
-
Gather and prepare a dataset of images.
-
Train a convolutional neural network (CNN) model using TensorFlow or Keras.
-
Evaluate the model's performance using metrics like accuracy and F1 score.
Create a Blog Post or Article on AI Trends
Show steps
Stay up-to-date with the latest advancements in AI by researching and writing about emerging trends, reinforcing your understanding of the field's evolution.
Browse courses on
AI Trends
Show steps
-
Research and gather information on recent AI developments.
-
Write a well-structured blog post or article summarizing your findings.
Review Python Basics
Show steps
Revisit the basics of Python to solidify fundamental concepts and prepare for advanced topics covered in this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Re-read Python documentation or tutorials on data types, operators, and control flow.
- Solve coding challenges or practice exercises on platforms like LeetCode or HackerRank.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Gain a comprehensive understanding of machine learning concepts and techniques through a hands-on approach using popular libraries like Scikit-Learn, Keras, and TensorFlow.
Show steps
Show steps
Show steps
- Read chapters 1-3, focusing on supervised learning algorithms.
- Implement the algorithms discussed in the book using Python code.
Follow Tutorials on Natural Language Processing (NLP)
Show steps
Enhance your understanding of NLP techniques by following guided tutorials, which will provide step-by-step instructions and hands-on practice.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Find tutorials on topics like text preprocessing, tokenization, and sentiment analysis.
- Follow the tutorials, implementing the code and experimenting with different parameters.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join Study Groups or Online Forums
Show steps
Engage with peers, discuss concepts, and learn from others' experiences by participating in study groups or online forums dedicated to AI and machine learning.
Show steps
Show steps
Show steps
- Find study groups or forums related to the course topics.
- Participate in discussions, ask questions, and share your knowledge.
Solve Competitive Programming Problems
Show steps
Develop your problem-solving skills and deepen your understanding of algorithms and data structures by solving competitive programming problems.
Browse courses on
Competitive Programming
Show steps
Show steps
Show steps
- Join online platforms like LeetCode or HackerRank.
- Solve problems of varying difficulty levels.
Build a Machine Learning Model for Image Classification
Show steps
Apply your knowledge by building a machine learning model that can classify images, reinforcing your understanding of model development and evaluation.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Gather and prepare a dataset of images.
- Train a convolutional neural network (CNN) model using TensorFlow or Keras.
- Evaluate the model's performance using metrics like accuracy and F1 score.
Create a Blog Post or Article on AI Trends
Show steps
Stay up-to-date with the latest advancements in AI by researching and writing about emerging trends, reinforcing your understanding of the field's evolution.
Browse courses on
AI Trends
Show steps
Show steps
Show steps
- Research and gather information on recent AI developments.
- Write a well-structured blog post or article summarizing your findings.
Career center
Learners who complete Introduction to AI and Machine Learning on GC - 한국어 will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/hw6trw/introduction
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Level
Introductory
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Google Cloud
Instructor
Google Cloud Training
Session
August 26, 2024
- October 14, 2024
Language
한국어
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Good to know
Devised by Google Cloud Training, a leader in professional skill development for AI and machine learning
Structured with a clear hierarchy of AI fundamentals, development, and solutions, ensuring a systematic learning experience
Covers both predictive and generative AI, providing a comprehensive understanding of the full spectrum of AI applications
Offers hands-on practice with real-world datasets and tools, allowing learners to apply their knowledge immediately
Incorporates Google Cloud's latest AI products and services, ensuring that learners are up-to-date with industry advancements
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