Coursera
  • 온라인 학위학사 및 석사 학위 살펴보기
  • MasterTrack™석사 학위를 따기 위한 학점 얻기
  • 대학교 수료증대학원 수준의 학습을 통해 경력을 쌓으세요.
경력 찾기기업용 Coursera대학교용
  • 검색
  • 상위 강좌
  • 로그인
  • 무료 회원 가입
    Coursera
    • 검색
    • Artificial Intelligence

    필터링 기준

    "artificial intelligence"에 대한 1704개의 결과

    • Placeholder
      Placeholder
      DeepLearning.AI, Stanford University

      Machine Learning

      획득할 기술: Machine Learning, Probability & Statistics, Machine Learning Algorithms, General Statistics, Theoretical Computer Science, Algorithms, Applied Machine Learning, Artificial Neural Networks, Regression, Econometrics, Computer Programming, Deep Learning, Python Programming, Statistical Programming, Mathematics, Tensorflow, Data Management, Data Structures, Statistical Machine Learning, Reinforcement Learning, Probability Distribution, Mathematical Theory & Analysis, Data Analysis, Data Mining, Linear Algebra, Computer Vision, Calculus, Feature Engineering, Bayesian Statistics, Operations Research, Research and Design, Strategy and Operations, Computational Logic, Accounting, Communication

      4.9

      (7.8k개의 검토)

      Beginner · Specialization · 1-3 Months

    • Placeholder
      DeepLearning.AI

      AI For Everyone

      획득할 기술: Machine Learning, Business Analysis, Applied Machine Learning, Business Transformation, Data Analysis, Data Model, Deep Learning, Exploratory Data Analysis, Forecasting, Human Computer Interaction, Natural Language Processing, People Analysis, Probability & Statistics, Reinforcement Learning, Statistical Analysis, Artificial Neural Networks, Machine Learning Algorithms

      4.8

      (37.6k개의 검토)

      Beginner · Course · 1-4 Weeks

    • Placeholder
      IBM Skills Network

      IBM Applied AI

      획득할 기술: Machine Learning, Cloud Computing, IBM Cloud, Python Programming, Data Analysis, Computer Vision, Computer Programming, Computer Science, Data Structures, Programming Principles, Algebra, Applied Machine Learning, Computational Thinking, Data Science, Statistical Programming, Computer Graphics, Theoretical Computer Science, Computer Graphic Techniques, Deep Learning, Natural Language Processing, Algorithms, Artificial Neural Networks, Machine Learning Software, Basic Descriptive Statistics, Computational Logic, Design and Product, Exploratory Data Analysis, Human Computer Interaction, Interactive Design, Other Web Frameworks, Product Design, Web Development, Application Development, Cloud API, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Operating Systems, Software Engineering, Software Engineering Tools, Software Testing, Systems Design, Web Development Tools

      4.6

      (41.9k개의 검토)

      Beginner · Professional Certificate · 3-6 Months

    • Placeholder
      University of Pennsylvania

      AI For Business

      획득할 기술: Machine Learning, Entrepreneurship, Leadership and Management, Strategy and Operations, Machine Learning Algorithms, Finance, Data Management, Theoretical Computer Science, Applied Machine Learning, Business Analysis, Human Resources, Marketing, Artificial Neural Networks, Deep Learning, Computational Thinking, Computer Programming, Data Analysis, Accounting, Algorithms, People Management, Regulations and Compliance, Sales, Big Data, Data Mining, Data Warehousing, Feature Engineering, Natural Language Processing, Reinforcement Learning, Audit, BlockChain, Business Transformation, Clinical Data Management, Customer Analysis, Customer Relationship Management, Customer Success, Database Administration, Databases, Decision Making, Financial Analysis, Innovation, Research and Design, Security Engineering, Software Security, Strategy

      4.6

      (121개의 검토)

      Beginner · Specialization · 3-6 Months

    • Placeholder
      IBM Skills Network

      AI Foundations for Everyone

      획득할 기술: Computer Science, Machine Learning, Applied Machine Learning, Data Science, Computational Thinking, Cloud Computing, IBM Cloud, Natural Language Processing, Computer Programming, Theoretical Computer Science, Deep Learning, Machine Learning Software, Computational Logic, Design and Product, Human Computer Interaction, Interactive Design, Product Design, Computer Vision, Machine Learning Algorithms, Software Engineering, Software Engineering Tools, Web Development, Web Development Tools

      4.7

      (12.5k개의 검토)

      Beginner · Specialization · 3-6 Months

    • Placeholder
      IBM Skills Network

      Introduction to Artificial Intelligence (AI)

      획득할 기술: Applied Machine Learning, Data Science, Computer Vision, Deep Learning, Machine Learning, Machine Learning Algorithms

      4.7

      (10.3k개의 검토)

      Beginner · Course · 1-4 Weeks

    • Placeholder
      Placeholder
      DeepLearning.AI

      Deep Learning

      획득할 기술: Deep Learning, Machine Learning, Artificial Neural Networks, Python Programming, Statistical Programming, Machine Learning Algorithms, Linear Algebra, Applied Machine Learning, Statistical Machine Learning, Dimensionality Reduction, Feature Engineering, Probability & Statistics, Business Psychology, Entrepreneurship, Machine Learning Software, Computer Vision, Marketing, General Statistics, Natural Language Processing, Computer Programming, Leadership and Management, Project Management, Regression, Sales, Strategy, Strategy and Operations, Tensorflow, Differential Equations, Mathematics, Applied Mathematics, Decision Making, Supply Chain Systems, Supply Chain and Logistics, Advertising, Communication, Estimation, Forecasting, Mathematical Theory & Analysis, Statistical Visualization, Algorithms, Theoretical Computer Science, Bayesian Statistics, Calculus, Probability Distribution, Statistical Tests, Big Data, Computer Architecture, Computer Networking, Data Management, Human Computer Interaction, Network Architecture, User Experience, Algebra, Computational Logic, Computer Graphic Techniques, Computer Graphics, Data Structures, Data Visualization, Hardware Design, Interactive Design, Markov Model, Network Model

      4.8

      (137.7k개의 검토)

      Intermediate · Specialization · 3-6 Months

    • Placeholder
      Placeholder
      IBM Skills Network

      IBM AI Engineering

      획득할 기술: Machine Learning, Computer Programming, Python Programming, Computer Vision, Deep Learning, Statistical Programming, Artificial Neural Networks, Machine Learning Algorithms, Probability & Statistics, General Statistics, Regression, Applied Machine Learning, Apache, Data Management, Data Mining, Data Analysis, Statistical Analysis, Big Data, Algorithms, Theoretical Computer Science, Statistical Machine Learning, Computer Graphics, Dimensionality Reduction, Tensorflow, Computer Graphic Techniques, Basic Descriptive Statistics, Business Analysis, Correlation And Dependence, Databases, Mathematics, NoSQL, SQL, Estimation, Econometrics, Entrepreneurship, Machine Learning Software, Probability Distribution, Data Science, Data Structures, IBM Cloud, Supply Chain Systems, Supply Chain and Logistics

      4.6

      (15.8k개의 검토)

      Intermediate · Professional Certificate · 3-6 Months

    • Placeholder
      Placeholder
      Placeholder
      University of Washington, Microsoft

      Autonomous AI for Industry

      획득할 기술: Entrepreneurship, Leadership and Management, Computer Networking, Decision Making, Deep Learning, Machine Learning, Marketing, Network Model, Reinforcement Learning, Sales, Strategy, Strategy and Operations, Business Psychology, Innovation

      4.5

      (24개의 검토)

      Beginner · Specialization · 3-6 Months

    • Placeholder
      Placeholder
      Google

      Google Data Analytics

      획득할 기술: Data Analysis, Statistical Programming, Data Science, Business Analysis, SQL, Spreadsheet Software, Data Visualization, Business, Data Management, Data Visualization Software, R Programming, Leadership and Management, Exploratory Data Analysis, Statistical Visualization, Change Management, Strategy and Operations, Communication, Statistical Analysis, Data Analysis Software, Business Communication, Data Structures, Tableau Software, Big Data, Cloud Computing, Critical Thinking, Customer Analysis, General Statistics, Plot (Graphics), Probability & Statistics, Small Data, Algorithms, Application Development, Budget Management, Computational Logic, Computer Architecture, Computer Networking, Computer Programming, Computer Programming Tools, Cryptography, Data Mining, Data Model, Database Administration, Database Design, Databases, Decision Making, Design and Product, Distributed Computing Architecture, Entrepreneurship, Extract, Transform, Load, Feature Engineering, Finance, Financial Analysis, Full-Stack Web Development, Interactive Data Visualization, Machine Learning, Mathematical Theory & Analysis, Mathematics, Network Security, Other Programming Languages, Problem Solving, Product Design, Programming Principles, Project Management, Research and Design, Security Engineering, Security Strategy, Software Engineering, Software Security, Storytelling, Theoretical Computer Science, Visual Design, Visualization (Computer Graphics), Web Development

      4.8

      (100.7k개의 검토)

      Beginner · Professional Certificate · 3-6 Months

    • Placeholder
      Placeholder
      Parsons School of Design, The New School

      Creativity and AI

      획득할 기술: Entrepreneurship, Research and Design, Creativity, Machine Learning, Artificial Neural Networks, Innovation, Market Research

      4.5

      (38개의 검토)

      Intermediate · Specialization · 3-6 Months

    • Placeholder
      Placeholder
      University of Chicago

      Machine Learning for Analytics MasterTrack™ Certificate

      획득할 기술: Probability & Statistics, Machine Learning, General Statistics, Business Analysis, Data Analysis, Statistical Analysis, Experiment, Probability Distribution, Python Programming, Applied Machine Learning, Regression, Statistical Tests, Advertising, Algebra, Communication, Data Management, Data Structures, Linear Algebra, Machine Learning Algorithms, Marketing, Theoretical Computer Science

      학점 제공

      Mastertrack · 6-12 Months

    artificial intelligence과(와) 관련된 검색

    artificial intelligence in healthcare
    artificial intelligence in marketing
    artificial intelligence: an overview
    artificial intelligence (ai) education for teachers
    artificial intelligence and legal issues
    artificial intelligence: ethics & societal challenges
    artificial intelligence on microsoft azure
    artificial intelligence for breast cancer detection
    1234…84

    요약하자면, 여기에 가장 인기 있는 artificial intelligence 강좌 10개가 있습니다.

    • Machine Learning: DeepLearning.AI
    • AI For Everyone: DeepLearning.AI
    • IBM Applied AI: IBM Skills Network
    • AI For Business: University of Pennsylvania
    • AI Foundations for Everyone: IBM Skills Network
    • Introduction to Artificial Intelligence (AI): IBM Skills Network
    • Deep Learning: DeepLearning.AI
    • IBM AI Engineering: IBM Skills Network
    • Autonomous AI for Industry: University of Washington
    • Google Data Analytics: Google

    인공 지능에 대한 자주 묻는 질문

    • Artificial intelligence (AI) is a fast-growing branch of computer science focused on enabling computers to perform a wide range of tasks that previously required human intelligence. Today, AI is used to power a wide range of tasks, such as image recognition, language translation, and prioritization of email or business workflows. So, if you have a smartphone, chances are you use software with AI capabilities every day.

      AI is often discussed in tandem with the closely related concept of machine learning. Machine learning is the use of step-by-step processes called algorithms to allow computers to solve problems on their own - and, over time, get steadily better at doing so. Well-designed machine learning algorithms give computers the ability to solve a wide range of problems much more effectively and flexibly than if programmers had to provide detailed instructions for one specific use case.

      While machine learning is used to create many simple AI applications, this approach typically requires massive, clearly-defined datasets to properly “train” the program. To create more sophisticated AI applications, an advanced type of machine learning called deep learning is used. Deep learning uses artificial neural networks that, as its name implies, are patterned after the human brain and do not require such structured datasets and human guidance to be successful. Instead, the AI application can be fed diverse, unstructured datasets and learn itself how to achieve a specified goal.

      Even today’s most powerful deep learning approaches are not capable of mimicking the complexity and creativity of the human brain and its tens of billions of neurons. However, the field of artificial intelligence has made incredible strides in recent years, and is changing the way we live and work in ways that would have seemed outlandish a decade ago. Who knows what the next decade of progress in this exciting field will yield? Students learning skills in this area today may end up producing even more radical breakthroughs.‎

    • As artificial intelligence (AI) touches more and more areas of our daily lives, it is becoming useful to more and more career paths. Indeed, at least some background in this field is required for a growing number of jobs, and it can help give you a significant advantage over the competition in many others.

      Naturally, AI and its subfields are in very high demand for popular computer science jobs. Data scientists rely on machine learning and deep learning skills in their daily work, applying various data mining techniques to both structured and unstructured big data in order to produce valuable insights for a wide variety of businesses. Skills in natural language processing (NLP) are needed to create useful chatbots for customer service as well as voice-activated assistants like Amazon’s Alexa. And advanced AI skills can put you on the cutting edge of computer programming, working on teams seeking to achieve ambitious goals like self-driving cars or autonomous robots.

      A background in AI can help you in more and more jobs outside the realm of computer science, as well - it’s not much of an exaggeration to say that if a job requires human intelligence to do, artificial intelligence can help.

      For example, a familiarity with machine learning can help business analysts understand and use sophisticated tools for predicting movements in the market - or develop these tools themselves. Doctors and other healthcare professionals are leveraging AI to assist with diagnosing illnesses, prescribing treatments, and analyzing medical data. Even creative professionals in visual arts and music can take advantage of AI tools to help them generate images and melodies.‎

    • Coursera offers online courses in an incredibly wide range of computer science topics, and artificial intelligence is no exception. If you’re a computer science student interested in this fast-growing field, online courses can give you an introduction to AI and machine learning, or help you hone your Python skills for data science. More advanced learners can dive deep, with courses and Specializations in AI engineering and deep learning. Even non-computer scientists can benefit from courses geared towards their field, such as the use of machine learning for trading and other business professionals.

      Whatever your level of expertise and area of interest, online courses let you learn remotely on a flexible schedule and, typically, a lower cost than on-campus alternatives. And thanks to Coursera’s partnerships with top-ranked schools like Stanford University and Imperial College London, as well as industry leaders like IBM and Google Cloud, students can get all the advantages of online learning while still getting a high-quality education in this exciting field.‎

    • The skills or experience you may need to have before learning artificial intelligence (AI) includes having a solid knowledge of math, science, and computer science, specifically data science. You may want to have experience with advanced math, such as calculus and algebra, Bayesian algorithms, plus probability and statistics. In addition, a science background may be good to have for learning AI, including an understanding of physics, mechanics, cognitive learning theory, and language processing. It will also help to have a good command of computer science, including programming languages and tools such as Python, C++, and Java. Understanding the basic foundations of machine learning, deep learning, and neural networks may also be helpful to you before learning AI. If you already have some experience in software development, automotive manufacturing, and aerospace manufacturing fields, you may already have some necessary understanding of the way AI is applied in these industries.‎

    • The kind of people who are best suited for roles in AI are interested in highly scientific concepts and tools. People well suited to work in roles in AI feel comfortable experimenting with advanced technologies and concepts, such as machine learning, a part of AI that has given the world self-driving cars, for example. They also feel energized working with sophisticated pieces of software that can make decisions by analyzing data. In addition, the type of people well suited to work in roles in AI may want to learn to have the ability to build sophisticated pieces of equipment, such as robotics, which operate on internal software.‎

    • Learning artificial intelligence may be right for you if you plan on becoming an AI developer, machine learning engineer, data scientist, or research engineer or if you want your company to become better at using AI. In addition, learning AI may be beneficial if you are in the medical field, which AI is transforming when it comes to diagnosing, treating, and predicting outcomes. Learning AI may benefit you if you want to understand what AI realistically can and can't do and if you want to be able to spot opportunities to apply AI to your organization’s problems and know how to navigate the ethics of machine learning, along with other dimensions of AI.‎

    이 FAQ 콘텐츠는 정보 전달 목적만으로 사용할 수 있습니다. 학습자는 과정 및 기타 학점 정보가 개인적, 직업적 및 재정적 목표에 부합하는지 확인하기 위해 추가 조사를 수행하는 것이 좋습니다.
    살펴볼 만한 다른 주제
    Placeholder
    예술 & 인문학
    338개의 강좌
    Placeholder
    비즈니스
    1095개의 강좌
    Placeholder
    컴퓨터 공학
    668개의 강좌
    Placeholder
    데이터 과학
    425개의 강좌
    Placeholder
    정보 기술
    145개의 강좌
    Placeholder
    건강
    471개의 강좌
    Placeholder
    수학 및 논리
    70개의 강좌
    Placeholder
    자기개발
    137개의 강좌
    Placeholder
    물리 과학 및 공학
    413개의 강좌
    Placeholder
    사회 과학
    401개의 강좌
    Placeholder
    언어 학습
    150개의 강좌

    Coursera Footer

    경력을 시작하거나 쌓기

    • Google 데이터 분석가
    • Google 디지털 마케팅 및 전자 상거래 전문 자격증
    • Python을 통한 Google IT 자동화 전문 자격증
    • Google IT 지원
    • Google 프로젝트 관리
    • Google UX 디자인
    • Google 클라우드 자격증: 클라우드 아키텍트 취득 준비
    • IBM 사이버 보안 분석가
    • IBM 데이터 분석가
    • IBM 데이터 엔지니어링
    • IBM 데이터 과학
    • IBM 풀스택 클라우드 개발자
    • IBM 기계 학습
    • Intuit 부기
    • Meta 프런트 엔드 개발자
    • DeepLearning.AI TensorFlow 개발자 전문 자격증
    • SAS 프로그래머 전문 자격증
    • 경력 시작
    • 수료증 취득 준비
    • 경력 쌓기
    • Python 구문 오류를 찾아내는 방법
    • Python 예외를 발견하는 방법
    • 모든 프로그래밍 자습서 보기

    인기 강좌 및 수료증

    • 무료 강좌
    • 인공 지능 강좌
    • 블록체인 강좌
    • 컴퓨터 공학 강좌
    • Cursos Gratis
    • 사이버 보안 강좌
    • 데이터 분석 강좌
    • 데이터 과학 강좌
    • 영어 말하기 강좌
    • 풀스택 웹 개발 강좌
    • Google 강좌
    • 인사 강좌
    • IT 강좌
    • 영어 학습 강좌
    • Microsoft Excel 강좌
    • 제품 관리 강좌
    • 프로젝트 관리 강좌
    • Python 강좌
    • SQL 강좌
    • 애자일 수료증
    • CAPM 수료증
    • CompTIA A+ 수료증
    • 데이터 분석 수료증
    • 스크럼 마스터 수료증
    • 모든 강좌 보기

    인기 컬렉션 및 문서

    • 하루만에 완료할 수 있는 무료 온라인 강좌
    • 인기 무료 강좌
    • 비즈니스 직무
    • 사이버 보안 직무
    • 신입 IT 직무
    • 데이터 분석가 면접 질문
    • 데이터 분석 프로젝트
    • 데이터 분석가로 취업하는 방법
    • 프로젝트 관리자로 취업하는 방법
    • IT 기술
    • 프로젝트 관리자 면접 질문
    • Python 프로그래밍 기술
    • 면접에서 물어보는 강점과 약점
    • 데이터 분석가가 하는 일
    • 소프트웨어 엔지니어가 하는 일
    • 데이터 엔지니어란
    • 데이터 과학자란
    • 제품 디자이너란
    • 스크럼 마스터란
    • UX 리서처란
    • PMP 수료증을 취득하는 방법
    • PMI 수료증
    • 인기 있는 사이버 보안 자격증
    • 인기 있는 SQL 자격증
    • 모든 Coursera 문서 읽기

    온라인으로 학위 또는 자격증 취득

    • Google 전문 자격증
    • 전문 자격증
    • 모든 자격증 보기
    • 학사 학위
    • 석사 학위
    • 컴퓨터 공학 학위
    • 데이터 과학 학위
    • MBA 및 경영학 학위
    • 데이터 분석 학위
    • 공중 보건 학위
    • 사회 과학 학위
    • 관리 학위
    • BA 및 BS 학위 비교
    • 학사 학위란 무엇인가요?
    • 개발해야 할 11가지 좋은 학습 습관
    • 추천서를 작성하는 방법
    • 비즈니스 학위 취득 후 취업 가능한 10개의 수요가 높은 직무
    • 컴퓨터 공학 석사 학위는 취득할 만한 가치가 있나요?
    • 모든 학위 프로그램 보기
    • Coursera 인도
    • Coursera 영국
    • Coursera 멕시코

    Coursera

    • 소개
    • 제공 내용
    • 리더십
    • 직업
    • 카탈로그
    • Coursera Plus
    • 전문 자격증
    • MasterTrack® 자격증
    • 학위
    • 기업용 Coursera
    • 정부용
    • 캠퍼스용
    • 파트너가 되기
    • 코로나바이러스감염증-19 대응

    커뮤니티

    • 학습자
    • 파트너
    • 베타 테스터
    • 번역가
    • 블로그
    • 기술 블로그
    • 지도 센터

    기타

    • 보도 자료
    • 투자자
    • 조건
    • 개인정보 보호
    • 도움말
    • 접근성
    • 문의하기
    • 문서
    • 디렉토리
    • 계열사
    • 현대 노예 선언문
    어디에서나 학습
    Placeholder
    Placeholder
    Placeholder
    © 2023 Coursera Inc. All rights reserved.
    • Placeholder
    • Placeholder
    • Placeholder
    • Placeholder
    • Placeholder