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    • Artificial Intelligence
    Related topics:응용 기계 학습응용 통계학심층 학습HCI(Human computer interaction)ibm aiIBM

    필터링 기준

    "artificial intelligence"에 대한 1415개의 결과

    • DeepLearning.AI

      DeepLearning.AI

      AI For Everyone

      획득할 기술: Machine Learning Algorithms, Artificial Neural Networks, Ethics, Deep Learning, Machine Learning

      4.8

      (35.9k개의 검토)

      Beginner · Course

    • IBM

      IBM

      IBM Applied AI

      획득할 기술: Applied Machine Learning, Cloud API, Cloud Computing, Computational Logic, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Data Management, Deep Learning, Extract, Transform, Load, IBM Cloud, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Python Programming, Software Engineering, Software Engineering Tools, Statistical Programming, Theoretical Computer Science, Web Development, Web Development Tools

      4.6

      (37k개의 검토)

      Beginner · Professional Certificate

    • IBM

      IBM

      IBM AI Engineering

      획득할 기술: Algorithms, Apache, Applied Machine Learning, Artificial Neural Networks, Basic Descriptive Statistics, Big Data, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Data Analysis, Data Management, Data Structures, Databases, Deep Learning, Econometrics, General Statistics, Keras, Machine Learning, Machine Learning Algorithms, Mathematics, NoSQL, Opencv, Probability & Statistics, Probability Distribution, PyTorch, Python Programming, Regression, SQL, Statistical Machine Learning, Statistical Programming, Tensorflow, Theoretical Computer Science

      4.6

      (14.5k개의 검토)

      Intermediate · Professional Certificate

    • IBM

      IBM

      Introduction to Artificial Intelligence (AI)

      획득할 기술: Ethics, Machine Learning Algorithms, Deep Learning, Applied Machine Learning, Computer Vision, Machine Learning

      4.7

      (9.3k개의 검토)

      Beginner · Course

    • University of Pennsylvania

      University of Pennsylvania

      AI For Business

      획득할 기술: Applied Machine Learning, Big Data, Computational Thinking, Computer Programming, Customer Relationship Management, Customer Success, Data Management, Data Warehousing, Database Administration, Databases, Entrepreneurship, Finance, Human Resources, Leadership, Leadership and Management, Machine Learning, Marketing, People Management, Reinforcement Learning, Sales, Security Engineering, Software Security, Strategy and Operations, Theoretical Computer Science

      4.6

      (74개의 검토)

      Beginner · Specialization

    • IBM

      IBM

      AI Foundations for Everyone

      획득할 기술: Applied Machine Learning, Cloud Computing, Computer Vision, Deep Learning, IBM Cloud, Machine Learning, Machine Learning Algorithms, Software Engineering, Software Engineering Tools, Web Development, Web Development Tools

      4.7

      (11.2k개의 검토)

      Beginner · Specialization

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      DeepLearning.AI

      DeepLearning.AI

      Deep Learning

      획득할 기술: Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Big Data, Communication, Computational Logic, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Convolutional Neural Network, Data Management, Deep Learning, Entrepreneurship, General Statistics, Hardware Design, Human Computer Interaction, Interactive Design, Linear Algebra, Machine Learning, Machine Learning Algorithms, Markov Model, Mathematical Optimization, Mathematical Theory & Analysis, Mathematics, Natural Language Processing, Probability & Statistics, Python Programming, Regression, Statistical Machine Learning, Statistical Programming, Strategy and Operations, Theoretical Computer Science

      4.8

      (133.1k개의 검토)

      Intermediate · Specialization

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      Imperial College London

      Imperial College London

      Mathematics for Machine Learning

      획득할 기술: Algebra, Algorithms, Analysis, Basic Descriptive Statistics, Calculus, Computer Programming, Data Analysis, Deep Learning, Differential Equations, General Statistics, Linear Algebra, Linearity, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Probability Distribution, Python Programming, Software Engineering, Software Testing, Statistical Programming, Theoretical Computer Science

      4.6

      (12.7k개의 검토)

      Beginner · Specialization

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      Duke University

      Duke University

      AI Product Management

      획득할 기술: Artificial Neural Networks, Communication, Computer Networking, Computer Vision, Data Management, Data Structures, Database Administration, Databases, Deep Learning, Design and Product, Econometrics, Entrepreneurship, General Statistics, Journalism, Leadership, Leadership and Management, Machine Learning, Machine Learning Algorithms, Machine Learning Software, Natural Language Processing, Network Security, Operating Systems, Probability & Statistics, Product Management, Project, Project Management, Security Engineering, Software, Strategy and Operations, Systems Design, Theoretical Computer Science

      4.6

      (80개의 검토)

      Beginner · Specialization

    • Placeholder
      University of Washington

      University of Washington

      Machine Learning

      획득할 기술: Algorithms, Applied Machine Learning, Business Analysis, Business Psychology, Computational Logic, Computational Thinking, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Analysis, Data Management, Data Structures, Deep Learning, Distributed Computing Architecture, Entrepreneurship, Estimation, Exploratory Data Analysis, General Statistics, Machine Learning, Machine Learning Algorithms, Markov Model, Mathematical Theory & Analysis, Mathematics, Natural Language Processing, Probability & Statistics, Python Programming, Regression, Statistical Analysis, Statistical Classification, Statistical Machine Learning, Statistical Programming, Theoretical Computer Science

      4.6

      (15.6k개의 검토)

      Intermediate · Specialization

    • Placeholder
      IBM

      IBM

      IBM Data Science

      획득할 기술: Algebra, Algorithms, Analysis, Business Analysis, Cloud API, Cloud Computing, Communication, Computational Logic, Computer Programming, Computer Programming Tools, Correlation And Dependence, Data Analysis, Data Management, Data Mining, Data Structures, Data Visualization, Database Administration, Database Application, Databases, Econometrics, Exploratory Data Analysis, Extract, Transform, Load, General Statistics, Machine Learning, Machine Learning Algorithms, Marketing, Mathematical Theory & Analysis, Mathematics, Plot (Graphics), Probability & Statistics, Python Programming, R Programming, Regression, SPSS, SQL, Spreadsheet Software, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Theoretical Computer Science, Web

      4.6

      (93.4k개의 검토)

      Beginner · Professional Certificate

    • Placeholder
      IBM

      IBM

      IBM Data Analyst

      획득할 기술: Algebra, Analysis, Apache, Big Data, Business Analysis, Computational Logic, Computer Programming, Computer Programming Tools, Correlation And Dependence, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Structures, Data Visualization, Data Visualization Software, Data Warehousing, Database Administration, Database Application, Databases, Econometrics, Exploratory Data Analysis, Extract, Transform, Load, General Statistics, Machine Learning, Mathematical Theory & Analysis, Mathematics, Microsoft Excel, NoSQL, Operating Systems, Plot (Graphics), Probability & Statistics, Python Programming, Regression, SQL, Spreadsheet Software, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Statistical Visualization, System Programming, Theoretical Computer Science, Web

      4.6

      (50.3k개의 검토)

      Beginner · Professional Certificate

    artificial intelligence과(와) 관련된 검색

    artificial intelligence in healthcare
    artificial intelligence in marketing
    artificial intelligence (ai) education for teachers
    artificial intelligence: an overview
    artificial intelligence algorithms models and limitations
    artificial intelligence on microsoft azure
    artificial intelligence for breast cancer detection
    artificial intelligence and legal issues
    1234…84

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

    • AI For Everyone: DeepLearning.AI
    • IBM Applied AI: IBM
    • IBM AI Engineering: IBM
    • Introduction to Artificial Intelligence (AI): IBM
    • AI For Business: University of Pennsylvania
    • AI Foundations for Everyone: IBM
    • Deep Learning: DeepLearning.AI
    • Mathematics for Machine Learning: Imperial College London
    • AI Product Management: Duke University
    • Machine Learning: University of Washington

    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.
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