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    • Artificial Intelligence

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    「artificial intelligence」の1381件の結果

    • DeepLearning.AI

      DeepLearning.AI

      AI For Everyone

      習得できるスキル: Machine Learning, Ethics, Deep Learning, Artificial Neural Networks, Machine Learning Algorithms

      4.8

      (36k件のレビュー)

      Beginner · Course

    • IBM

      IBM

      IBM Applied AI

      習得できるスキル: Applied Machine Learning, Artificial Neural Networks, Cloud API, Cloud Computing, Computational Logic, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Data Management, Deep Learning, Ethics, Extract, Transform, Load, IBM Cloud, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Natural Language Processing, Python Programming, Software Engineering, Software Engineering Tools, Statistical Programming, Theoretical Computer Science, Web Development, Web Development Tools

      4.6

      (37.2k件のレビュー)

      Beginner · Professional Certificate

    • IBM

      IBM

      IBM AI Engineering

      習得できるスキル: Algorithms, Apache, Applied Machine Learning, Artificial Neural Networks, Basic Descriptive Statistics, Big Data, Business Analysis, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Correlation And Dependence, Data Analysis, Data Management, Data Structures, Databases, Decision Tree, Deep Learning, Dimensionality Reduction, Econometrics, General Statistics, Machine Learning, Machine Learning Algorithms, Mathematics, NoSQL, Probability & Statistics, Probability Distribution, Python Programming, Recommender Systems, Regression, SQL, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Supply Chain Systems, Supply Chain and Logistics, Theoretical Computer Science

      4.6

      (14.6k件のレビュー)

      Intermediate · Professional Certificate

    • IBM

      IBM

      Introduction to Artificial Intelligence (AI)

      習得できるスキル: Deep Learning, Machine Learning, Ethics, Machine Learning Algorithms, Applied Machine Learning, Computer Vision

      4.7

      (9.3k件のレビュー)

      Beginner · Course

    • University of Pennsylvania

      University of Pennsylvania

      AI For Business

      習得できるスキル: Accounting, Applied Machine Learning, Artificial Neural Networks, Big Data, Business Analysis, Clinical Data Management, Computational Thinking, Computer Programming, Customer Analysis, Customer Relationship Management, Customer Success, Data Analysis, Data Management, Data Mining, Data Warehousing, Database Administration, Databases, Deep Learning, Entrepreneurship, Feature Engineering, Finance, Financial Analysis, Human Resources, Leadership, Leadership and Management, Machine Learning, Marketing, Natural Language Processing, Reinforcement Learning, Sales, Security Engineering, Software Security, Strategy and Operations, Theoretical Computer Science

      4.7

      (80件のレビュー)

      Beginner · Specialization

    • DeepLearning.AI

      DeepLearning.AI

      Deep Learning

      習得できるスキル: Advertising, Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Business Psychology, Communication, Computational Logic, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Networking, Computer Programming, Computer Vision, Deep Learning, Entrepreneurship, General Statistics, Hardware Design, Human Computer Interaction, Interactive Design, Leadership and Management, Linear Algebra, Machine Learning, Machine Learning Algorithms, Marketing, Markov Model, Mathematical Theory & Analysis, Mathematics, Modeling, Natural Language Processing, Network Architecture, Object Detection, Probability & Statistics, Project, Project Management, Python Programming, Regression, Sales, Statistical Machine Learning, Statistical Programming, Strategy, Strategy and Operations, Supply Chain Systems, Supply Chain and Logistics, Theoretical Computer Science, User Experience

      4.8

      (133.3k件のレビュー)

      Intermediate · Specialization

    • Placeholder
      Imperial College London

      Imperial College London

      Mathematics for Machine Learning

      習得できるスキル: Algebra, Algorithms, Analysis, Artificial Neural Networks, Basic Descriptive Statistics, Calculus, Computer Programming, Data Analysis, Deep Learning, Differential Equations, General Statistics, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Probability Distribution, Python Programming, Regression, Statistical Programming, Theoretical Computer Science

      4.6

      (12.8k件のレビュー)

      Beginner · Specialization

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      IBM

      IBM

      AI Foundations for Everyone

      習得できるスキル: Applied Machine Learning, Cloud Computing, Computer Vision, Deep Learning, Ethics, IBM Cloud, Machine Learning, Machine Learning Algorithms, Natural Language Processing, Software Engineering, Software Engineering Tools, Web Development, Web Development Tools

      4.7

      (11.3k件のレビュー)

      Beginner · Specialization

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      Duke University

      Duke University

      AI Product Management

      習得できるスキル: Algorithms, Artificial Neural Networks, Business Psychology, Computer Networking, Computer Vision, Data Management, Data Structures, Database Administration, Databases, Deep Learning, Design and Product, Econometrics, Entrepreneurship, General Statistics, Human Computer Interaction, Leadership and Management, Machine Learning, Machine Learning Algorithms, Natural Language Processing, Network Security, Operating Systems, Probability & Statistics, Product Management, Project, Regression, Research and Design, Security Engineering, Strategy and Operations, Systems Design, Theoretical Computer Science, User Experience, User Experience Design

      4.6

      (81件のレビュー)

      Beginner · Specialization

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      IBM

      IBM

      IBM Data Science

      習得できるスキル: Algebra, Algorithms, Business Analysis, 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, Geovisualization, Interactive Data Visualization, Logistic Regression, Machine Learning, Machine Learning Algorithms, Marketing, Mathematical Theory & Analysis, Mathematics, Modeling, Plot (Graphics), Probability & Statistics, Python Programming, R Programming, Regression, SPSS, SQL, Spreadsheet Software, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Supply Chain Systems, Supply Chain and Logistics, Theoretical Computer Science

      4.6

      (94k件のレビュー)

      Beginner · Professional Certificate

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      Google

      Google

      Google Data Analytics

      習得できるスキル: Algorithms, Application Development, Big Data, Budget Management, Business Analysis, Business Communication, Change Management, Cloud Computing, Communication, Computational Logic, Computer Networking, Computer Programming, Computer Programming Tools, Cryptography, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Model, Data Structures, Data Type, Data Visualization, Data Visualization Software, Database Administration, Database Design, Databases, Decision Making, Design and Product, Entrepreneurship, Extract, Transform, Load, Feature Engineering, Finance, Financial Analysis, General Statistics, Ggplot2, Interactive Data Visualization, Leadership and Management, Machine Learning, Mathematical Theory & Analysis, Mathematics, Network Security, Other Programming Languages, Plot (Graphics), Privacy, Probability & Statistics, Problem Solving, Product Design, Programming Principles, Project Management, R Programming, Research and Design, SQL, Security Engineering, Security Strategy, Small Data, Software, Software Engineering, Software Security, Spreadsheet Software, Statistical Analysis, Statistical Programming, Storytelling, Strategy and Operations, Theoretical Computer Science, Visual Design

      4.8

      (64.6k件のレビュー)

      Beginner · Professional Certificate

    • Placeholder
      University of Washington

      University of Washington

      Machine Learning

      習得できるスキル: Algorithms, Analysis, Applied Machine Learning, Boosting (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, Decision Tree, Deep Learning, Distributed Computing Architecture, Entrepreneurship, Estimation, Exploratory Data Analysis, General Statistics, Machine Learning, Machine Learning Algorithms, Markov Model, Mathematical Theory & Analysis, Mathematics, Modeling, Natural Language Processing, Probability & Statistics, Python Programming, Regression, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Theoretical Computer Science

      4.6

      (15.6k件のレビュー)

      Intermediate · Specialization

    artificial intelligenceに関連する検索

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

    要約して、artificial intelligence の人気コース10選をご紹介します。

    • 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
    • Deep Learning: DeepLearning.AI
    • Mathematics for Machine Learning: Imperial College London
    • AI Foundations for Everyone: IBM
    • AI Product Management: Duke University
    • IBM Data Science: IBM

    人工知能に関するよくある質問

    • 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.‎

    このFAQの内容は、情報提供のみを目的としています。受講生は、自分の個人的、職業的、経済的な目標に合ったコースやその他の資格を取得するために、さらに調べることをお勧めします。
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