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    • Reinforcement Learning

    필터링 기준

    "reinforcement learning"에 대한 72개의 결과

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      University of Washington

      Machine Teaching for Autonomous AI

      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

      4.7

      (14개의 검토)

      Beginner · Course · 1-4 Weeks

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      IBM Skills Network

      Machine Learning Introduction for Everyone

      획득할 기술: Machine Learning, Algorithms, Data Analysis, Deep Learning, Machine Learning Algorithms, Probability & Statistics, Regression, Reinforcement Learning, Theoretical Computer Science

      4.5

      (89개의 검토)

      Beginner · Course · 1-4 Weeks

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      University of Colorado Boulder

      Machine Learning: Theory and Hands-on Practice with Python

      획득할 기술: Machine Learning, Statistical Machine Learning, Machine Learning Algorithms, Probability & Statistics, Python Programming, Statistical Programming, Regression, Deep Learning, Data Analysis, Artificial Neural Networks, Applied Machine Learning, Correlation And Dependence, Statistical Analysis, Statistical Tests, Exploratory Data Analysis, Algorithms, Reinforcement Learning, Theoretical Computer Science, Basic Descriptive Statistics, Data Mining, Feature Engineering, General Statistics, Natural Language Processing, Data Management, Data Structures, Dimensionality Reduction

      3.1

      (27개의 검토)

      Intermediate · Specialization · 3-6 Months

    • 무료

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      Caltech

      The Science of the Solar System

      획득할 기술: Business Analysis, Critical Thinking, Data Analysis, Data Visualization, Design and Product, Entrepreneurship, Exploratory Data Analysis, Leadership and Management, Machine Learning, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Problem Solving, Product Lifecycle, Reinforcement Learning, Research and Design, Scientific Visualization, Strategy and Operations

      4.8

      (729개의 검토)

      Mixed · Course · 1-3 Months

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      Autodesk

      Autodesk Certified Professional: Revit for Structural Design Exam Prep

      획득할 기술: Computer Graphics, Graphics Software, Collaboration, Communication, Leadership and Management, Machine Learning, Reinforcement Learning

      4.7

      (371개의 검토)

      Advanced · Course · 1-4 Weeks

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      University of Cape Town

      Fintech Startups in Emerging Markets

      획득할 기술: Finance, Regulations and Compliance, Banking, BlockChain, FinTech, Accounting, Business Analysis, Business Design, Cryptography, Data Analysis, Entrepreneurship, Financial Analysis, Financial Management, Machine Learning, Research and Design, Risk Management, Security Engineering, Theoretical Computer Science, Advertising, Communication, Machine Learning Algorithms, Marketing, Reinforcement Learning, Software Architecture, Software Engineering

      4.6

      (181개의 검토)

      Beginner · Specialization · 3-6 Months

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      Google Cloud

      Creating a Business Value with Data and Looker 日本語版

      획득할 기술: Data Visualization, Applied Machine Learning, Computer Networking, Databases, Machine Learning, Network Security, Reinforcement Learning, Security Engineering, Looker (Software)

      4.0

      (10개의 검토)

      Intermediate · Specialization · 3-6 Months

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      Alberta Machine Intelligence Institute

      Machine Learning: Algorithms in the Real World

      획득할 기술: Machine Learning, Machine Learning Algorithms, Strategy and Operations, Applied Machine Learning, Mathematics, Algorithms, Artificial Neural Networks, Data Analysis, Regression, Theoretical Computer Science, Reinforcement Learning, Basic Descriptive Statistics, Computer Programming, Data Analysis Software, Data Warehousing, Exploratory Data Analysis, Extract, Transform, Load, Linear Algebra, Probability & Statistics, Python Programming, Statistical Analysis

      4.6

      (1k개의 검토)

      Intermediate · Specialization · 3-6 Months

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      University of Alberta

      Prediction and Control with Function Approximation

      획득할 기술: Machine Learning, Reinforcement Learning, Artificial Neural Networks, Entrepreneurship, Algorithms, Computer Programming, Deep Learning, Machine Learning Algorithms, Python Programming, Statistical Programming, Theoretical Computer Science, Business Psychology

      4.8

      (752개의 검토)

      Intermediate · Course · 1-3 Months

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      University of Washington

      Building Autonomous AI

      획득할 기술: Reinforcement Learning

      Intermediate · Course · 1-3 Months

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      University of Washington

      Designing Autonomous AI

      획득할 기술: Reinforcement Learning

      Beginner · Course · 1-4 Weeks

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      Google Cloud

      Google Cloud Digital Leader Training 日本語版

      획득할 기술: Cloud Computing, DevOps, Computer Networking, Network Security, Security Engineering, Applied Machine Learning, Budget Management, Cloud API, Databases, Entrepreneurship, Finance, Innovation, Machine Learning, Operating Systems, Reinforcement Learning, Research and Design, Software Architecture, Software Engineering, System Security, Theoretical Computer Science

      4.5

      (28개의 검토)

      Beginner · Specialization · 3-6 Months

    reinforcement learning과(와) 관련된 검색

    reinforcement learning in finance
    reinforcement learning for trading strategies
    reinforcement learning: qwik start
    a complete reinforcement learning system (capstone)
    fundamentals of reinforcement learning
    unsupervised learning, recommenders, reinforcement learning
    machine learning and reinforcement learning in finance
    deep learning and reinforcement learning
    1234…6

    요약하자면, 여기에 가장 인기 있는 reinforcement learning 강좌 10개가 있습니다.

    • Machine Teaching for Autonomous AI: University of Washington
    • Machine Learning Introduction for Everyone: IBM Skills Network
    • Machine Learning: Theory and Hands-on Practice with Python: University of Colorado Boulder
    • The Science of the Solar System: Caltech
    • Autodesk Certified Professional: Revit for Structural Design Exam Prep: Autodesk
    • Fintech Startups in Emerging Markets: University of Cape Town
    • Creating a Business Value with Data and Looker 日本語版: Google Cloud
    • Machine Learning: Algorithms in the Real World: Alberta Machine Intelligence Institute
    • Prediction and Control with Function Approximation: University of Alberta
    • Building Autonomous AI: University of Washington

    Machine Learning에서 학습할 수 있는 스킬

    Python 프로그래밍 (33)
    TensorFlow (32)
    심층 학습 (30)
    인공 신경 회로망 (24)
    빅 데이터 (18)
    통계 분류 (17)
    강화 학습 (13)
    대수학 (10)
    베이지안 (10)
    선형 대수 (10)
    선형 회귀 (9)
    Numpy (9)

    강화 학습에 대한 자주 묻는 질문

    • Reinforcement learning is a machine learning paradigm in which software agents use a process of trial and error to learn how to complete tasks in a way that maximizes cumulative rewards as defined by their programmers. In contrast to supervised learning paradigms, reinforcement learning systems do not need labeled input/output pairs or explicit corrections of suboptimal actions; and, in contrast to unsupervised learning, reinforcement learning defines an explicit goal, which is the maximization of the value returned by the Q-learning (or “quality” learning) algorithm as a result of its actions.

      Because it combines the goal orientation of supervised learning with the flexibility of unsupervised learning, reinforcement learning is very important in creating artificial intelligence (AI) applications requiring successful problem-solving in complex situations. For example, they are often used in financial engineering to develop optimal trading algorithms for the stock market. They are also used to build intelligent systems to allow robots and self-driving cars to navigate real-world environments safely.‎

    • As one of the main paradigms for machine learning, reinforcement learning is an essential skill for careers in this fast-growing field. Reinforcement learning is particularly important for developing artificially intelligent digital agents for real-world problem-solving in industries like finance, automotive, robotics, logistics, and smart assistants. According to Glassdoor, the average annual salary for machine learning engineers in America is $114,121 per year, a high level of pay which reflects the high level of demand for this expertise.‎

    • Absolutely. Coursera hosts a wide variety of courses in reinforcement learning and related topics in machine learning, as well as the use of these techniques in applied contexts such as finance and self-driving cars. These courses and Specializations are offered by top-ranked institutions in this field, including the deepmind.ai, New York University, the University of Toronto, and the University of Alberta’s Machine Intelligence Institute. You can learn remotely on a flexible schedule while still getting feedback from expert professors and instructors, ensuring that you’ll get a high quality education with all the reinforcement you need to learn these valuable skills with confidence.‎

    • Because reinforcement learning itself isn't a beginner-level subject, you'll need to have a good grasp on the fundamentals of machine learning before starting to learn it. Additionally, many courses will require you to have a strong background in high-level mathematics such as linear algebra, statistics, and probability. Most courses will require you to be proficient in Python, although people familiar with other programming languages like C++, Matlab, and JavaScript can often use those skills to help them learn reinforcement learning. Having the ability to implement algorithms from pseudocode may be another prerequisite. As you progress, you'll gain skills in using reinforcement learning solutions to solve problems with probabilistic artificial intelligence, function approximation, and intelligent systems.‎

    • People best suited to roles within the reinforcement learning realm should have a passion for machine learning with a drive for analytics and data and an interest in providing frontline support to solve real-world problems while leveraging innate creative problem-solving skills. Additionally, many companies like to see that candidates have strong communication skills and the ability to collaborate across disciplines and departments. There are a variety of roles associated with reinforcement learning, including analysts, engineers, and researchers. In late February 2021, there were more than 1,800 job listings for people proficient in reinforcement learning on LinkedIn.‎

    • If you want to be a part of the future of machine learning, learning reinforcement learning may be a good move for you. This innovative machine learning technique creates an algorithm that learns through trial and error, leading to a combination of short- and long-term rewards such as the ability to define sequences to solve problems using a reward-based learning approach. It's useful across multiple industries, including the tech industry, business, advertising, finance, and e-commerce, all of which find reinforcement learning useful in part because of its ability to offer greater personalization. Ultimately, if you want to work within AI and machine learning, this could be a step to advancing your goals.‎

    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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