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    • Reinforcement Learning

    篩選依據

    ''reinforcement learning'的 84 個結果

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      University of Alberta

      Reinforcement Learning

      您將獲得的技能: Machine Learning, Reinforcement Learning, Artificial Neural Networks, Entrepreneurship, Mathematics, Machine Learning Algorithms, Python Programming, Statistical Programming, Business Psychology, Computer Programming, Markov Model, Theoretical Computer Science, Algorithms, Deep Learning, Leadership and Management, Planning, Software Architecture, Software Engineering, Supply Chain and Logistics, Operations Research, Research and Design, Strategy and Operations

      4.7

      (3.1k 條評論)

      Intermediate · Specialization · 3-6 Months

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      DeepLearning.AI,Stanford University

      Machine Learning

      您將獲得的技能: 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

      (8.1k 條評論)

      Beginner · Specialization · 1-3 Months

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      University of Alberta

      Fundamentals of Reinforcement Learning

      您將獲得的技能: Machine Learning, Reinforcement Learning, Machine Learning Algorithms, Python Programming, Statistical Programming, Markov Model, Computer Programming, Mathematics, Operations Research, Research and Design, Strategy and Operations

      4.8

      (2.5k 條評論)

      Intermediate · Course · 1-3 Months

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      DeepLearning.AI

      Unsupervised Learning, Recommenders, Reinforcement Learning

      您將獲得的技能: Machine Learning, Probability & Statistics, Machine Learning Algorithms, General Statistics, Applied Machine Learning, Theoretical Computer Science, Algorithms, Mathematics, Reinforcement Learning, Econometrics, Data Management, Data Structures, Tensorflow, Artificial Neural Networks, Data Analysis, Data Mining, Mathematical Theory & Analysis, Probability Distribution, Bayesian Statistics, Computer Programming, Operations Research, Python Programming, Research and Design, Statistical Programming, Strategy and Operations, Communication

      4.9

      (783 條評論)

      Beginner · Course · 1-4 Weeks

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      New York University

      Machine Learning and Reinforcement Learning in Finance

      您將獲得的技能: Machine Learning, Finance, Machine Learning Algorithms, Mathematics, Algorithms, Probability & Statistics, Theoretical Computer Science, Applied Mathematics, Calculus, General Statistics, Investment Management, Applied Machine Learning, Artificial Neural Networks, Business Analysis, Data Analysis, Deep Learning, Financial Analysis, Machine Learning Software, Tensorflow, Advertising, Communication, Computer Programming, Entrepreneurship, Marketing, Markov Model, Operations Research, Python Programming, Research and Design, Statistical Machine Learning, Strategy and Operations

      3.7

      (771 條評論)

      Intermediate · Specialization · 3-6 Months

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      New York Institute of Finance

      Machine Learning for Trading

      您將獲得的技能: Machine Learning, Finance, Leadership and Management, Cloud Computing, Cloud Platforms, Risk Management, Strategy, Applied Machine Learning, Artificial Neural Networks, Entrepreneurship, Investment Management, Marketing, Probability & Statistics, Sales, Securities Trading, Strategy and Operations, Business Psychology, Computer Programming, General Statistics, Mathematics, Python Programming, Reinforcement Learning, Statistical Programming

      3.9

      (1k 條評論)

      Intermediate · Specialization · 1-3 Months

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      DeepLearning.AI

      Deep Learning

      您將獲得的技能: 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

      (137.8k 條評論)

      Intermediate · Specialization · 3-6 Months

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      IBM Skills Network

      Deep Learning and Reinforcement Learning

      您將獲得的技能: Deep Learning, Machine Learning, Artificial Neural Networks, Computer Vision, Computer Programming, Python Programming, Reinforcement Learning, Statistical Programming

      4.6

      (125 條評論)

      Intermediate · Course · 1-3 Months

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      Google Cloud

      Advanced Machine Learning on Google Cloud

      您將獲得的技能: Machine Learning, Cloud Computing, Google Cloud Platform, Cloud Platforms, Probability & Statistics, Business Psychology, General Statistics, Deep Learning, Entrepreneurship, Statistical Programming, Apache, Cloud Applications, Data Management, Machine Learning Software, Natural Language Processing, Python Programming, Reinforcement Learning, Tensorflow, Artificial Neural Networks, Computer Vision, Performance Management, Strategy and Operations, Applied Machine Learning, Cloud API, Computational Thinking, Computer Architecture, Computer Programming, Data Analysis, Data Engineering, Distributed Computing Architecture, Hardware Design, Machine Learning Algorithms, Other Cloud Platforms and Tools, Theoretical Computer Science

      4.5

      (1.4k 條評論)

      Advanced · Specialization · 3-6 Months

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      Google Cloud

      Reinforcement Learning: Qwik Start

      您將獲得的技能: Reinforcement Learning

      Beginner · Project · Less Than 2 Hours

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      University of Alberta

      A Complete Reinforcement Learning System (Capstone)

      您將獲得的技能: Artificial Neural Networks, Machine Learning, Reinforcement Learning

      4.7

      (583 條評論)

      Intermediate · Course · 1-3 Months

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      Alberta Machine Intelligence Institute

      Machine Learning: Algorithms in the Real World

      您將獲得的技能: 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

      (1k 條評論)

      Intermediate · Specialization · 3-6 Months

    與 reinforcement learning 相關的搜索

    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…7

    總之,這是我們最受歡迎的 reinforcement learning 門課程中的 10 門

    • Reinforcement Learning: University of Alberta
    • Machine Learning: DeepLearning.AI
    • Fundamentals of Reinforcement Learning: University of Alberta
    • Unsupervised Learning, Recommenders, Reinforcement Learning: DeepLearning.AI
    • Machine Learning and Reinforcement Learning in Finance: New York University
    • Machine Learning for Trading: New York Institute of Finance
    • Deep Learning: DeepLearning.AI
    • Deep Learning and Reinforcement Learning: IBM Skills Network
    • Advanced Machine Learning on Google Cloud: Google Cloud
    • Reinforcement Learning: Qwik Start: Google Cloud

    您可以在 Machine Learning 中學到的技能

    Python 程序設計 (33)
    Tensorflow (32)
    深度學習 (30)
    人工神經網絡 (24)
    大數據 (18)
    統計分類 (17)
    代數 (10)
    貝葉斯定理 (10)
    線性代數 (10)
    線性回歸 (9)
    Numpy (9)

    關於 強化學習 的常見問題

    • 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.‎

    此常見問題解答內容僅供參考。建議學生多做研究,確保所追求的課程和其他證書符合他們的個人、專業和財務目標。
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