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    86 Ergebnisse für „reinforcement learning“

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      University of Alberta

      Reinforcement Learning

      Kompetenzen, die Sie erwerben: Machine Learning, Reinforcement Learning, Artificial Neural Networks, Machine Learning Algorithms, Mathematics, Python Programming, Statistical Programming, Entrepreneurship, Markov Model, Business Psychology, Computer Programming, Deep Learning, Theoretical Computer Science, Algorithms, Leadership and Management, Planning, Software Architecture, Software Engineering, Supply Chain and Logistics, Operations Research, Research and Design, Strategy and Operations

      4.7

      (3.1k Bewertungen)

      Intermediate · Specialization · 3-6 Months

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      DeepLearning.AI

      Unsupervised Learning, Recommenders, Reinforcement Learning

      Kompetenzen, die Sie erwerben: Machine Learning, Probability & Statistics, General Statistics, Machine Learning Algorithms, Applied Machine Learning, Mathematics, Reinforcement Learning, Theoretical Computer Science, Econometrics, Algorithms, 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

      (963 Bewertungen)

      Beginner · Course · 1-4 Weeks

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      DeepLearning.AI, Stanford University

      Machine Learning

      Kompetenzen, die Sie erwerben: Machine Learning, Probability & Statistics, Machine Learning Algorithms, General Statistics, Theoretical Computer Science, Applied Machine Learning, Algorithms, 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

      (9.6k Bewertungen)

      Beginner · Specialization · 1-3 Months

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      University of Alberta

      Fundamentals of Reinforcement Learning

      Kompetenzen, die Sie erwerben: 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 Bewertungen)

      Intermediate · Course · 1-3 Months

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      Columbia University

      Decision Making and Reinforcement Learning

      Kompetenzen, die Sie erwerben: Deep Learning, Machine Learning, Reinforcement Learning

      Intermediate · Course · 1-3 Months

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      DeepLearning.AI

      Deep Learning

      Kompetenzen, die Sie erwerben: 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

      (138.4k Bewertungen)

      Intermediate · Specialization · 3-6 Months

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      New York Institute of Finance

      Machine Learning for Trading

      Kompetenzen, die Sie erwerben: 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 Bewertungen)

      Intermediate · Specialization · 1-3 Months

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      New York University

      Machine Learning and Reinforcement Learning in Finance

      Kompetenzen, die Sie erwerben: Machine Learning, Finance, Algorithms, Mathematics, Theoretical Computer Science, Machine Learning Algorithms, Applied Mathematics, Calculus, General Statistics, Investment Management, Probability & Statistics, 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

      (775 Bewertungen)

      Intermediate · Specialization · 3-6 Months

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      IBM Skills Network

      Deep Learning and Reinforcement Learning

      Kompetenzen, die Sie erwerben: Deep Learning, Machine Learning, Artificial Neural Networks, Computer Programming, Computer Vision, Python Programming, Reinforcement Learning, Statistical Programming

      4.5

      (131 Bewertungen)

      Intermediate · Course · 1-3 Months

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      Google Cloud

      Reinforcement Learning: Qwik Start

      Kompetenzen, die Sie erwerben: Reinforcement Learning

      Beginner · Project · Less Than 2 Hours

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      University of Alberta

      A Complete Reinforcement Learning System (Capstone)

      Kompetenzen, die Sie erwerben: Artificial Neural Networks, Machine Learning, Reinforcement Learning, Computer Programming, Python Programming, Statistical Programming

      4.7

      (585 Bewertungen)

      Intermediate · Course · 1-3 Months

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      New York Institute of Finance

      Reinforcement Learning for Trading Strategies

      Kompetenzen, die Sie erwerben: Machine Learning, Artificial Neural Networks, Business Psychology, Cloud Computing, Computer Programming, Entrepreneurship, Finance, General Statistics, Investment Management, Leadership and Management, Marketing, Mathematics, Probability & Statistics, Python Programming, Reinforcement Learning, Sales, Statistical Programming, Strategy, Strategy and Operations

      3.6

      (204 Bewertungen)

      Intermediate · Course · 1-4 Weeks

    Suchanfragen mit Bezug zu reinforcement learning

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    Zusammenfassend sind hier 10 unserer beliebtesten reinforcement learning Kurse

      Fähigkeiten, die Sie bei Machine Learning erlernen können

      Python-Programmierung (33)
      TensorFlow (32)
      Deep Learning (30)
      Künstliches Neuronales Netz (24)
      Big Data (18)
      Statistische Klassifikation (17)
      Algebra (10)
      Bayes (10)
      Lineare Algebra (10)
      Lineare Regression (9)
      Numpy (9)

      Häufig gestellte Fragen zum Thema Verstärkungslernen

      • 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.‎

      Diese häufig gestellten Fragen dienen nur zu Informationszwecken. Den Lernenden wird empfohlen, eingehender zu recherchieren, ob Kurse und andere angestrebte Qualifikationen wirklich ihren persönlichen, beruflichen und finanziellen Vorstellungen entsprechen.
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