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    • Reinforcement Learning

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    52 results for "reinforcement learning"

    • University of Alberta

      University of Alberta

      Reinforcement Learning

      Skills you'll gain: Approximation, Artificial Neural Networks, Business Psychology, Computer Programming, Deep Learning, Entrepreneurship, Euler'S Totient Function, Leadership and Management, Machine Learning, Machine Learning Algorithms, Markov Model, Mathematics, Operations Research, Planning, Process, Python Programming, Reinforcement Learning, Relative Change And Difference, Research and Design, Statistical Programming, Strategy and Operations, Supply Chain and Logistics

      4.7

      (2.9k reviews)

      Intermediate · Specialization

    • DeepLearning.AI

      DeepLearning.AI

      Deep Learning

      Skills you'll gain: 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, Logistic Regression, Machine Learning, Machine Learning Algorithms, Marketing, Markov Model, Mathematical Theory & Analysis, Mathematics, Modeling, Natural Language Processing, Network Architecture, Probability & Statistics, Project Management, Python Programming, Regression, Sales, Speech, Statistical Machine Learning, Statistical Programming, Strategy, Strategy and Operations, Supply Chain Systems, Supply Chain and Logistics, Theoretical Computer Science

      4.8

      (133.2k reviews)

      Intermediate · Specialization

    • University of Alberta

      University of Alberta

      Fundamentals of Reinforcement Learning

      Skills you'll gain: Python Programming, Process, Strategy and Operations, Computer Programming, Operations Research, Mathematics, Research and Design, Reinforcement Learning, Statistical Programming, Machine Learning

      4.8

      (2.4k reviews)

      Intermediate · Course

    • New York University

      New York University

      Machine Learning and Reinforcement Learning in Finance

      Skills you'll gain: Accounting, Algorithms, Applied Mathematics, Artificial Neural Networks, Business Analysis, Calculus, Data Analysis, Entrepreneurship, Finance, Financial Analysis, General Statistics, Investment Management, Machine Learning, Machine Learning Algorithms, Mathematics, Probability & Statistics, Reinforcement Learning, Theoretical Computer Science

      3.7

      (742 reviews)

      Intermediate · Specialization

    • Google Cloud

      Google Cloud

      Reinforcement Learning: Qwik Start

      Beginner · Project

    • IBM

      IBM

      Deep Learning and Reinforcement Learning

      Skills you'll gain: Artificial Neural Networks, Python Programming, Statistical Programming, Computer Vision, Machine Learning, Deep Learning, Computer Programming, Reinforcement Learning

      4.6

      (98 reviews)

      Intermediate · Course

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      Google Cloud

      Google Cloud

      Machine Learning for Trading

      Skills you'll gain: Artificial Neural Networks, Business Psychology, Cloud Computing, Cloud Platforms, Computer Programming, Entrepreneurship, Finance, General Statistics, Investment Management, Leadership and Management, Machine Learning, Marketing, Mathematics, Modeling, Probability & Statistics, Python Programming, Reinforcement Learning, Risk Management, Sales, Statistical Programming, Strategy, Strategy and Operations, Trading

      3.9

      (937 reviews)

      Intermediate · Specialization

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      University of Washington

      University of Washington

      Designing Autonomous AI

      Beginner · Course

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      New York University

      New York University

      Reinforcement Learning in Finance

      3.6

      (118 reviews)

      Advanced · Course

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      Free

      University of Washington

      University of Washington

      Computational Neuroscience

      Skills you'll gain: Entrepreneurship, Python Programming, Matlab, Computer Networking, Data Analysis, Artificial Neural Networks, Machine Learning Algorithms, Mathematics, Computer Programming, General Statistics, Probability & Statistics, Machine Learning, Network Model, Neuroscience, Reinforcement Learning, Communication, Business Psychology, Linear Algebra, Data Analysis Software, Statistical Programming, Deep Learning, Modeling

      4.6

      (938 reviews)

      Beginner · Course

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      University of Alberta

      University of Alberta

      A Complete Reinforcement Learning System (Capstone)

      Skills you'll gain: Artificial Neural Networks, Machine Learning, Reinforcement Learning

      4.7

      (554 reviews)

      Intermediate · Course

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      University of Alberta

      University of Alberta

      Sample-based Learning Methods

      Skills you'll gain: Entrepreneurship, Machine Learning Algorithms, Planning, Machine Learning, Supply Chain and Logistics, Leadership and Management, Relative Change And Difference, Reinforcement Learning, Markov Model

      4.8

      (1.1k reviews)

      Intermediate · Course

    Searches related to reinforcement learning

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    In summary, here are 10 of our most popular reinforcement learning courses

    • Reinforcement Learning: University of Alberta
    • Deep Learning: DeepLearning.AI
    • Fundamentals of Reinforcement Learning: University of Alberta
    • Machine Learning and Reinforcement Learning in Finance: New York University
    • Reinforcement Learning: Qwik Start: Google Cloud
    • Deep Learning and Reinforcement Learning: IBM
    • Machine Learning for Trading: Google Cloud
    • Designing Autonomous AI: University of Washington
    • Reinforcement Learning in Finance: New York University
    • Computational Neuroscience: University of Washington

    Skills you can learn in Machine Learning

    Python Programming (33)
    Tensorflow (32)
    Deep Learning (30)
    Artificial Neural Network (24)
    Big Data (18)
    Statistical Classification (17)
    Algebra (10)
    Bayesian (10)
    Linear Algebra (10)
    Linear Regression (9)
    Numpy (9)

    Frequently Asked Questions about Reinforcement Learning

    • 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.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.
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