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    • Reinforcement Learning

    Reinforcement Learning Courses Online

    Study reinforcement learning for AI applications. Learn to design algorithms that learn from interaction with their environment.

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    Explore the Reinforcement Learning Course Catalog

    • U

      University of London

      Graduate Certificate in Computer Science

      Skills you'll gain: Virtual Reality, Human Computer Interaction, Game Design, Full-Stack Web Development, React Native, Agile Software Development, Animations, Data Ethics, Git (Version Control System), Event-Driven Programming, Network Security, Web Applications, Data Science, Natural Language Processing, Unsupervised Learning, Combinatorics, Database Design, Web Development, Generative AI, Usability Testing

      Credit offered

      Graduate Certificate · 6 - 12 Months

    • G

      Google Cloud

      Responsible AI for Developers: Fairness & Bias - Polski

      Skills you'll gain: Data Ethics, Artificial Intelligence, Artificial Intelligence and Machine Learning (AI/ML), Open Source Technology, Google Cloud Platform, Tensorflow

      Intermediate · Course · 1 - 3 Months

    • U

      Universidad de los Andes

      Analítica de datos: visualización, predicción y toma de decisiones Certificado MasterTrack®

      Skills you'll gain: Network Analysis, Data Storytelling, Predictive Modeling, Data Presentation, Marketing Analytics, Cash Flows, Product Marketing, Matplotlib, Financial Data, Financial Statement Analysis, Portfolio Management, Data Cleansing, Advertising, Applied Machine Learning, Statistical Machine Learning, Random Forest Algorithm, Operations Research, Natural Language Processing, Mathematical Modeling, Systems Analysis

      Credit offered

      Mastertrack · 6 - 12 Months

    • U

      University of Colorado Boulder

      Master of Science in Data Science

      Skills you'll gain: Object Oriented Design, User Story, SQL, New Product Development, Real-Time Operating Systems, Model Based Systems Engineering, Unsupervised Learning, Field-Programmable Gate Array (FPGA), Delegation Skills, Failure Analysis, Sampling (Statistics), Supplier Management, Sustainability Reporting, Accountability, Data Ethics, Data Mining, Statistical Modeling, Database Design, Goal Setting, Generative AI

      Earn a degree

      Degree · 1 - 4 Years

    • P

      Pontificia Universidad Católica de Chile

      Magíster en Business Analytics

      Skills you'll gain: Revenue Management, Customer Analysis, Descriptive Analytics, FinTech, Database Design, People Analytics, Time Series Analysis and Forecasting, Biostatistics, Predictive Analytics, Statistical Reporting, Digital Transformation, Deep Learning, Risk Analysis, Bayesian Statistics, Cloud Security, Network Model, Data Visualization, Assertiveness, Web Scraping, Governance

      Earn a degree

      Degree · 1 - 4 Years

    • U

      University of London

      Graduate Diploma in Computer Science

      Skills you'll gain: Virtual Reality, Human Computer Interaction, Game Design, Full-Stack Web Development, React Native, Agile Software Development, Animations, Data Ethics, Git (Version Control System), Event-Driven Programming, Network Security, Web Applications, Data Science, Natural Language Processing, Unsupervised Learning, Combinatorics, Database Design, Web Development, Generative AI, Usability Testing

      Credit offered

      Graduate Certificate · 6 - 12 Months

    • Status: New
      New
      G

      Google Cloud

      Usar o BigQuery ML para inferência

      Skills you'll gain:

      Beginner · Course · 1 - 4 Weeks

    • Status: New
      New
      G

      Google Cloud

      使用 BigQuery 机器学习推理功能

      Skills you'll gain:

      Beginner · Course · 1 - 4 Weeks

    • U

      Universidad de los Andes

      Maestría en Ingeniería de Software

      Skills you'll gain: User Story, Process Analysis, Maintainability, Test Automation, Angular, Android Jetpack, Risk Mitigation, Cloud-Native Computing, Dashboard, Design Thinking, Site Reliability Engineering, API Design, Video Game Development, Internet Of Things, Sprint Planning, User Experience Design, Microservices, Machine Learning, Security Requirements Analysis, Cloud Applications

      Earn a degree

      Degree · 1 - 4 Years

    • I

      Indian Statistical Institute

      Postgraduate Diploma in Applied Statistics

      Skills you'll gain: Sampling (Statistics), Time Series Analysis and Forecasting, Sample Size Determination, Statistical Inference, Statistical Machine Learning, Statistical Analysis, NumPy, Spatial Data Analysis, Statistical Hypothesis Testing, Probability, Data Compilation, Economics, Statistical Methods, Dimensionality Reduction, Lifelong Learning, Regression Analysis, Surveys, Bayesian Statistics, Graphing, Market Data

      Postgraduate Diploma · 6 - 12 Months

    • U

      University of Pittsburgh

      Master of Data Science

      Skills you'll gain:

      Earn a degree

      Degree · 1 - 4 Years

    • U

      University of Illinois at Urbana-Champaign

      Master of Science in Accounting

      Skills you'll gain: Data Visualization, Data Storytelling, Management Accounting, Fund Accounting, Operations Management, Mergers & Acquisitions, Financial Statement Analysis, Marketing, Risk Management, Machine Learning Algorithms, Business Strategy, Extract, Transform, Load, Financial Auditing, Descriptive Statistics, Process Improvement, Marketing Analytics, Generative AI, Financial Market, Strategic Decision-Making, Corporate Tax

      Earn a degree

      Degree · 1 - 4 Years

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

    • Graduate Certificate in Computer Science: University of London
    • Responsible AI for Developers: Fairness & Bias - Polski: Google Cloud
    • Analítica de datos: visualización, predicción y toma de decisiones Certificado MasterTrack®: Universidad de los Andes
    • Master of Science in Data Science: University of Colorado Boulder
    • Magíster en Business Analytics: Pontificia Universidad Católica de Chile
    • Graduate Diploma in Computer Science: University of London
    • Usar o BigQuery ML para inferência: Google Cloud
    • 使用 BigQuery 机器学习推理功能: Google Cloud
    • Maestría en Ingeniería de Software: Universidad de los Andes
    • Postgraduate Diploma in Applied Statistics: Indian Statistical Institute

    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)
    Reinforcement Learning (13)
    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.‎

    Online Reinforcement Learning courses offer a convenient and flexible way to enhance your knowledge or learn new Reinforcement Learning skills. Choose from a wide range of Reinforcement Learning courses offered by top universities and industry leaders tailored to various skill levels.‎

    When looking to enhance your workforce's skills in Reinforcement Learning, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    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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