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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

    • D

      DeepLearning.AI

      Mathematics for Machine Learning and Data Science

      Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Probability, Linear Algebra, Statistical Inference, Applied Mathematics, NumPy, Calculus, Dimensionality Reduction, Numerical Analysis, Mathematical Modeling, Machine Learning, Machine Learning Methods, Python Programming, Jupyter, Data Manipulation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2.7K reviews

      Intermediate · Specialization · 1 - 3 Months

    • I

      IBM

      IBM AI Engineering

      Skills you'll gain: Prompt Engineering, Large Language Modeling, PyTorch (Machine Learning Library), Supervised Learning, Feature Engineering, Generative AI, Keras (Neural Network Library), Deep Learning, Jupyter, Natural Language Processing, Reinforcement Learning, Unsupervised Learning, Generative AI Agents, Scikit Learn (Machine Learning Library), Image Analysis, Data Manipulation, Tensorflow, Python Programming, Verification And Validation, Artificial Neural Networks

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      20K reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • I

      IBM

      IBM Machine Learning

      Skills you'll gain: Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Feature Engineering, Generative AI, Dimensionality Reduction, Reinforcement Learning, Data Cleansing, Data Access, Deep Learning, Data Analysis, Regression Analysis, Applied Machine Learning, Machine Learning Algorithms, Machine Learning, Statistical Analysis, Statistical Inference, Statistical Hypothesis Testing, Classification And Regression Tree (CART), Scikit Learn (Machine Learning Library)

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      3.1K reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • I

      IBM

      Introduction to Artificial Intelligence (AI)

      Skills you'll gain: Large Language Modeling, Artificial Intelligence, Generative AI, Data Ethics, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Deep Learning, Artificial Neural Networks, Governance, Prompt Engineering, Machine Learning, Automation, Digital Transformation, Business Transformation, Business Technologies, Ethical Standards And Conduct, Computer Vision, Emerging Technologies, Natural Language Processing

      4.7
      Rating, 4.7 out of 5 stars
      ·
      19K reviews

      Beginner · Course · 1 - 4 Weeks

    • J

      Johns Hopkins University

      Applied Machine Learning

      Skills you'll gain: PyTorch (Machine Learning Library), Unsupervised Learning, Computer Vision, Machine Learning Algorithms, Applied Machine Learning, Image Analysis, Dimensionality Reduction, Supervised Learning, Reinforcement Learning, Feature Engineering, Regression Analysis, Data Cleansing, Machine Learning, Data Mining, Scikit Learn (Machine Learning Library), Statistical Machine Learning, Advanced Analytics, Deep Learning, Artificial Neural Networks, Decision Tree Learning

      3.7
      Rating, 3.7 out of 5 stars
      ·
      7 reviews

      Intermediate · Specialization · 3 - 6 Months

    • D

      DeepLearning.AI

      Generative AI with Large Language Models

      Skills you'll gain: Generative AI, Large Language Modeling, OpenAI, ChatGPT, Prompt Engineering, PyTorch (Machine Learning Library), Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Tensorflow, Applied Machine Learning, Scalability, Natural Language Processing, Application Deployment, Reinforcement Learning, Performance Tuning, Performance Metric

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3.3K reviews

      Intermediate · Course · 1 - 4 Weeks

    • U

      University of Alberta

      A Complete Reinforcement Learning System (Capstone)

      Skills you'll gain: Reinforcement Learning, Solution Architecture, Artificial Intelligence, Performance Testing, Artificial Neural Networks, Machine Learning Algorithms, Markov Model, Algorithms, Debugging

      4.7
      Rating, 4.7 out of 5 stars
      ·
      638 reviews

      Intermediate · Course · 1 - 3 Months

    • N

      New York Institute of Finance

      Reinforcement Learning for Trading Strategies

      Skills you'll gain: Reinforcement Learning, Financial Trading, Deep Learning, Portfolio Management, Machine Learning Methods, Artificial Intelligence and Machine Learning (AI/ML), Artificial Neural Networks, Machine Learning Software, Applied Machine Learning, Markov Model, Machine Learning, Financial Market, Time Series Analysis and Forecasting

      3.5
      Rating, 3.5 out of 5 stars
      ·
      239 reviews

      Intermediate · Course · 1 - 4 Weeks

    • I

      Imperial College London

      Mathematics for Machine Learning

      Skills you'll gain: Linear Algebra, Dimensionality Reduction, NumPy, Regression Analysis, Calculus, Applied Mathematics, Probability & Statistics, Feature Engineering, Jupyter, Advanced Mathematics, Data Science, Statistics, Machine Learning Algorithms, Machine Learning Methods, Statistical Analysis, Artificial Neural Networks, Algorithms, Data Manipulation, Python Programming, Machine Learning

      4.6
      Rating, 4.6 out of 5 stars
      ·
      15K reviews

      Beginner · Specialization · 3 - 6 Months

    • G

      Google Cloud

      Reinforcement Learning: Qwik Start

      Skills you'll gain: Reinforcement Learning, Google Cloud Platform, GitHub, Tensorflow, Artificial Intelligence, Machine Learning, Jupyter

      3.7
      Rating, 3.7 out of 5 stars
      ·
      10 reviews

      Beginner · Project · Less Than 2 Hours

    • I

      IBM

      AI Foundations for Everyone

      Skills you'll gain: Prompt Engineering, ChatGPT, Large Language Modeling, Generative AI, Artificial Intelligence, Data Ethics, Artificial Intelligence and Machine Learning (AI/ML), OpenAI, IBM Cloud, Private Cloud, Data Loss Prevention, Applied Machine Learning, Deep Learning, WordPress, Artificial Neural Networks, Governance, Machine Learning, Generative AI Agents, Automation, Digital Transformation

      4.7
      Rating, 4.7 out of 5 stars
      ·
      26K reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of Michigan

      Python for Everybody

      Skills you'll gain: Web Scraping, Data Processing, Relational Databases, JSON, Database Design, SQL, Network Protocols, Databases, Web Services, Restful API, Data Modeling, Programming Principles, Data Structures, Data Collection, Data Visualization Software, Data Manipulation, Computer Programming, Python Programming, Data Import/Export, Software Installation

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      277K reviews

      Beginner · Specialization · 3 - 6 Months

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

    • Mathematics for Machine Learning and Data Science: DeepLearning.AI
    • IBM AI Engineering: IBM
    • IBM Machine Learning: IBM
    • Introduction to Artificial Intelligence (AI): IBM
    • Applied Machine Learning: Johns Hopkins University
    • Generative AI with Large Language Models: DeepLearning.AI
    • A Complete Reinforcement Learning System (Capstone): University of Alberta
    • Reinforcement Learning for Trading Strategies: New York Institute of Finance
    • Mathematics for Machine Learning: Imperial College London
    • Reinforcement Learning: Qwik Start: Google Cloud

    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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