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    • Recommender Systems

    Recommender Systems Courses Online

    Master recommender systems for personalized recommendations. Learn to build and evaluate recommendation algorithms for various applications.

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    Explore the Recommender Systems Course Catalog

    • Status: Free Trial
      Free Trial
      U

      University of Minnesota

      Recommender Systems

      Skills you'll gain: AI Personalization, Machine Learning Algorithms, Taxonomy, Applied Machine Learning, Machine Learning, Dimensionality Reduction, Performance Metric, Spreadsheet Software, Data Collection, Performance Measurement, Benchmarking, Usability Testing, Exploratory Data Analysis, A/B Testing, Analysis, Artificial Intelligence and Machine Learning (AI/ML), Technical Design, Algorithms, System Design and Implementation, Predictive Modeling

      4.3
      Rating, 4.3 out of 5 stars
      ·
      818 reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      Unsupervised Learning, Recommenders, Reinforcement Learning

      Skills you'll gain: Unsupervised Learning, Machine Learning Methods, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Data Ethics, Machine Learning, Machine Learning Algorithms, Supervised Learning, Reinforcement Learning, Statistical Machine Learning, Artificial Neural Networks, Deep Learning, Anomaly Detection, Dimensionality Reduction, Algorithms, Collaborative Software

      4.9
      Rating, 4.9 out of 5 stars
      ·
      4.7K reviews

      Beginner · Course · 1 - 4 Weeks

    • S

      Sungkyunkwan University

      Recommender Systems

      Skills you'll gain: Scalability, Deep Learning, Applied Machine Learning, AI Personalization, Data Mining, Unsupervised Learning, Predictive Modeling, Data Processing, Machine Learning, Machine Learning Algorithms, Algorithms, Artificial Neural Networks, Analysis

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      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

    • E

      EIT Digital

      Basic Recommender Systems

      Skills you'll gain: Data Ethics, Usability, System Requirements, Machine Learning Algorithms, Innovation, Algorithms, Predictive Modeling, Data-Driven Decision-Making, Data Mining, Applied Machine Learning, Statistical Methods, Performance Tuning

      4.3
      Rating, 4.3 out of 5 stars
      ·
      43 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      D
      S

      Multiple educators

      Machine Learning

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Machine Learning Methods, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning Algorithms, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Tensorflow, Scikit Learn (Machine Learning Library), Artificial Intelligence, NumPy, Predictive Modeling, Deep Learning, Reinforcement Learning, Random Forest Algorithm, Feature Engineering

      Build toward a degree

      4.9
      Rating, 4.9 out of 5 stars
      ·
      34K reviews

      Beginner · Specialization · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Minnesota

      Introduction to Recommender Systems: Non-Personalized and Content-Based

      Skills you'll gain: Taxonomy, AI Personalization, Spreadsheet Software, Machine Learning, Predictive Analytics, Statistical Methods, Persona Development, Descriptive Statistics, Data Collection, Algorithms, Java Programming

      4.4
      Rating, 4.4 out of 5 stars
      ·
      647 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Minnesota

      Recommender Systems: Evaluation and Metrics

      Skills you'll gain: Performance Metric, Data Collection, Performance Measurement, Benchmarking, Usability Testing, A/B Testing, Analysis, Data-Driven Decision-Making, Predictive Analytics, Customer Engagement

      4.4
      Rating, 4.4 out of 5 stars
      ·
      233 reviews

      Mixed · Course · 1 - 3 Months

    • P

      Packt

      Recommender Systems Complete Course Beginner to Advanced

      Skills you'll gain: Tensorflow, Keras (Neural Network Library), Natural Language Processing, Deep Learning, Predictive Modeling, Applied Machine Learning, Time Series Analysis and Forecasting, Artificial Neural Networks, Machine Learning, Machine Learning Algorithms

      Intermediate · Course · 1 - 4 Weeks

    • P

      Packt

      Building Recommender Systems with Machine Learning and AI

      Skills you'll gain: Apache Spark, Deep Learning, Python Programming, Scalability, Applied Machine Learning, Unsupervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Dimensionality Reduction, Feature Engineering, Performance Tuning

      Intermediate · Course · 3 - 6 Months

    • E

      EIT Digital

      Advanced Recommender Systems

      Skills you'll gain: Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Feature Engineering, Dimensionality Reduction, Machine Learning, Unsupervised Learning, Supervised Learning, Predictive Modeling, Algorithms, Performance Tuning, Data Analysis

      3.8
      Rating, 3.8 out of 5 stars
      ·
      23 reviews

      Intermediate · Course · 1 - 3 Months

    • P

      Packt

      Recommender Systems: An Applied Approach using Deep Learning

      Skills you'll gain: Deep Learning, Tensorflow, Data Processing, AI Personalization, Data Manipulation, Applied Machine Learning, Predictive Modeling, Machine Learning, Artificial Neural Networks, Data Visualization Software, System Design and Implementation, Matplotlib

      Intermediate · Course · 1 - 4 Weeks

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    In summary, here are 10 of our most popular recommender systems courses

    • Recommender Systems: University of Minnesota
    • Unsupervised Learning, Recommenders, Reinforcement Learning: DeepLearning.AI
    • Recommender Systems: Sungkyunkwan University
    • IBM AI Engineering: IBM
    • Basic Recommender Systems: EIT Digital
    • Machine Learning: DeepLearning.AI
    • Introduction to Recommender Systems: Non-Personalized and Content-Based: University of Minnesota
    • Recommender Systems: Evaluation and Metrics: University of Minnesota
    • Recommender Systems Complete Course Beginner to Advanced: Packt
    • Building Recommender Systems with Machine Learning and AI: Packt

    Skills you can learn in Probability And Statistics

    R Programming (19)
    Inference (16)
    Linear Regression (12)
    Statistical Analysis (12)
    Statistical Inference (11)
    Regression Analysis (10)
    Biostatistics (9)
    Bayesian (7)
    Logistic Regression (7)
    Probability Distribution (7)
    Bayesian Statistics (6)
    Medical Statistics (6)

    Frequently Asked Questions about Recommender Systems

    Recommender systems are processes that information filtering systems use to identify and predict the amount of interest a user is likely to have in items. Recommender systems then suggest those items that are the most likely to be well received by the user. These systems are mainly used in commercial or retail settings, to show potential customers what previous customers with similar interests also viewed or purchased. The goal of using a recommender system is to increase sales by showing users the items they're most likely to want.‎

    When you learn about recommender systems, you can become more valuable to your employer by helping to increase sales by applying this deep-learning tactic. It can help you become more data literate as a professional in the field of marketing. If you enjoy advanced mathematics or building spreadsheets, learning about recommender systems may prove especially satisfying to you because it involves using algorithms and spreadsheets. Learning how to program recommender systems is important for IT teams and website builders working for commercial companies.‎

    Learning about recommender systems can help you launch a new career in data science or in the IT field. You could work for large companies that want to keep visitors on their sites as long as possible by offering products, music, or videos that site users are likely to appreciate based on previous behaviors. Other career fields you could enter after adding recommender systems to your educational portfolio include data science, data mining, machine learning, and artificial intelligence (AI).‎

    Taking courses on Coursera can help you learn about recommender systems by introducing the information at your current level of study, so you are challenged enough to find the learning exciting. It can also help because you get to progress through the recommender systems courses while covering topics, such as TensorFlow and collaborative filtering, at your own pace on Coursera, so you can finish as quickly or as slowly as you need to thoroughly absorb the material.‎

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

    When looking to enhance your workforce's skills in Recommender Systems, 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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