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    • Linear Algebra

    Linear Algebra Courses Online

    Study linear algebra for mathematical and engineering applications. Learn about matrices, vectors, and linear transformations.

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    Explore the Linear Algebra Course Catalog

    • P

      Packt

      Mastering Image Segmentation with PyTorch

      Skills you'll gain: PyTorch (Machine Learning Library), Image Analysis, Deep Learning, Computer Vision, Artificial Neural Networks, Data Processing, Debugging, Machine Learning Methods, Performance Tuning, Regression Analysis

      Beginner · Course · 1 - 4 Weeks

    • A

      American Psychological Association

      Measures of Correlation and Contingency

      Skills you'll gain: Regression Analysis, Correlation Analysis, Statistical Software, Statistical Analysis, Statistical Methods, Probability & Statistics, Quantitative Research, Data Analysis, Descriptive Statistics, Statistical Hypothesis Testing

      Beginner · Course · 1 - 3 Months

    • Status: New
      New
      Status: Free
      Free
      C

      Coursera Instructor Network

      Quantitative Research Methods: Tools for Data Analysis

      Skills you'll gain: Quantitative Research, Data-Driven Decision-Making, Descriptive Statistics, Data Literacy, Data Analysis, Statistics, Research Design, Statistical Analysis, Microsoft Excel, Research Methodologies, R Programming, Data Presentation, Data Collection, Correlation Analysis, Data Ethics

      Intermediate · Course · 1 - 4 Weeks

    • P

      Packt

      Recommender Systems Complete Course Beginner to Advanced

      Skills you'll gain: Time Series Analysis and Forecasting, Natural Language Processing, Tensorflow, Keras (Neural Network Library), Artificial Neural Networks, Predictive Analytics, Deep Learning, Machine Learning, NumPy, Pandas (Python Package)

      Intermediate · Course · 1 - 4 Weeks

    • G

      Google Cloud

      Website Modernization with Generative AI on Google Cloud

      Skills you'll gain: Generative AI, Web Content, Web Analytics and SEO, AI Personalization, Information Architecture, Google Cloud Platform, Prompt Engineering, Image Analysis, Large Language Modeling, Performance Testing

      Beginner · Course · 1 - 4 Weeks

    • Status: Free
      Free
      N

      Northwestern University

      현대 로봇공학, 강좌 1: 로봇 동작의 기초

      Skills you'll gain: Mathematical Modeling, Engineering, Mechanics, Automation, Virtual Environment, Simulation and Simulation Software, Matlab, Control Systems, Applied Mathematics, Linear Algebra, Computer Programming Tools, Differential Equations, Spatial Data Analysis

      Intermediate · Course · 1 - 3 Months

    • Status: New
      New
      U

      University of Pennsylvania

      Intro to Predictive Analytics Using Python

      Skills you'll gain: Feature Engineering, Unsupervised Learning, Predictive Modeling, Predictive Analytics, Decision Tree Learning, Classification And Regression Tree (CART), Supervised Learning, Forecasting, Random Forest Algorithm, Scikit Learn (Machine Learning Library), Data Analysis, Regression Analysis, Machine Learning, Python Programming

      Beginner · Course · 1 - 4 Weeks

    • Status: Free
      Free
      U

      University of Washington

      머신 러닝: 회귀 모델

      Skills you'll gain: Regression Analysis, Supervised Learning, Predictive Modeling, Statistical Modeling, Statistical Methods, Machine Learning, Scikit Learn (Machine Learning Library), Applied Machine Learning, Feature Engineering, Linear Algebra, NumPy, Algorithms

      Mixed · Course · 1 - 3 Months

    • Status: New
      New
      P

      Politecnico di Milano

      Derivatives: a guide to calculation

      Skills you'll gain: ChatGPT, Calculus, Derivatives, Mathematical Software, AI Personalization, Applied Mathematics, Mathematics Education, Machine Learning Algorithms, Algebra, Geometry, Mathematical Theory & Analysis, Graphing

      Intermediate · Course · 1 - 3 Months

    • D

      Duke University

      Designing Larger Python Programs for Data Science

      Skills you'll gain: Python Programming, Simulations, Debugging, Statistical Methods, Pandas (Python Package), Program Development, Unit Testing, Test Case, Data Science, Development Testing, Software Design, Computational Thinking, Integration Testing, Computer Programming, Data Manipulation, Data Structures, Data Cleansing, Data Analysis, Software Engineering, Systems Integration

      Beginner · Course · 1 - 4 Weeks

    • U

      University of Colorado Boulder

      Preparar Atividades PhET para Educação STEM

      Skills you'll gain: Mathematics Education, Design Reviews, Simulations, Interactive Design, Design Thinking, Experimentation, Scientific Methods, Chemistry, Physics, Graphing, Biology, Algebra

      Intermediate · Course · 1 - 3 Months

    • J

      Johns Hopkins University

      Network Visualization and Intervention

      Skills you'll gain: Data Visualization, Network Analysis, Relational Databases, Data Visualization Software, Data Storytelling, Data Literacy, Data Analysis, Data Manipulation, Persuasive Communication, Innovation

      Intermediate · Course · 1 - 4 Weeks

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    1…515253…55

    In summary, here are 10 of our most popular linear algebra courses

    • Mastering Image Segmentation with PyTorch: Packt
    • Measures of Correlation and Contingency: American Psychological Association
    • Quantitative Research Methods: Tools for Data Analysis: Coursera Instructor Network
    • Recommender Systems Complete Course Beginner to Advanced: Packt
    • Website Modernization with Generative AI on Google Cloud: Google Cloud
    • 현대 로봇공학, 강좌 1: 로봇 동작의 기초: Northwestern University
    • Intro to Predictive Analytics Using Python: University of Pennsylvania
    • 머신 러닝: 회귀 모델: University of Washington
    • Derivatives: a guide to calculation: Politecnico di Milano
    • Designing Larger Python Programs for Data Science: Duke University

    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 Linear Algebra

    Linear algebra courses on Coursera cover a wide array of fundamental and advanced topics, enabling learners to:

    • Understand vectors and matrices and their applications in various fields
    • Learn methods for solving systems of linear equations
    • Explore eigenvalues and eigenvectors and their significance in transformations
    • Apply linear algebra concepts to computer graphics, data analysis, and more
    • Discover how linear algebra supports machine learning and artificial intelligence algorithms‎

    No prior experience in advanced mathematics is required to start with the introductory linear algebra courses available on Coursera. These courses are designed to accommodate learners at different proficiency levels:

    • Beginners can start with basic concepts of vectors and matrix operations.
    • Intermediate learners can delve into more complex subjects such as linear transformations and space.
    • Advanced learners can tackle topics like abstract vector spaces and computational linear algebra.‎

    Coursera offers various credentials through its linear algebra courses, designed to cater to both novices and professionals:

    • Course certificates that validate your knowledge of linear algebra fundamentals
    • Professional certificates that demonstrate your specialized skills in mathematics
    • Specializations with focused expertise in advanced linear algebra topics‎

    Looking to enhance your team's linear algebra skills? Coursera provides tailored enterprise solutions for teams ranging of 5-125 employees. Our offerings include advanced analytics, customized learning paths, and collaborative tools. To explore our linear algebra training options and make a purchase, please visit our Coursera for Teams page.‎

    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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