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    • Linear Regression

    Linear Regression Courses Online

    Explore linear regression for statistical modeling. Learn to analyze relationships between variables and make predictions based on data.

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    Explore the Linear Regression Course Catalog

    • Status: Free Trial
      Free Trial
      U

      University of Colorado System

      Nonlinear Kalman Filters (and Parameter Estimation)

      Skills you'll gain: Matlab, Estimation, Numerical Analysis, Performance Tuning, Mathematical Modeling, Control Systems, Simulation and Simulation Software, Derivatives, System Implementation, Calculus, Computer Programming Tools

      Intermediate · Course · 1 - 4 Weeks

    • C

      Coursera Project Network

      Análisis Exploratorio de Datos con Python

      Skills you'll gain: Exploratory Data Analysis, Scatter Plots, Data Analysis, Correlation Analysis, Box Plots, Pandas (Python Package), Data Visualization, Histogram, Statistical Analysis, Python Programming, Software Installation

      Intermediate · Guided Project · Less Than 2 Hours

    • Status: New
      New
      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

    • U

      University of Maryland, College Park

      Cómo manejar datos faltantes

      Skills you'll gain: Sampling (Statistics), Data Quality, Stata, Statistical Analysis, Statistical Software, Statistical Methods, Data Analysis, Regression Analysis, Data Integrity, R Programming

      Mixed · Course · 1 - 3 Months

    • Status: Free
      Free
      C

      Coursera Project Network

      Оптимизация портфеля с помощью модели Марковица

      Skills you'll gain: Financial Analysis, Portfolio Management, Financial Modeling, Correlation Analysis, Financial Market, Investment Management, Variance Analysis, Risk Modeling

      Intermediate · Guided Project · Less Than 2 Hours

    • P

      Packt

      120 Quick Stata Tips

      Skills you'll gain: Stata, Scripting, Graphing, Data Visualization Software, Data Manipulation, Statistical Analysis, Data Analysis, Data Management, Statistical Programming, Data Transformation, Debugging, Data Import/Export, Regression Analysis

      Beginner · Course · 1 - 3 Months

    • S

      Sungkyunkwan University

      Machine Learning for Smart Beta

      Skills you'll gain: Data Processing, Portfolio Management, Investment Management, Classification And Regression Tree (CART), Statistical Machine Learning, Applied Machine Learning, R Programming, Feature Engineering, Machine Learning, Financial Modeling, Predictive Modeling, Decision Tree Learning, Random Forest Algorithm, Supervised Learning

      Intermediate · Course · 1 - 4 Weeks

    • Status: New
      New
      Status: Free Trial
      Free Trial
      E

      Edureka

      Applied Machine Learning with Python

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Applied Machine Learning, Machine Learning Algorithms, Data-Driven Decision-Making, Regression Analysis, Machine Learning, Scikit Learn (Machine Learning Library), Decision Tree Learning, Data Mining, Python Programming, Analytics, Statistical Modeling, Predictive Modeling, Statistical Methods, Classification And Regression Tree (CART), Statistical Analysis, Random Forest Algorithm, Feature Engineering, Dimensionality Reduction

      Intermediate · Course · 1 - 4 Weeks

    • P

      Packt

      Deep Neural Network for Beginners Using Python

      Skills you'll gain: Deep Learning, Data Processing, Artificial Neural Networks, Feature Engineering, Data Cleansing, Python Programming, Network Architecture, Machine Learning Algorithms, NumPy, Regression Analysis, Supervised Learning, Performance Tuning

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Advanced Methods in Machine Learning Applications

      Skills you'll gain: Unsupervised Learning, Applied Machine Learning, Dimensionality Reduction, Reinforcement Learning, Regression Analysis, Machine Learning, Data Mining, Machine Learning Algorithms, Statistical Machine Learning, Advanced Analytics, Predictive Modeling, Random Forest Algorithm, Decision Tree Learning, Supervised Learning

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      P

      Packt

      Foundations of Deep Learning and Neural Networks

      Skills you'll gain: Deep Learning, Artificial Neural Networks, Image Analysis, Keras (Neural Network Library), Tensorflow, Computer Vision, Machine Learning Methods, Network Architecture, Linear Algebra, Probability Distribution

      Intermediate · Course · 1 - 3 Months

    • N

      Northwestern University

      현대 로봇공학, 강좌 1: 로봇 동작의 기초

      Skills you'll gain: Mathematical Modeling, Engineering, Mechanics, Automation, Torque (Physics), Engineering Analysis, Virtual Environment, Simulation and Simulation Software, Engineering Calculations, Matlab, Applied Mathematics, Linear Algebra, Computer Programming Tools

      Intermediate · Course · 1 - 3 Months

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

    • Nonlinear Kalman Filters (and Parameter Estimation): University of Colorado System
    • Análisis Exploratorio de Datos con Python: Coursera Project Network
    • Quantitative Research Methods: Tools for Data Analysis: Coursera Instructor Network
    • Cómo manejar datos faltantes: University of Maryland, College Park
    • Оптимизация портфеля с помощью модели Марковица: Coursera Project Network
    • 120 Quick Stata Tips: Packt
    • Machine Learning for Smart Beta: Sungkyunkwan University
    • Applied Machine Learning with Python: Edureka
    • Deep Neural Network for Beginners Using Python: Packt
    • Advanced Methods in Machine Learning Applications: Johns Hopkins University

    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 Linear Regression

    Linear regression is a type of statistical data analysis that examines which variables help significantly predict the outcome of a situation. You can use linear regression to determine the relationships between one dependent variable and one or more independent variables to sort out which variables will contribute most to the outcome you seek to achieve. Linear regression also helps you forecast the impact that changes to variables will make in different scenarios. It's a tool you can use to help predict outcomes and make adjustments to help achieve the outcome you're looking for.‎

    If you're in a career that relies on data analysis, linear regression is a tool that can help you determine the relationship between variables that affect scenarios that you need to predict or plan for. You can use linear regression to anticipate how the factors that affect a situation now will make a difference in the future. For example, you can use linear regression to predict the cost of a project or the amount of time it will take to complete the project based on different variables. Linear regression can help you make more informed and educated decisions to better navigate the present and plan for the future.‎

    Data analysts use linear regression in different areas of business to determine which variables affect outcomes the most. You can apply linear regression as a data scientist or analyst with a single company or in a consultant role across multiple businesses. You can even set up your own business as a consultant to be in control of your own schedule and career trajectory. Analysts use linear regression in polling and surveys as well as in policy research fields, and various corporations use data analysis to help plan for the future. You can also apply your knowledge of linear regression in higher education and as a professor or research assistant.‎

    You can use online courses to learn linear regression in order to deepen your knowledge and skills in your current job or to find a new career. Whether you're already familiar with linear regression or whether you're brand new to the concept, online courses can give you the knowledge you need to apply linear regression in your workplace. Online courses not only teach you the concepts, but they also provide you with real-life applications of what you're learning. One of the biggest benefits of online learning is that you can sharpen your skills in a way that fits your schedule and lifestyle. You can walk away from your online learning confident in what you've discovered about linear regression and prepared to apply it to your career.‎

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

    When looking to enhance your workforce's skills in Linear Regression, 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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