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    • Logistic Regression

    Logistic Regression Courses Online

    Study logistic regression for binary classification. Learn to model and predict binary outcomes using logistic regression techniques.

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    Explore the Logistic Regression Course Catalog

    • Status: New
      New
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      E

      EDUCBA

      Logistic Regression Fundamentals: Analyze & Predict

      Skills you'll gain: SAS (Software), Predictive Modeling, Regression Analysis, Analytics, Statistical Methods, Data Transformation, Statistical Analysis, Business Analytics, Logistics, Estimation, Probability

      Mixed · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      I

      Imperial College London

      Logistic Regression in R for Public Health

      Skills you'll gain: Descriptive Statistics, Exploratory Data Analysis, Regression Analysis, Statistics, R Programming, Predictive Modeling, Statistical Analysis, Feature Engineering, Probability & Statistics, Data Wrangling, Statistical Inference, R (Software), Data Cleansing, Public Health

      4.8
      Rating, 4.8 out of 5 stars
      ·
      366 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Michigan

      Logistic Regression and Prediction for Health Data

      Skills you'll gain: Statistical Inference, Predictive Analytics, R Programming, Probability & Statistics, Classification And Regression Tree (CART), Statistical Modeling, Statistical Methods, Biostatistics, Regression Analysis, Statistical Analysis, Statistics, Statistical Hypothesis Testing, Data Analysis, Risk Analysis, Descriptive Statistics

      Intermediate · Course · 1 - 4 Weeks

    • C

      Coursera Project Network

      Logistic Regression with NumPy and Python

      Skills you'll gain: Matplotlib, Data Visualization, Seaborn, Exploratory Data Analysis, NumPy, Data Analysis, Jupyter, Data Science, Machine Learning, Python Programming, Supervised Learning, Algorithms

      4.5
      Rating, 4.5 out of 5 stars
      ·
      394 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • Status: Free Trial
      Free Trial
      S

      SAS

      Predictive Modeling with Logistic Regression using SAS

      Skills you'll gain: SAS (Software), Predictive Modeling, Statistical Modeling, Big Data, Regression Analysis, Data Analysis Software, Advanced Analytics, Feature Engineering, Performance Analysis, Performance Measurement, Statistical Analysis, Data Manipulation, Data Cleansing

      4.6
      Rating, 4.6 out of 5 stars
      ·
      63 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Regression Models

      Skills you'll gain: Regression Analysis, Statistical Analysis, Statistical Modeling, Correlation Analysis, Data Analysis, Statistical Methods, Predictive Modeling, Probability & Statistics, Statistical Inference

      4.4
      Rating, 4.4 out of 5 stars
      ·
      3.4K reviews

      Mixed · Course · 1 - 4 Weeks

    What brings you to Coursera today?

    • Status: Free Trial
      Free Trial
      D
      S

      Multiple educators

      Machine Learning

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

      4.9
      Rating, 4.9 out of 5 stars
      ·
      36K reviews

      Beginner · Specialization · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      G

      Google

      Regression Analysis: Simplify Complex Data Relationships

      Skills you'll gain: Regression Analysis, Statistical Hypothesis Testing, Data Analysis, Advanced Analytics, Statistical Analysis, Correlation Analysis, Analytical Skills, Business Analytics, Supervised Learning, Statistical Modeling, Scikit Learn (Machine Learning Library), Variance Analysis, Predictive Modeling, Probability & Statistics, Exploratory Data Analysis, Machine Learning, Python Programming

      4.7
      Rating, 4.7 out of 5 stars
      ·
      560 reviews

      Advanced · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      I

      IBM

      Machine Learning with Python

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Feature Engineering, Applied Machine Learning, Scikit Learn (Machine Learning Library), Machine Learning, Predictive Modeling, Dimensionality Reduction, Regression Analysis, Decision Tree Learning, Classification And Regression Tree (CART), Statistical Modeling

      4.7
      Rating, 4.7 out of 5 stars
      ·
      18K reviews

      Intermediate · Course · 1 - 3 Months

    • Status: New
      New
      Status: Free Trial
      Free Trial
      U

      University of Pittsburgh

      Probability Theory and Regression for Predictive Analytics

      Skills you'll gain: Probability Distribution, Data Science, Probability & Statistics, Predictive Analytics, Probability, Statistical Modeling, Data Analysis, Regression Analysis, Statistical Analysis, Statistical Methods, Statistical Machine Learning, Bayesian Statistics, Statistical Inference, Feature Engineering, Applied Mathematics, Python Programming, Machine Learning, Algorithms

      Build toward a degree

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      Supervised Machine Learning: Regression and Classification

      Skills you'll gain: Supervised Learning, Applied Machine Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, NumPy, Predictive Modeling, Feature Engineering, Artificial Intelligence, Classification And Regression Tree (CART), Python Programming, Regression Analysis, Statistical Modeling, Data Transformation

      4.9
      Rating, 4.9 out of 5 stars
      ·
      30K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Multiple Regression Analysis in Public Health

      Skills you'll gain: Biostatistics, Regression Analysis, Statistical Methods, Public Health, Probability & Statistics, Statistical Analysis, Advanced Analytics, Statistical Modeling, Predictive Modeling, Data Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      315 reviews

      Beginner · Course · 1 - 4 Weeks

    Logistic Regression learners also search

    Regression
    Regression Analysis
    Regression Models
    Linear Regression
    Predictive Modeling
    Statistical Modeling
    Predictive Analytics
    Data Modeling
    1234…48

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

    • Logistic Regression Fundamentals: Analyze & Predict: EDUCBA
    • Logistic Regression in R for Public Health: Imperial College London
    • Logistic Regression and Prediction for Health Data: University of Michigan
    • Logistic Regression with NumPy and Python: Coursera Project Network
    • Predictive Modeling with Logistic Regression using SAS : SAS
    • Regression Models: Johns Hopkins University
    • Machine Learning: DeepLearning.AI
    • Regression Analysis: Simplify Complex Data Relationships: Google
    • Machine Learning with Python: IBM
    • Probability Theory and Regression for Predictive Analytics: University of Pittsburgh

    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)
    Probability Distribution (7)
    Bayesian Statistics (6)
    Medical Statistics (6)

    Frequently Asked Questions about Logistic Regression

    Logistic regression is a technique used in statistics that allows people to estimate the probability of something happening based on existing data they have about that event taking place before. Mathematical models are used often in science and engineering disciplines to explain concepts using mathematical language, and one of these models is logical regression. Logistic regression works using binary data, meaning there are only two possible outcomes for the event: It takes place, or it doesn’t take place. To figure out the probability of these two outcomes, logistic regression uses equations that calculate odds ratios — the odds that something will happen or it won’t. This predictive modeling tool plays a large role not only in statistics but also in machine learning, which involves computers learning information that they haven’t explicitly been programmed to process.‎

    If you’re considering going into a career field that works with data, software or mathematics, logical regression is a valuable area of study to focus on. Logistic regression becomes an important step of the programming process when you’re building software that deals with predictive modeling or data analysis. And, if you’re interested in enhancing your understanding of machine learning, logistic regression is an essential. When you understand modeling with logical regression, you can progress more easily to the complex models involved with machine learning while learning how to best prepare data for processing.‎

    A career as a data scientist or data analyst gives you the opportunity to apply your knowledge of logistic regression, but you’ll also frequently draw upon your skills in this arena if you want to go into the field of machine learning. Although these careers are relatively broad, working with machine learning and logistic regression is also possible in a variety of specialties you’ll find in software engineering, computational linguistics and software development. As you begin to learn more about logistic regression while taking online classes, you may discover a particular area of interest you want to explore — and your new skills can help you discover more.‎

    Taking online courses about logistic regression can give you the knowledge you need to progress in your field or start fresh. In your career as a data scientist or analyst, you know the importance of statistical approaches and the variety of data-modeling techniques you utilize on a regular basis. But if you’re ready to dig deeper into these concepts to boost your understanding and put new ideas and skills into practice, taking online courses about logistic regression can get you where you want to go. If you’re starting with the basics, take a ground-up approach with introductory courses that create a solid foundation for future learning. Or, if you’re looking to supplement your existing knowledge base with a greater understanding of logistic regression, try courses that help you learn the concept’s role in machine learning and programming software for predictive modeling. You’ll appreciate your newfound comprehension of these innovative ideas — and you’ll love the freedom to participate in online courses when and where it’s most convenient for you.‎

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

    When looking to enhance your workforce's skills in Logistic 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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