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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: Free Trial
      Free Trial
      K

      Kennesaw State University

      Six Sigma Tools for Improve and Control

      Skills you'll gain: Six Sigma Methodology, Process Improvement, Process Optimization, Correlation Analysis, Statistical Hypothesis Testing, Lean Six Sigma, Kaizen Methodology, Quality Improvement, Regression Analysis, Statistical Process Controls, Continuous Improvement Process, Process Capability, Quality Management, Project Management, Cost Benefit Analysis, Statistical Inference, Document Control

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.3K reviews

      Beginner · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Breast Cancer Prediction Using Machine Learning

      Skills you'll gain: Data Cleansing, Data Processing, Applied Machine Learning, Data Import/Export, Python Programming, Google Cloud Platform, Scikit Learn (Machine Learning Library), Predictive Modeling, Supervised Learning, Machine Learning Algorithms

      4.4
      Rating, 4.4 out of 5 stars
      ·
      45 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • S

      SAS

      Statistics with SAS

      Skills you'll gain: SAS (Software), Statistical Hypothesis Testing, Statistical Software, Statistical Analysis, Predictive Modeling, Statistical Modeling, Statistical Methods, Regression Analysis, Probability & Statistics, Data Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      297 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      W

      Wesleyan University

      Machine Learning for Data Analysis

      Skills you'll gain: Classification And Regression Tree (CART), Decision Tree Learning, Predictive Modeling, Random Forest Algorithm, Applied Machine Learning, Predictive Analytics, Unsupervised Learning, Machine Learning, Supervised Learning, Data Analysis, Data Mining, Feature Engineering, Exploratory Data Analysis, Regression Analysis, Statistical Analysis, Statistical Methods

      4.2
      Rating, 4.2 out of 5 stars
      ·
      324 reviews

      Mixed · Course · 1 - 4 Weeks

    • U

      University of Michigan

      Mindware: Critical Thinking for the Information Age

      Skills you'll gain: Data Analysis, Statistics, Analytical Skills, Experimentation, Critical Thinking, Research Design, Behavioral Economics, Decision Making, Regression Analysis, Probability, Correlation Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.4K reviews

      Beginner · Course · 1 - 3 Months

    • D

      Duke University

      Bayesian Statistics

      Skills you'll gain: Bayesian Statistics, Statistical Hypothesis Testing, Statistical Modeling, Statistical Methods, Statistical Inference, Statistical Analysis, Regression Analysis, Data Analysis, R Programming, Probability, Data-Driven Decision-Making, Probability Distribution

      3.8
      Rating, 3.8 out of 5 stars
      ·
      797 reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University of Pennsylvania

      A Crash Course in Causality: Inferring Causal Effects from Observational Data

      Skills you'll gain: R Programming, Statistical Analysis, Statistical Methods, Statistical Software, Statistical Modeling, Statistical Inference, Data Analysis, Probability & Statistics, Regression Analysis, Research Design, Graph Theory

      4.7
      Rating, 4.7 out of 5 stars
      ·
      564 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Washington

      Practical Predictive Analytics: Models and Methods

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Statistical Machine Learning, Predictive Analytics, Advanced Analytics, Statistical Methods, Decision Tree Learning, Statistical Inference, Statistical Analysis, Machine Learning Algorithms, Machine Learning, Graph Theory, Probability & Statistics, Big Data

      4.1
      Rating, 4.1 out of 5 stars
      ·
      320 reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      Q

      Queen Mary University of London

      Market Research

      Skills you'll gain: Qualitative Research, Proposal Development, Market Research, Research Reports, Data Collection, Research Design, Research Methodologies, Data Analysis, Statistical Hypothesis Testing, Survey Creation, Statistical Analysis, Surveys, Correlation Analysis, Quantitative Research, Research, Science and Research, Market Analysis, Focus Group, Regression Analysis, Content Performance Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      458 reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Modern Regression Analysis in R

      Skills you'll gain: Statistical Inference, Statistical Modeling, Regression Analysis, Data Ethics, Statistical Methods, Statistical Hypothesis Testing, Data Science, R Programming, Data Modeling, Statistical Analysis, Predictive Modeling, Probability & Statistics, Correlation Analysis, Forecasting, Linear Algebra

      Build toward a degree

      4.4
      Rating, 4.4 out of 5 stars
      ·
      30 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      K

      Kennesaw State University

      Six Sigma Advanced Define and Measure Phases

      Skills you'll gain: Process Capability, Team Management, Statistical Process Controls, Exploratory Data Analysis, Six Sigma Methodology, Probability & Statistics, Process Analysis, Statistical Analysis, Lean Six Sigma, Process Mapping, Correlation Analysis, Data Analysis, Data Collection, Regression Analysis, Process Improvement, Quality Improvement, Business Process, Graphing

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.3K reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      N

      New York University

      Machine Learning and Reinforcement Learning in Finance

      Skills you'll gain: Supervised Learning, Reinforcement Learning, Applied Machine Learning, Machine Learning, Statistical Methods, Dimensionality Reduction, Unsupervised Learning, Machine Learning Algorithms, Artificial Neural Networks, Decision Tree Learning, Predictive Modeling, Financial Trading, Financial Market, Derivatives, Scikit Learn (Machine Learning Library), Markov Model, Regression Analysis, Deep Learning, Market Liquidity, Financial Services

      3.7
      Rating, 3.7 out of 5 stars
      ·
      814 reviews

      Intermediate · Specialization · 3 - 6 Months

    Logistic Regression learners also search

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    Statistical Modeling
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    1…101112…45

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

    • Six Sigma Tools for Improve and Control: Kennesaw State University
    • Breast Cancer Prediction Using Machine Learning: Coursera Project Network
    • Statistics with SAS: SAS
    • Machine Learning for Data Analysis: Wesleyan University
    • Mindware: Critical Thinking for the Information Age : University of Michigan
    • Bayesian Statistics: Duke University
    • A Crash Course in Causality: Inferring Causal Effects from Observational Data: University of Pennsylvania
    • Practical Predictive Analytics: Models and Methods: University of Washington
    • Market Research: Queen Mary University of London
    • Modern Regression Analysis in R: University of Colorado Boulder

    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 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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