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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

    • E

      Erasmus University Rotterdam

      Necessary Condition Analysis (NCA)

      Skills you'll gain: Data Analysis, Statistical Reporting, Quantitative Research, Statistical Analysis, Statistical Software, Small Data, Qualitative Research, R Programming, Sampling (Statistics), Technical Communication, Research Methodologies, Scatter Plots, Statistical Hypothesis Testing

      4.9
      Rating, 4.9 out of 5 stars
      ·
      28 reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Michigan

      Data Science for Health Research

      Skills you'll gain: Exploratory Data Analysis, Rmarkdown, Statistical Modeling, Tidyverse (R Package), Data Visualization Software, Regression Analysis, R Programming, Statistical Methods, Ggplot2, Data Wrangling, Statistical Inference, Probability & Statistics, Scatter Plots, Data Manipulation, Predictive Analytics, Correlation Analysis, Histogram, Classification And Regression Tree (CART), Data Analysis, Statistical Analysis

      5
      Rating, 5 out of 5 stars
      ·
      9 reviews

      Intermediate · Specialization · 1 - 3 Months

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

      University of Glasgow

      Applied AI for Engineers and Scientists: Foundations

      Skills you'll gain: Artificial Neural Networks, Matlab, Supervised Learning, Applied Machine Learning, Machine Learning, Data Manipulation, Data Cleansing, Artificial Intelligence, Feature Engineering, Data Transformation, Classification And Regression Tree (CART), Predictive Modeling, Numerical Analysis, Artificial Intelligence and Machine Learning (AI/ML), Mathematical Software, Programming Principles, Computer Programming, Scripting, Data Structures, Machine Learning Algorithms

      Beginner · Specialization · 1 - 3 Months

    • Status: Free
      Free
      C

      Coursera Project Network

      PyCaret: Anatomy of Regression

      Skills you'll gain: Regression Analysis, Statistical Modeling, Predictive Modeling, Scikit Learn (Machine Learning Library), Feature Engineering, Data Manipulation, Pandas (Python Package), Machine Learning Methods, Data Visualization, Exploratory Data Analysis, Performance Tuning

      4.5
      Rating, 4.5 out of 5 stars
      ·
      14 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • Status: Free Trial
      Free Trial
      S

      SAS

      Analyzing Time Series and Sequential Data

      Skills you'll gain: Time Series Analysis and Forecasting, SAS (Software), Forecasting, Feature Engineering, Statistical Analysis, Data Analysis, Statistical Methods, Regression Analysis, Data Transformation, Exploratory Data Analysis, Predictive Modeling, Applied Machine Learning, Advanced Analytics, Statistical Modeling, Unsupervised Learning, Bayesian Statistics, Automation, Anomaly Detection, Data Processing, Data Manipulation

      5
      Rating, 5 out of 5 stars
      ·
      10 reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Pennsylvania

      Machine Learning Essentials

      Skills you'll gain: Statistical Machine Learning, Python Programming, Supervised Learning, Machine Learning, Regression Analysis, Statistical Analysis, Classification And Regression Tree (CART), Applied Machine Learning, Statistical Inference, Predictive Modeling, Probability & Statistics

      Intermediate · Course · 1 - 4 Weeks

    • C

      Coursera Project Network

      Decision Tree Classifier for Beginners in R

      Skills you'll gain: Classification And Regression Tree (CART), Decision Tree Learning, Predictive Modeling, Data Manipulation, Statistical Modeling, R Programming, Supervised Learning, Machine Learning Algorithms

      4.8
      Rating, 4.8 out of 5 stars
      ·
      6 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • I

      IBM

      데이터 과학이란 무엇인가?

      Skills you'll gain: Data Storytelling, Data Mining, Data Science, Data-Driven Decision-Making, Digital Transformation, Big Data, Data Analysis, Business Analytics, Regression Analysis, Analytics, Apache Hadoop, Health Informatics

      4.5
      Rating, 4.5 out of 5 stars
      ·
      21 reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      Universidad Austral

      Introducción al Aprendizaje Profundo

      Skills you'll gain: Supervised Learning, Deep Learning, Artificial Neural Networks, Machine Learning, Machine Learning Algorithms, Artificial Intelligence and Machine Learning (AI/ML), Linear Algebra, Regression Analysis

      4.2
      Rating, 4.2 out of 5 stars
      ·
      6 reviews

      Beginner · Course · 1 - 4 Weeks

    • C

      Coursera Instructor Network

      Advanced Quantitative Statistics with Excel

      Skills you'll gain: Regression Analysis, Microsoft Excel, Statistical Hypothesis Testing, Business Analytics, Data-Driven Decision-Making, Excel Formulas, Data Visualization, Business Analysis, Data Presentation, Business Intelligence, Statistical Analysis, Statistical Methods, Analytics, Spreadsheet Software, Data Analysis, Data Analysis Software, Correlation Analysis, Probability & Statistics, Variance Analysis, Forecasting

      4.7
      Rating, 4.7 out of 5 stars
      ·
      9 reviews

      Beginner · Course · 1 - 4 Weeks

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

      University of Colorado Boulder

      Statistics and Data Analysis with R

      Skills you'll gain: Descriptive Statistics, Statistical Hypothesis Testing, Regression Analysis, Probability Distribution, Statistical Analysis, R Programming, Data Import/Export, Probability & Statistics, Statistical Modeling, Statistical Methods, Plot (Graphics), Statistics, Data Manipulation, Predictive Modeling, Data Wrangling, Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      6 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Data Science Decisions in Time

      Skills you'll gain: Precision Medicine, Game Theory, Reinforcement Learning, Data-Driven Decision-Making, Clinical Trials, Bioinformatics, Data Analysis, Image Analysis, Decision Tree Learning, Analytics, Markov Model, Bayesian Statistics, Time Series Analysis and Forecasting, Business Analytics, Data Science, Strategic Decision-Making, Statistical Methods, Anomaly Detection, Cybersecurity, Algorithms

      Intermediate · Specialization · 3 - 6 Months

    Logistic Regression learners also search

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    1…333435…45

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

    • Necessary Condition Analysis (NCA): Erasmus University Rotterdam
    • Data Science for Health Research: University of Michigan
    • Applied AI for Engineers and Scientists: Foundations: University of Glasgow
    • PyCaret: Anatomy of Regression: Coursera Project Network
    • Analyzing Time Series and Sequential Data: SAS
    • Machine Learning Essentials: University of Pennsylvania
    • Decision Tree Classifier for Beginners in R: Coursera Project Network
    • 데이터 과학이란 무엇인가?: IBM
    • Introducción al Aprendizaje Profundo: Universidad Austral
    • Advanced Quantitative Statistics with Excel: Coursera Instructor Network

    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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