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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
      R

      Rice University

      Business Statistics and Analysis

      Skills you'll gain: Statistical Hypothesis Testing, Microsoft Excel, Pivot Tables And Charts, Regression Analysis, Descriptive Statistics, Probability & Statistics, Graphing, Spreadsheet Software, Probability Distribution, Business Analytics, Statistical Analysis, Statistical Modeling, Excel Formulas, Data Analysis, Data Presentation, Statistics, Business Analysis, Statistical Methods, Sample Size Determination, Statistical Inference

      4.7
      Rating, 4.7 out of 5 stars
      ·
      13K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      AI for Medicine

      Skills you'll gain: Deep Learning, Statistical Analysis, Clinical Trials, Feature Engineering, Risk Modeling, Treatment Planning, Data Analysis, Precision Medicine, Decision Tree Learning, Predictive Modeling, Patient Treatment, Image Analysis, Machine Learning Methods, Applied Machine Learning, AI Personalization, Machine Learning, Random Forest Algorithm, Artificial Intelligence and Machine Learning (AI/ML), Data Processing, Medical Imaging

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2.4K reviews

      Intermediate · Specialization · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      I

      IBM

      Data Science Methodology

      Skills you'll gain: Jupyter, Peer Review, Data Modeling, Data Science, Data Cleansing, Business Analysis, Data Mining, Predictive Modeling, Data Quality, Data Storytelling, Analytical Skills, User Feedback, Decision Tree Learning, Stakeholder Engagement

      4.6
      Rating, 4.6 out of 5 stars
      ·
      21K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      I

      IBM

      Supervised Machine Learning: Regression

      Skills you'll gain: Supervised Learning, Regression Analysis, Predictive Modeling, Machine Learning, Statistical Modeling, Classification And Regression Tree (CART), Scikit Learn (Machine Learning Library), Feature Engineering, Statistical Analysis, Performance Metric

      4.7
      Rating, 4.7 out of 5 stars
      ·
      741 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      Natural Language Processing with Classification and Vector Spaces

      Skills you'll gain: Natural Language Processing, Supervised Learning, Dimensionality Reduction, Feature Engineering, Machine Learning Algorithms, Artificial Intelligence, Tensorflow, Linear Algebra, Probability & Statistics

      4.6
      Rating, 4.6 out of 5 stars
      ·
      4.6K reviews

      Intermediate · Course · 1 - 4 Weeks

    • U

      University of Amsterdam

      Data Analytics for Lean Six Sigma

      Skills you'll gain: Lean Six Sigma, Statistical Hypothesis Testing, Minitab, Regression Analysis, Data Visualization Software, Probability Distribution, Descriptive Statistics, Data Analysis, Statistical Analysis, Box Plots, Analytics, Process Improvement, Correlation Analysis, Variance Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3.4K reviews

      Beginner · Course · 1 - 3 Months

    • E

      Erasmus University Rotterdam

      Econometrics: Methods and Applications

      Skills you'll gain: Econometrics, Time Series Analysis and Forecasting, Regression Analysis, Data Analysis, Statistical Analysis, Quantitative Research, Statistical Modeling, Statistics, Predictive Analytics, Probability, Linear Algebra, Peer Review

      4.6
      Rating, 4.6 out of 5 stars
      ·
      1.2K reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Practical Machine Learning

      Skills you'll gain: Predictive Modeling, Machine Learning Algorithms, Statistical Machine Learning, Feature Engineering, Supervised Learning, Classification And Regression Tree (CART), Applied Machine Learning, Decision Tree Learning, Machine Learning, Random Forest Algorithm, Regression Analysis, Data Processing, Data Collection

      4.5
      Rating, 4.5 out of 5 stars
      ·
      3.3K reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      I

      IBM

      IBM Business Intelligence (BI) Analyst

      Skills you'll gain: Dashboard, Data Storytelling, Data Warehousing, SQL, Database Design, MySQL, Presentations, Descriptive Statistics, Extract, Transform, Load, Business Intelligence, IBM DB2, Tableau Software, Relational Databases, Star Schema, Data Visualization Software, Interactive Data Visualization, Regression Analysis, Data-Driven Decision-Making, Excel Formulas, Microsoft Excel

      4.7
      Rating, 4.7 out of 5 stars
      ·
      14K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Statistical Inference

      Skills you'll gain: Statistical Inference, Statistical Hypothesis Testing, Probability & Statistics, Probability, Bayesian Statistics, Statistical Methods, Statistical Modeling, Statistical Analysis, Probability Distribution, Sampling (Statistics), Sample Size Determination, Data Analysis

      4.2
      Rating, 4.2 out of 5 stars
      ·
      4.4K reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Washington

      Machine Learning

      Skills you'll gain: Regression Analysis, Applied Machine Learning, Feature Engineering, Machine Learning, Image Analysis, Unsupervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Predictive Modeling, Classification And Regression Tree (CART), Supervised Learning, Bayesian Statistics, Statistical Modeling, Artificial Intelligence, Deep Learning, Data Mining, Computer Vision, Statistical Machine Learning, Predictive Analytics, Text Mining, Machine Learning Algorithms

      4.6
      Rating, 4.6 out of 5 stars
      ·
      16K reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Michigan

      Applied Machine Learning in Python

      Skills you'll gain: Feature Engineering, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning, Decision Tree Learning, Unsupervised Learning, Dimensionality Reduction, Random Forest Algorithm

      4.6
      Rating, 4.6 out of 5 stars
      ·
      8.6K reviews

      Intermediate · Course · 1 - 4 Weeks

    Logistic Regression learners also search

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    1…678…45

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

    • Business Statistics and Analysis: Rice University
    • AI for Medicine: DeepLearning.AI
    • Data Science Methodology: IBM
    • Supervised Machine Learning: Regression: IBM
    • Natural Language Processing with Classification and Vector Spaces: DeepLearning.AI
    • Data Analytics for Lean Six Sigma: University of Amsterdam
    • Econometrics: Methods and Applications: Erasmus University Rotterdam
    • Practical Machine Learning: Johns Hopkins University
    • IBM Business Intelligence (BI) Analyst: IBM
    • Statistical Inference: 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 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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