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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

    • K

      Kennesaw State University

      The Analyze Phase for the 6 σ Black Belt

      Skills you'll gain: Six Sigma Methodology, Lean Six Sigma, Statistical Hypothesis Testing, Statistical Analysis, Quality Improvement, Statistical Inference, Process Analysis, Correlation Analysis, Data Analysis, Probability & Statistics, Risk Analysis, Regression Analysis, Sample Size Determination

      4.4
      Rating, 4.4 out of 5 stars
      ·
      47 reviews

      Mixed · Course · 1 - 3 Months

    • U

      University of Illinois Urbana-Champaign

      Machine Learning for Accounting with Python

      Skills you'll gain: Machine Learning Algorithms, Unsupervised Learning, Scikit Learn (Machine Learning Library), Machine Learning, Text Mining, Applied Machine Learning, Time Series Analysis and Forecasting, Data Processing, Supervised Learning, Predictive Modeling, Python Programming, Regression Analysis, Feature Engineering, Jupyter, Pandas (Python Package), Natural Language Processing, Statistical Analysis, Performance Metric

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      43 reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University of Colorado System

      Regression Modeling for Marketers

      Skills you'll gain: Statistical Software, Marketing Analytics, Regression Analysis, Statistical Modeling, Data Visualization, Statistical Analysis, Market Analysis, Marketing, Predictive Analytics, Marketing Strategies, Predictive Modeling, Customer Insights, Market Dynamics, Statistical Hypothesis Testing, A/B Testing

      Intermediate · Course · 1 - 4 Weeks

    • A

      Alberta Machine Intelligence Institute

      Machine Learning Algorithms: Supervised Learning Tip to Tail

      Skills you'll gain: Supervised Learning, Machine Learning Algorithms, Applied Machine Learning, Jupyter, Machine Learning, Classification And Regression Tree (CART), Scikit Learn (Machine Learning Library), Business Solutions, Python Programming, Regression Analysis, Performance Analysis, Feature Engineering, Data Processing, Performance Metric

      4.7
      Rating, 4.7 out of 5 stars
      ·
      413 reviews

      Mixed · Course · 1 - 4 Weeks

    • U

      UNSW Sydney (The University of New South Wales)

      Requirements Writing

      Skills you'll gain: Concision, Technical Writing, Requirements Management, Requirements Analysis, Business Requirements, Functional Requirement, Writing, User Requirements Documents, System Requirements, Product Requirements, Proofreading, Editing, Grammar, Style Guides, Verification And Validation, Systems Engineering

      4.6
      Rating, 4.6 out of 5 stars
      ·
      342 reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free
      Free
      N

      National Taiwan University

      機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations

      Skills you'll gain: Supervised Learning, Machine Learning, Machine Learning Algorithms, Data Validation, Classification And Regression Tree (CART), Applied Machine Learning, Algorithms, Regression Analysis, Predictive Modeling, Verification And Validation, Feature Engineering, Statistical Methods, Data Transformation

      4.9
      Rating, 4.9 out of 5 stars
      ·
      330 reviews

      Intermediate · Course · 1 - 3 Months

    • C

      Coursera Project Network

      ML Parameters Optimization: GridSearch, Bayesian, Random

      Skills you'll gain: Scikit Learn (Machine Learning Library), Regression Analysis, Performance Tuning, Applied Machine Learning, Machine Learning Methods, Statistical Machine Learning, Bayesian Statistics

      4.9
      Rating, 4.9 out of 5 stars
      ·
      7 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • M

      Microsoft

      Prepare for DP-100: Data Science on Microsoft Azure Exam

      Skills you'll gain: Databricks, Microsoft Azure, Data Science, MLOps (Machine Learning Operations), Predictive Modeling, Scikit Learn (Machine Learning Library), Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Application Deployment, Deep Learning, Tensorflow, Regression Analysis

      4.4
      Rating, 4.4 out of 5 stars
      ·
      59 reviews

      Intermediate · Course · 1 - 3 Months

    • S

      SAS

      SAS Statistical Business Analyst

      Skills you'll gain: SAS (Software), Predictive Modeling, Predictive Analytics, Statistical Hypothesis Testing, Statistical Analysis, Correlation Analysis, Statistical Modeling, Regression Analysis, Exploratory Data Analysis, Statistical Methods, Probability & Statistics, Big Data, Plot (Graphics), Data Analysis, Data Literacy, Data Analysis Software, Advanced Analytics, Statistical Machine Learning, Feature Engineering, Performance Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      218 reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • P

      Packt

      Regression Analysis for Statistics & Machine Learning in R

      Skills you'll gain: Regression Analysis, Data Cleansing, R Programming, Data Manipulation, Statistical Analysis, Classification And Regression Tree (CART), Random Forest Algorithm, Data Transformation, Statistical Modeling, Exploratory Data Analysis, Feature Engineering, Predictive Modeling, Dimensionality Reduction, Machine Learning

      Intermediate · Course · 1 - 3 Months

    • U

      Universidad de los Andes

      Modelos predictivos con aprendizaje automático

      Skills you'll gain: Data Ethics, Predictive Modeling, Classification And Regression Tree (CART), Supervised Learning, Regression Analysis, Applied Machine Learning, Predictive Analytics, Scikit Learn (Machine Learning Library), Machine Learning, Decision Tree Learning, Python Programming, Unsupervised Learning

      4.7
      Rating, 4.7 out of 5 stars
      ·
      73 reviews

      Beginner · Course · 1 - 4 Weeks

    • J

      Johns Hopkins University

      Data Literacy

      Skills you'll gain: Surveys, Survey Creation, Data Literacy, Data Analysis, Peer Review, Research Design, Statistics, Sampling (Statistics), Regression Analysis, Descriptive Statistics, Research, Probability, Quantitative Research, Statistical Hypothesis Testing, Analytics, Probability Distribution, Analysis, Report Writing, Correlation Analysis, Statistical Inference

      4.6
      Rating, 4.6 out of 5 stars
      ·
      247 reviews

      Beginner · Specialization · 3 - 6 Months

    Logistic Regression learners also search

    Regression
    Regression Analysis
    Regression Models
    Linear Regression
    Predictive Modeling
    Statistical Modeling
    Predictive Analytics
    Data Modeling
    1…232425…44

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

    • The Analyze Phase for the 6 σ Black Belt: Kennesaw State University
    • Machine Learning for Accounting with Python: University of Illinois Urbana-Champaign
    • Regression Modeling for Marketers: University of Colorado System
    • Machine Learning Algorithms: Supervised Learning Tip to Tail: Alberta Machine Intelligence Institute
    • Requirements Writing: UNSW Sydney (The University of New South Wales)
    • 機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations: National Taiwan University
    • ML Parameters Optimization: GridSearch, Bayesian, Random: Coursera Project Network
    • Prepare for DP-100: Data Science on Microsoft Azure Exam: Microsoft
    • SAS Statistical Business Analyst: SAS
    • Regression Analysis for Statistics & Machine Learning in R: Packt

    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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