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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
      I

      IBM

      AI Workflow: Machine Learning, Visual Recognition and NLP

      Skills you'll gain: Tensorflow, Supervised Learning, Artificial Neural Networks, Regression Analysis, Classification And Regression Tree (CART), Applied Machine Learning, Machine Learning Algorithms, Deep Learning, IBM Cloud, Image Analysis, Machine Learning, Random Forest Algorithm, Artificial Intelligence and Machine Learning (AI/ML), Predictive Modeling, Decision Tree Learning, Natural Language Processing, Feature Engineering, Scikit Learn (Machine Learning Library), Business Metrics, Computer Vision

      4.4
      Rating, 4.4 out of 5 stars
      ·
      86 reviews

      Advanced · Course · 1 - 4 Weeks

    • S

      SAS

      Machine Learning Using SAS Viya

      Skills you'll gain: Supervised Learning, Decision Tree Learning, Applied Machine Learning, Data Processing, Predictive Modeling, Feature Engineering, SAS (Software), Machine Learning, Data Analysis, Artificial Neural Networks, Performance Tuning

      4.7
      Rating, 4.7 out of 5 stars
      ·
      101 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      E

      Edureka

      Machine Learning and NLP Basics

      Skills you'll gain: Natural Language Processing, Deep Learning, Predictive Modeling, Text Mining, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Tensorflow, Applied Machine Learning, Machine Learning Algorithms, Supervised Learning, Artificial Neural Networks, Unstructured Data

      3.4
      Rating, 3.4 out of 5 stars
      ·
      25 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      A

      Arizona State University

      Introduction to Machine Learning with Python

      Skills you'll gain: Supervised Learning, Unsupervised Learning, Generative AI, Deep Learning, Image Analysis, Machine Learning Algorithms, Applied Machine Learning, Python Programming, Machine Learning, Artificial Neural Networks, Computer Vision, Computer Programming, Regression Analysis

      3.7
      Rating, 3.7 out of 5 stars
      ·
      20 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Advanced Probability and Statistical Methods

      Skills you'll gain: Regression Analysis, Statistical Hypothesis Testing, Statistical Analysis, Probability & Statistics, Statistical Methods, Probability Distribution, Data Analysis, Markov Model, Data Science, Statistical Modeling, Statistics, Statistical Inference, Probability, R Programming, Applied Mathematics

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      Q

      Queen Mary University of London

      Analysis and Interpretation of Data

      Skills you'll gain: Data Analysis, Statistical Analysis, Correlation Analysis, Quantitative Research, Regression Analysis, Content Performance Analysis, Marketing Analytics, Probability & Statistics, Trend Analysis, Data Cleansing, Digital Marketing, Exploratory Data Analysis, Market Research, Return On Investment

      4.5
      Rating, 4.5 out of 5 stars
      ·
      11 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      S

      SkillUp EdTech

      ASQ-Certified Six Sigma Black Belt (CSSBB) Exam Prep

      Skills you'll gain: Statistical Process Controls, Statistical Hypothesis Testing, Lean Methodologies, Team Motivation, Process Improvement, Six Sigma Methodology, Kaizen Methodology, Process Capability, Risk Analysis, Team Leadership, Quality Improvement, Team Management, Benchmarking, Motivational Skills, Performance Measurement, Change Management, Process Analysis, Statistical Analysis, Continuous Improvement Process, Organizational Change

      4.8
      Rating, 4.8 out of 5 stars
      ·
      30 reviews

      Intermediate · Specialization · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      L

      LearnQuest

      Introduction to Data Science and scikit-learn in Python

      Skills you'll gain: Feature Engineering, Exploratory Data Analysis, Pandas (Python Package), Scikit Learn (Machine Learning Library), Data Manipulation, NumPy, Data Analysis, Data Processing, Python Programming, Regression Analysis, Predictive Modeling, Machine Learning, Classification And Regression Tree (CART), Statistical Methods, Computer Programming, Data Structures

      3.7
      Rating, 3.7 out of 5 stars
      ·
      49 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Precalculus: Mathematical Modeling

      Skills you'll gain: Data Analysis, Applied Mathematics, Mathematical Modeling, Analytical Skills, Statistics, Systems Of Measurement, Correlation Analysis, Regression Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      89 reviews

      Beginner · Course · 1 - 4 Weeks

    • N

      National Taiwan University

      人工智慧:機器學習與理論基礎 (Artificial Intelligence - Learning & Theory)

      Skills you'll gain: Reinforcement Learning, Deep Learning, Theoretical Computer Science, Artificial Neural Networks, Artificial Intelligence, Classification And Regression Tree (CART), Machine Learning, Supervised Learning, Computer Science, Unsupervised Learning, Algorithms, Network Architecture

      4.6
      Rating, 4.6 out of 5 stars
      ·
      59 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      G

      Google Cloud

      Launching into Machine Learning en Español

      Skills you'll gain: Exploratory Data Analysis, Data Quality, Applied Machine Learning, Machine Learning, Supervised Learning, Machine Learning Algorithms, Google Cloud Platform, Data Analysis, Big Data, Sampling (Statistics), Regression Analysis, Performance Tuning

      4.7
      Rating, 4.7 out of 5 stars
      ·
      131 reviews

      Beginner · Course · 1 - 3 Months

    • H

      H2O.ai

      Practical Machine Learning on H2O

      Skills you'll gain: Unsupervised Learning, Deep Learning, Anomaly Detection, Applied Machine Learning, Machine Learning Algorithms, Machine Learning, Supervised Learning, Decision Tree Learning, Predictive Modeling, Random Forest Algorithm, Data Import/Export, Artificial Neural Networks, Data Modeling, Data Manipulation, Feature Engineering

      4.5
      Rating, 4.5 out of 5 stars
      ·
      74 reviews

      Intermediate · Course · 1 - 3 Months

    Logistic Regression learners also search

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    1…262728…45

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

    • AI Workflow: Machine Learning, Visual Recognition and NLP: IBM
    • Machine Learning Using SAS Viya: SAS
    • Machine Learning and NLP Basics: Edureka
    • Introduction to Machine Learning with Python: Arizona State University
    • Advanced Probability and Statistical Methods: Johns Hopkins University
    • Analysis and Interpretation of Data: Queen Mary University of London
    • ASQ-Certified Six Sigma Black Belt (CSSBB) Exam Prep: SkillUp EdTech
    • Introduction to Data Science and scikit-learn in Python: LearnQuest
    • Precalculus: Mathematical Modeling: Johns Hopkins University
    • 人工智慧:機器學習與理論基礎 (Artificial Intelligence - Learning & Theory): National Taiwan 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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