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    • Logistic Regression
    Related topics:선형 회귀회귀 모델공급망 분석당뇨병듀크 통계학Numpy

    필터링 기준

    "logistic regression"에 대한 85개의 결과

    • Imperial College London

      Imperial College London

      Logistic Regression in R for Public Health

      획득할 기술: Machine Learning, Machine Learning Algorithms, Regression, Statistical Programming, Analysis, Supply Chain, Interpretation, Logistic Regression, General Statistics, Bayesian Statistics, Probability & Statistics

      4.8

      (315개의 검토)

      Intermediate · Course · 1-4 Weeks

    • Coursera Project Network

      Coursera Project Network

      Logistic Regression with NumPy and Python

      획득할 기술: Machine Learning, Supply Chain, Matplotlib, Statistical Programming, Probability & Statistics, Logistic Regression, Computer Programming, Algebra, Analysis, Numpy

      4.5

      (383개의 검토)

      Beginner · Rhyme Project · Less Than 2 Hours

    • SAS

      SAS

      Predictive Modeling with Logistic Regression using SAS

      획득할 기술: Regression, Mathematics, Statistical Programming, General Statistics, Communication, Machine Learning, Probability & Statistics, Marketing, SAS (Software), Computer Programming, Algebra, Advertising, Python Programming

      4.6

      (31개의 검토)

      Intermediate · Course · 1-3 Months

    • Coursera Project Network

      Coursera Project Network

      Logistic Regression&application as Classification Algorithm

      Intermediate · Rhyme Project · Less Than 2 Hours

    • Coursera Project Network

      Coursera Project Network

      Logistic Regression for Classification using Julia

      획득할 기술: Probability & Statistics

      Beginner · Rhyme Project · Less Than 2 Hours

    • Coursera Project Network

      Coursera Project Network

      Logistic Regression with Python and Numpy

      획득할 기술: Project, Machine Learning, Regression, Modeling, Supply Chain, Probability & Statistics, Communication, Statistical Programming, Python Programming, Strategy and Operations, Marketing, Gratitude, Numpy, Logistic Regression, Computer Programming

      4.5

      (144개의 검토)

      Intermediate · Rhyme Project · Less Than 2 Hours

    • Placeholder
      Coursera Project Network

      Coursera Project Network

      Predict Ad Clicks Using Logistic Regression and XG-Boost

      Beginner · Rhyme Project · Less Than 2 Hours

    • Placeholder
      Coursera Project Network

      Coursera Project Network

      Logistic Regression 101: US Household Income Classification

      Beginner · Rhyme Project · Less Than 2 Hours

    • Placeholder
      Duke University

      Duke University

      Introduction to Machine Learning

      획득할 기술: Theoretical Computer Science, Machine Learning Algorithms, Regression, General Statistics, Convolutional Neural Network, Machine Learning, Deep Learning, Language, Supply Chain, Probability & Statistics, Natural Language Processing, Computer Vision, Logistic Regression, Algorithms, Artificial Neural Networks

      4.7

      (3k개의 검토)

      Intermediate · Course · 1-3 Months

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      Coursera Project Network

      Coursera Project Network

      Evaluate Machine Learning Models with Yellowbrick

      획득할 기술: Supply Chain and Logistics, Leadership and Management, Marketing, Change Management, Machine Learning, Regression, Analysis, Statistical Programming, Communication, Supply Chain, Probability & Statistics, Computer Programming, Scikit-Learn, Python Programming, Strategy and Operations, Modeling, Supply Chain Systems, Evaluation, Advertising, Logistic Regression

      4.8

      (49개의 검토)

      Intermediate · Rhyme Project · Less Than 2 Hours

    • Placeholder
      Coursera Project Network

      Coursera Project Network

      Diabetes Prediction With Pyspark MLLIB

      획득할 기술: Cleaning, Statistical Programming, Python Programming, Probability & Statistics, Supply Chain, Data Management, Machine Learning, Computer Programming, Logistic Regression, Extract, Transform, Load

      4.6

      (13개의 검토)

      Intermediate · Rhyme Project · Less Than 2 Hours

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      University of Chicago

      University of Chicago

      Machine Learning for Analytics MasterTrack™ Certificate

      획득할 기술: General Statistics, Experiment, Probability & Statistics, Machine Learning

      학점 제공

      Mastertrack

    logistic regression과(와) 관련된 검색

    logistic regression with numpy and python
    logistic regression for classification using julia
    logistic regression in r for public health
    logistic regression&application as classification algorithm
    logistic regression with python and numpy
    logistic regression 101: us household income classification
    predictive modeling with logistic regression using sas
    predict ad clicks using logistic regression and xg-boost
    1234…8

    요약하자면, 여기에 가장 인기 있는 logistic regression 강좌 10개가 있습니다.

    • Logistic Regression in R for Public Health: Imperial College London
    • Logistic Regression with NumPy and Python: Coursera Project Network
    • Predictive Modeling with Logistic Regression using SAS: SAS
    • Logistic Regression&application as Classification Algorithm: Coursera Project Network
    • Logistic Regression for Classification using Julia: Coursera Project Network
    • Logistic Regression with Python and Numpy: Coursera Project Network
    • Predict Ad Clicks Using Logistic Regression and XG-Boost: Coursera Project Network
    • Logistic Regression 101: US Household Income Classification: Coursera Project Network
    • Introduction to Machine Learning: Duke University
    • Evaluate Machine Learning Models with Yellowbrick: Coursera Project Network

    Probability And Statistics에서 학습할 수 있는 스킬

    R 프로그래밍 (19)
    추정 (16)
    선형 회귀 (12)
    통계 분석 (12)
    통계적 추론 (11)
    회귀 분석 (10)
    생물 통계학 (9)
    베이지안 (7)
    로지스틱 회귀 (7)
    확률 분포 (7)
    베이지안 통계 (6)
    의료 통계학 (6)

    Logistic Regression에 대한 자주 묻는 질문

    • 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.‎

    이 FAQ 콘텐츠는 정보 전달 목적만으로 사용할 수 있습니다. 학습자는 과정 및 기타 학점 정보가 개인적, 직업적 및 재정적 목표에 부합하는지 확인하기 위해 추가 조사를 수행하는 것이 좋습니다.
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