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    • Logistic Regression

    필터링 기준

    "logistic regression"에 대한 87개의 결과

    • IBM

      IBM

      AI Workflow: Machine Learning, Visual Recognition and NLP

      획득할 기술: Python Programming, Mathematics, Statistical Programming, Natural Language Processing, Computer Programming, Econometrics, Artificial Neural Networks, Probability & Statistics, Data Management, Boosting (Machine Learning), Algebra, Theoretical Computer Science, Machine Learning

      4.4

      (63개의 검토)

      Advanced · Course · 1-4 Weeks

    • CFA Institute

      CFA Institute

      Statistics for Machine Learning for Investment Professionals

      획득할 기술: Machine Learning Algorithms, Python Programming, Statistical Programming, Computer Programming, Regression, General Statistics, Probability & Statistics, Machine Learning

      5.0

      (7개의 검토)

      Beginner · Course · 1-3 Months

    • University of Maryland, College Park

      University of Maryland, College Park

      Combining and Analyzing Complex Data

      획득할 기술: Statistical Programming, Communication, General Statistics, Data Analysis, Probability & Statistics, Software, Data Management, Analysis, Sources

      4.2

      (54개의 검토)

      Mixed · Course · 1-4 Weeks

    • University of Colorado Boulder

      University of Colorado Boulder

      Generalized Linear Models and Nonparametric Regression

      획득할 기술: Machine Learning Algorithms, General Statistics, Regression, Probability & Statistics, Machine Learning

      Intermediate · Course · 1-4 Weeks

    • SAS

      SAS

      Regression Modeling Fundamentals

      획득할 기술: Regression Analysis, Statistical Programming, Marketing, Hypothesis Testing, Experiment, Regression, Modeling, Supply Chain and Logistics, Communication, Advertising, Hypothesis, Algebra, General Statistics, SAS (Software), Mathematics, Machine Learning, Supply Chain, Econometrics, Probability & Statistics, Linearity, Supply Chain Systems, Analysis

      4.8

      (28개의 검토)

      Intermediate · Course · 1-3 Months

    • SAS

      SAS

      Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls

      획득할 기술: Big Data, Data Analysis, Accounting, Business Analysis, Data Management, Financial Analysis

      4.9

      (54개의 검토)

      Beginner · Course · 1-4 Weeks

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      Queen Mary University of London

      Queen Mary University of London

      Analysis and Interpretation of Data

      Beginner · Course · 1-4 Weeks

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      Google Cloud

      Google Cloud

      Machine Learning with Spark on Google Cloud Dataproc

      Intermediate · Project · Less Than 2 Hours

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      Databricks

      Databricks

      Data Science with Databricks for Data Analysts

      획득할 기술: Algebra, Algorithms, Apache, Applied Machine Learning, Big Data, Business Analysis, Computer Programming, Data Analysis, Data Management, Data Mining, Data Structures, Databases, Exploratory Data Analysis, Feature Engineering, General Statistics, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Regression, SQL, Statistical Analysis, Statistical Programming, Supply Chain Systems, Supply Chain and Logistics, Theoretical Computer Science

      4.5

      (365개의 검토)

      Intermediate · Specialization · 3+ Months

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      Microsoft

      Microsoft

      Microsoft Azure AI Fundamentals AI-900 Exam Prep

      획득할 기술: Algorithms, Cloud Computing, Communication, Computer Graphics, Computer Vision, General Statistics, Human Computer Interaction, Interactive Design, Machine Learning, Marketing, Microsoft Azure, Natural Language Processing, Probability & Statistics, Regression, Theoretical Computer Science

      4.7

      (265개의 검토)

      Beginner · Specialization · 3+ Months

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      University of California San Diego

      University of California San Diego

      Python Data Products for Predictive Analytics

      획득할 기술: Algebra, Big Data, Business Analysis, Communication, Computer Programming, Computer Programming Tools, Data Analysis, Data Management, Data Mining, Data Visualization, Entrepreneurship, Feature Engineering, General Statistics, Leadership and Management, Linear Algebra, Machine Learning, Mathematical Optimization, Mathematics, Other Programming Languages, Probability & Statistics, Problem Solving, Python Programming, Regression, Research and Design, Statistical Programming, Supply Chain Systems, Supply Chain and Logistics, Web, Web Development

      4.2

      (222개의 검토)

      Intermediate · Specialization · 3+ Months

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      SAS

      SAS

      SAS Statistical Business Analyst

      획득할 기술: Advertising, Algebra, Analysis, Business Analysis, Communication, Computer Programming, Data Analysis, Econometrics, Experiment, General Statistics, Machine Learning, Marketing, Mathematics, Modeling, Probability & Statistics, Python Programming, Regression, Regression Analysis, SAS (Software), Statistical Analysis, Statistical Programming, Supply Chain, Supply Chain Systems, Supply Chain and Logistics

      4.6

      (86개의 검토)

      Intermediate · Professional Certificate · 3+ Months

    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
    1…456…8

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

    • AI Workflow: Machine Learning, Visual Recognition and NLP: IBM
    • Statistics for Machine Learning for Investment Professionals: CFA Institute
    • Combining and Analyzing Complex Data: University of Maryland, College Park
    • Generalized Linear Models and Nonparametric Regression: University of Colorado Boulder
    • Regression Modeling Fundamentals: SAS
    • Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls: SAS
    • Analysis and Interpretation of Data: Queen Mary University of London
    • Machine Learning with Spark on Google Cloud Dataproc: Google Cloud
    • Data Science with Databricks for Data Analysts: Databricks
    • Microsoft Azure AI Fundamentals AI-900 Exam Prep: Microsoft

    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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