What is 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.

Why learn logistic regression?

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.

What career opportunities can arise from learning logistic regression?

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.

How can taking online courses help me learn logistic regression?

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.

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Logistic Regression

필터링 기준

"logistic regression"에 대한 87개의 결과

Johns Hopkins University
Data Literacy
획득할 기술: Advertising, Change Management, Communication, Data Analysis, Data Visualization, Entrepreneurship, Feature Engineering, General Statistics, Leadership and Management, Machine Learning, Marketing, Mathematics, Probability & Statistics, Regression, Research and Design, Strategy and Operations, Survey Creation
4.6
(128개의 검토)
Beginner · Specialization · 3+ Months
University of California San Diego
Design Thinking and Predictive Analytics for Data Products
획득할 기술: Python Programming, Regression Analysis, Problem Solving, Big Data, Statistical Programming, General Statistics, Leadership and Management, Supply Chain and Logistics, Mathematics, Entrepreneurship, Regression, Business Analysis, Computer Programming, Data Management, Data Analysis, Analytics, Research and Design, Algebra, Supply Chain Systems, Machine Learning, Analysis, Feature Engineering, Probability & Statistics
4.5
(61개의 검토)
Intermediate · Course · 1-3 Months
IBM
Deep Neural Networks with PyTorch
획득할 기술: Algorithms, General Statistics, Python Programming, Machine Learning Algorithms, Computer Graphic Techniques, Computer Vision, Computer Graphics, Mathematics, Convolutional Neural Network, Econometrics, Artificial Neural Networks, PyTorch, Regression, Computer Programming, Statistical Machine Learning, Probability & Statistics, Theoretical Computer Science, Machine Learning, Deep Learning, Probability Distribution
4.4
(1.1k개의 검토)
Intermediate · Course · 1-3 Months
University of California, Irvine
Predictive Modeling, Model Fitting, and Regression Analysis
획득할 기술: Exploratory Data Analysis, General Statistics, Data Analysis, Supervision, Regression Analysis, Big Data, Algebra, Supply Chain and Logistics, Data Structures, Business Analysis, Algorithms, Regression, Randomness, Data Management, Analysis, Theoretical Computer Science, Data Clustering Algorithms, Supply Chain Systems, Machine Learning, Probability & Statistics
4.3
(36개의 검토)
Intermediate · Course · 1-4 Weeks
University of Illinois at Urbana-Champaign
Data Modeling and Regression Analysis in Business
획득할 기술: Regression Analysis, Data Analysis, R Programming, Marketing, Big Data, Supply Chain and Logistics, Regression, Communication, Business Analysis, Algorithms, General Statistics, Statistical Programming, Data Management, Advertising, Data Visualization, Rstudio, Analysis, Theoretical Computer Science, Analytics, Statistical Analysis, Probability & Statistics
4.3
(52개의 검토)
Intermediate · Course · 1-4 Weeks
무료
Stanford University
Machine Learning
획득할 기술: Algorithms, Computer Vision, Mathematics, Geostatistics, Security Engineering, Supply Chain, Econometrics, Distributed Computing Architecture, General Statistics, Computer Networking, Computer Architecture, Applied Machine Learning, Other Programming Languages, Regression, Computer Programming, Natural Language Processing, Linear Algebra, Data Analysis, Theoretical Computer Science, Calculus, Machine Learning Algorithms, Artificial Neural Networks, Differential Equations, Statistical Machine Learning, Support Vector Machine, Data Analysis Software, Linearity, Dimensionality Reduction, Feature Engineering, Probability Distribution, Deep Learning, Network Security, Estimation, Data Mining, Machine Learning, Probability & Statistics
4.9
(170.1k개의 검토)
Mixed · Course · 3+ Months
Rutgers the State University of New Jersey
Supply Chain Analytics
획득할 기술: Accounting, Analysis, Analytics, Big Data, Budget Management, Business Analysis, Chaining, Data Analysis, Data Management, Demand, Entrepreneurship, Finance, Financial Analysis, Inventory Management, Leadership and Management, Marketing, Operations Management, Probability & Statistics, Regression, Research and Design, Sales, Strategy and Operations, Supply Chain, Supply Chain Systems, Supply Chain and Logistics
4.6
(1.6k개의 검토)
Beginner · Specialization · 3+ Months

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

Data Literacy: Johns Hopkins University
Microsoft Azure Machine Learning: Microsoft
Design Thinking and Predictive Analytics for Data Products: University of California San Diego
Quantifying Relationships with Regression Models: Johns Hopkins University
Introducción a los algoritmos de regresión: Coursera Project Network
Deep Neural Networks with PyTorch: IBM
Inferential Statistics: University of Amsterdam
Using R for Regression and Machine Learning in Investment: Sungkyunkwan University
Predictive Modeling, Model Fitting, and Regression Analysis: University of California, Irvine
Data Modeling and Regression Analysis in Business: University of Illinois at Urbana-Champaign

Probability And Statistics에서 학습할 수 있는 스킬

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.‎