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Barsha Saha
In this project, you will learn about Logistic Regression and its application as Classification Algorithm. The project demonstrates the theoretical background of Logistic Regression using the Sigmoidal function. It also explains the suitability of linear vs logistic regression to answer the specific types of research questions. Finally, it covers an implementation of classification algorithm using logit model. The project utilizes the 'Candy' dataset for illustrative purpose.
Register for this course and see more details by visiting:
OpenCourser.com/course/b6a4tj/logistic
What's inside
Syllabus
Project Overview
Welcome to Logistic Regression & Application as Classification Algorithm. In this R-based project, you will learn about Logistic Regression and its application as Classification Algorithm. The project demonstrates the theoretical background of Logistic Regression using the Sigmoidal function. It also explains the suitability of linear vs logistic regression to answer the specific types of research questions. Finally, it covers an implementation of classification algorithm using logit model. The project utilizes the 'Candy' dataset for illustrative purpose.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills in deploying classification algorithms to solve real-world data science problems
Implements logistic regression models using the glm() function in R
Suitable for individuals with a basic understanding of statistics and probability
Utilized illustrative examples and practical exercises to reinforce concepts
Covers the mathematical underpinnings of logistic regression
Emphasizes the practical application of logistic regression in data science
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Logistic Regression&application as Classification Algorithm with these
activities:
Connect with experts in logistic regression
Show steps
Establishes connections with experienced professionals in the field, providing guidance, support, and industry insights to enhance your learning journey and career prospects.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors through professional organizations or online platforms.
-
Reach out to mentors and express your interest in connecting.
-
Build a mutually beneficial relationship through regular meetings or correspondence.
Review logistic regression basics
Show steps
Refreshes your understanding of the fundamental concepts of logistic regression, preparing you for deeper exploration in this course.
Browse courses on
Logistic Regression
Show steps
-
Revisit the definition and mathematical formulation of logistic regression.
-
Go through examples of binary classification problems that logistic regression can solve.
Gather resources on logistic regression applications
Show steps
Broadens your perspective by showcasing real-world applications of logistic regression, demonstrating its versatility and impact in various domains.
Browse courses on
Case Studies
Show steps
-
Identify industries and research areas where logistic regression is commonly used.
-
Collect case studies and examples that illustrate the successful implementation of logistic regression.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice interpreting logistic regression results
Show steps
Strengthens your ability to interpret the results of logistic regression models, enabling you to extract meaningful insights from your analyses.
Browse courses on
Odds Ratio
Show steps
-
Analyze the output of a logistic regression model, including coefficients, p-values, and odds ratios.
-
Interpret the significance of the coefficients and odds ratios in the context of your research question.
Discuss logistic regression challenges with peers
Show steps
Fosters collaborative learning by engaging with peers to discuss common challenges and explore alternative approaches, enhancing your problem-solving skills.
Browse courses on
Model Limitations
Show steps
-
Join a study group or online forum dedicated to logistic regression.
-
Participate in discussions, share your experiences, and seek advice on overcoming challenges.
-
Collaborate on solving complex logistic regression problems
Explore advanced topics in logistic regression
Show steps
Expands your knowledge beyond the basics, exposing you to advanced techniques and theoretical concepts in logistic regression, enhancing your overall understanding.
Browse courses on
Regularization Techniques
Show steps
-
Investigate regularization techniques such as L1 and L2 regularization.
-
Study different types of logistic regression models, including binomial and multinomial regression.
-
Explore the theoretical underpinnings of logistic regression, such as the maximum likelihood estimation.
Build a logistic regression model from scratch
Show steps
Provides hands-on experience in constructing and evaluating logistic regression models, reinforcing your understanding of the underlying principles.
Browse courses on
Model Building
Show steps
-
Choose a dataset suitable for binary classification.
-
Prepare the data by handling missing values and feature scaling.
-
Develop a logistic regression model using a programming language or statistical software.
-
Evaluate the performance of the model using metrics such as accuracy, precision, and recall.
Attend a workshop on advanced logistic regression techniques
Show steps
Provides an immersive learning experience with hands-on practice and expert insights, enabling you to master advanced logistic regression techniques and stay up-to-date with industry best practices.
Show steps
-
Identify and register for relevant workshops offered by universities, industry leaders, or online platforms.
-
Attend the workshop and actively participate in sessions and discussions.
-
Network with other attendees and experts in the field.
Contribute to open-source logistic regression projects
Show steps
Provides practical experience in applying your logistic regression skills while contributing to the broader machine learning community, fostering a sense of collaboration and real-world impact.
Browse courses on
Software Development
Show steps
-
Identify open-source projects related to logistic regression.
-
Review the project's documentation and contribute bug fixes or feature enhancements.
-
Engage with the project's community through forums or online discussions.
Connect with experts in logistic regression
Show steps
Establishes connections with experienced professionals in the field, providing guidance, support, and industry insights to enhance your learning journey and career prospects.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors through professional organizations or online platforms.
- Reach out to mentors and express your interest in connecting.
- Build a mutually beneficial relationship through regular meetings or correspondence.
Review logistic regression basics
Show steps
Refreshes your understanding of the fundamental concepts of logistic regression, preparing you for deeper exploration in this course.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Revisit the definition and mathematical formulation of logistic regression.
- Go through examples of binary classification problems that logistic regression can solve.
Gather resources on logistic regression applications
Show steps
Broadens your perspective by showcasing real-world applications of logistic regression, demonstrating its versatility and impact in various domains.
Browse courses on
Case Studies
Show steps
Show steps
Show steps
- Identify industries and research areas where logistic regression is commonly used.
- Collect case studies and examples that illustrate the successful implementation of logistic regression.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice interpreting logistic regression results
Show steps
Strengthens your ability to interpret the results of logistic regression models, enabling you to extract meaningful insights from your analyses.
Browse courses on
Odds Ratio
Show steps
Show steps
Show steps
- Analyze the output of a logistic regression model, including coefficients, p-values, and odds ratios.
- Interpret the significance of the coefficients and odds ratios in the context of your research question.
Discuss logistic regression challenges with peers
Show steps
Fosters collaborative learning by engaging with peers to discuss common challenges and explore alternative approaches, enhancing your problem-solving skills.
Browse courses on
Model Limitations
Show steps
Show steps
Show steps
- Join a study group or online forum dedicated to logistic regression.
- Participate in discussions, share your experiences, and seek advice on overcoming challenges.
- Collaborate on solving complex logistic regression problems
Explore advanced topics in logistic regression
Show steps
Expands your knowledge beyond the basics, exposing you to advanced techniques and theoretical concepts in logistic regression, enhancing your overall understanding.
Browse courses on
Regularization Techniques
Show steps
Show steps
Show steps
- Investigate regularization techniques such as L1 and L2 regularization.
- Study different types of logistic regression models, including binomial and multinomial regression.
- Explore the theoretical underpinnings of logistic regression, such as the maximum likelihood estimation.
Build a logistic regression model from scratch
Show steps
Provides hands-on experience in constructing and evaluating logistic regression models, reinforcing your understanding of the underlying principles.
Browse courses on
Model Building
Show steps
Show steps
Show steps
- Choose a dataset suitable for binary classification.
- Prepare the data by handling missing values and feature scaling.
- Develop a logistic regression model using a programming language or statistical software.
- Evaluate the performance of the model using metrics such as accuracy, precision, and recall.
Attend a workshop on advanced logistic regression techniques
Show steps
Provides an immersive learning experience with hands-on practice and expert insights, enabling you to master advanced logistic regression techniques and stay up-to-date with industry best practices.
Show steps
Show steps
Show steps
- Identify and register for relevant workshops offered by universities, industry leaders, or online platforms.
- Attend the workshop and actively participate in sessions and discussions.
- Network with other attendees and experts in the field.
Contribute to open-source logistic regression projects
Show steps
Provides practical experience in applying your logistic regression skills while contributing to the broader machine learning community, fostering a sense of collaboration and real-world impact.
Browse courses on
Software Development
Show steps
Show steps
Show steps
- Identify open-source projects related to logistic regression.
- Review the project's documentation and contribute bug fixes or feature enhancements.
- Engage with the project's community through forums or online discussions.
Career center
Learners who complete Logistic Regression&application as Classification Algorithm will develop knowledge and skills
that may be useful to these careers:
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze data and make predictions for a wide range of financial applications. Logistic Regression is a valuable tool in quantitative finance, used in areas such as credit scoring, risk modeling, and financial forecasting. A course on Logistic Regression can provide Quantitative Analysts with a strong foundation in this technique, enabling them to build and evaluate models more effectively.
Data Scientist
Data Scientist
Data Scientists use a variety of techniques, including statistical modeling, machine learning, and data analysis, to extract insights from data. Logistic Regression is a foundational statistical technique used in data science for modeling and predicting binary outcomes. A course on Logistic Regression can provide Data Scientists with a solid understanding of this technique, enabling them to develop more accurate and effective predictive models.
Risk Analyst
Risk Analyst
Risk Analysts assess and quantify risks for various types of organizations. Logistic Regression is commonly used in risk analysis to model and predict the likelihood of events such as loan defaults or insurance claims. A course on Logistic Regression can help Risk Analysts gain specialized knowledge in this technique, allowing them to more effectively identify, manage, and mitigate potential risks.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve complex problems. Logistic Regression is a widely used machine learning algorithm for binary classification tasks. A course on Logistic Regression can provide Machine Learning Engineers with a strong foundation in this algorithm, allowing them to build and evaluate models more effectively.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and determinants of health-related states and events in populations. Logistic Regression is a key statistical technique used in epidemiology to model and predict the risk of disease outcomes. A course on Logistic Regression can provide Epidemiologists with specialized knowledge in this technique, allowing them to more effectively analyze epidemiological data and contribute to the advancement of public health.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze biological and medical data. Logistic Regression is a key statistical technique used in biostatistics to model and predict the probability of disease outcomes. A course on Logistic Regression can provide Biostatisticians with specialized knowledge in this technique, allowing them to effectively analyze medical data and contribute to the advancement of healthcare.
Statistical Analyst
Statistical Analyst
Statistical Analysts apply statistical techniques to analyze data and derive meaningful insights for various industries. Logistic Regression is a commonly used statistical technique for modeling and predicting binary outcomes. A course on Logistic Regression can provide Statistical Analysts with a deeper understanding of this technique, enhancing their ability to extract valuable information from data.
Educational Researcher
Educational Researcher
Educational Researchers use statistical methods to evaluate the effectiveness of educational programs and interventions. Logistic Regression is a commonly used statistical technique for modeling and predicting student outcomes. A course on Logistic Regression can provide Educational Researchers with specialized knowledge in this technique, allowing them to more effectively analyze educational data and contribute to improvements in education.
Statistician
Statistician
Statisticians apply statistical techniques to analyze data and derive meaningful insights. Logistic Regression is a commonly used statistical technique for modeling and predicting binary outcomes. A course on Logistic Regression can provide Statisticians with a deeper understanding of this technique, enhancing their ability to develop and interpret statistical models.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage risks in various industries. Logistic Regression is a valuable tool in actuarial science, used in areas such as insurance pricing, claims modeling, and risk management. A course on Logistic Regression can provide Actuaries with specialized knowledge in this technique, enabling them to more effectively assess and mitigate risks.
Market Researcher
Market Researcher
Market Researchers analyze market trends, customer behavior, and industry data to inform marketing strategies. Logistic Regression is a key statistical technique used in market research to model and predict customer behavior. A course on Logistic Regression can provide Market Researchers with a deeper understanding of how to use this technique to enhance their research accuracy and derive meaningful insights for decision-making.
Financial Analyst
Financial Analyst
Financial Analysts use financial data and analytical techniques to evaluate investment opportunities. Logistic Regression is a valuable tool in financial analysis, used in areas such as credit scoring, risk assessment, and portfolio optimization. A course on Logistic Regression can provide Financial Analysts with a deeper understanding of this technique, enabling them to make more informed investment decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to optimize complex systems and solve business problems. Logistic Regression is a valuable tool in operations research, used in areas such as resource allocation, demand forecasting, and risk analysis. A course on Logistic Regression can provide Operations Research Analysts with a deeper understanding of this technique, enabling them to develop more efficient and effective solutions.
Business Analyst
Business Analyst
Business Analysts use data and analytical techniques to identify and solve business problems. Logistic Regression is a valuable tool in business analysis, used in areas such as customer segmentation, churn prediction, and risk assessment. A course on Logistic Regression can provide Business Analysts with a deeper understanding of this technique, enabling them to develop more effective data-driven solutions.
Data Analyst
Data Analyst
Data Analysts use various statistical techniques to analyze data and derive meaningful insights. A course on Logistic Regression can help Data Analysts build a foundation in a widely used technique for predicting binary outcomes. By understanding the mathematics behind Logistic Regression, Data Analysts can enhance their ability to identify trends, patterns, and relationships in data, thereby improving their overall analytical capabilities.
For more career information including salaries, visit:
OpenCourser.com/course/b6a4tj/logistic
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Logistic Regression&application as Classification Algorithm.
Has detailed exploration of logistic regression and other generalized linear models.
Save
A great way to get a broad overview of logistic regression. It is written in a typically academic textbook style.
Standard textbook that provides a comprehensive overview of the topic.
A helpful book for those with some background in statistics and/or advanced mathematics.
Would provide some prerequisite knowledge in other statistical concepts that may not be covered in the course.
Covers logistic regression and other advanced statistical models.
May be of value if you want more in-depth knowledge of machine learning.
Would be helpful if you are not familiar with programming and machine learning.
Would broaden your knowledge of statistics beyond the scope of the course.
Presents more contemporary statistical concepts that may be valuable.
May be helpful if you are interested in pursuing machine learning in R.
Would be a good supplemental reference.
May be of value if you want to learn Python for machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/b6a4tj/logistic
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Barsha Saha
Language
English
Good to know
Develops skills in deploying classification algorithms to solve real-world data science problems
Implements logistic regression models using the glm() function in R
Suitable for individuals with a basic understanding of statistics and probability
Utilized illustrative examples and practical exercises to reinforce concepts
Covers the mathematical underpinnings of logistic regression
Emphasizes the practical application of logistic regression in data science
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