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Ryan Ahmed
In this hands-on guided project, we will train regression models to find the probability of a student getting accepted into a particular university based on their profile. This project could be practically used to get the university acceptance rate for individual students using web application.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/1fggd9/university
What's inside
Syllabus
University Admission Prediction Using Multiple Linear Regression
In this hands-on project, we will train regression models to find the probability of a student getting accepted into a particular university based on their profile. This project could be practically used to get the university acceptance rate for individual students using web application. In this hands-on project we will go through the following tasks: (1) Understand the Problem Statement, (2) Import libraries and datasets, (3) Perform Exploratory Data Analysis, (4) Perform Data Visualization, (5) Create Training and Testing Datasets, (6) Train and Evaluate a Linear Regression Model, (7) Train and Evaluate an Artificial Neural Network Model, (8) Train and Evaluate a Random Forest Regressor and Decision Tree Model, (9) Understand the various regression KPIs, (10) Calculate and Print Regression model KPIs.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines data visualization, which is standard in industry
Taught by Ahmed, who are recognized for their work in data visualization
Involves hands-on labs and interactive materials
Builds a strong foundation for beginners
Develops professional skills in data visualization
Course explicitly requires that this course be taken in serial with others as part of a series
Save this course
Save University Admission Prediction Using Multiple Linear Regression to your list so you can find it easily later:
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Reviews summary
Highly-rated course for regression modeling
Learners say this university admissions prediction course using multiple linear regression is very insightful and well received. It is a great option for beginners and students who need to learn more about the topic. They also appreciate the instructor's explanations and guidance, saying they make the material easy to understand.
Content is clear and easy to follow
"Very Insightful Project"
"make you understand the code completely"
"la explicación justa para entender que esta haciendo"
Course is accessible for those new to the topic
"Excellent for beginners."
"It is useful for absolute beginners."
"A nice project which gives a good idea on all the topics"
Instructor is knowledgeable and helpful
"brilliant course"
"excellent instructor"
"the teacher is far better than other Coursera guided projects"
Coding text is too small in the online system
"The coding text is too small in the online coding system."
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 University Admission Prediction Using Multiple Linear Regression with these
activities:
Organize Course Resources
Show steps
Establish a structured system to organize notes, assignments, and materials from the course, ensuring easy access and efficient review when needed.
Show steps
-
Create folders or notebooks for different sections of the course.
-
File notes, assignments, and other materials in the appropriate folders.
-
Use a color-coding system or tags to categorize the materials.
Recall Regression Analysis
Show steps
Start by refreshing your pre-existing Regression Analysis knowledge to ensure you are all set for this upcoming course.
Browse courses on
Regression Analysis
Show steps
-
Review class notes or textbooks on Regression Analysis.
-
Look up and review online resources or tutorials on Regression Analysis.
-
Participate in online forums or discussions on Regression Analysis.
Practice Regression Calculations
Show steps
Sharpen your computational skills in regression analysis to enhance your understanding of the underlying concepts and improve your ability to solve problems in this domain.
Browse courses on
Regression Analysis
Show steps
-
Find practice problems on regression calculations from textbooks or online resources.
-
Solve the problems and check your answers.
-
Repeat the process to reinforce your understanding.
-
Focus on understanding the concepts behind the calculations.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Collaborative Learning Groups
Show steps
Engage in regular discussions with peers to reinforce your understanding of course concepts, share insights, and support each other's learning journey.
Show steps
-
Join or form a study group with classmates.
-
Meet regularly to discuss course materials.
-
Collaborate on assignments and projects.
Explore Machine Learning Algorithms
Show steps
Take your learning beyond the classroom by exploring additional tutorials and resources to gain a deeper understanding of the machine learning algorithms used in university admission prediction.
Browse courses on
Machine Learning
Show steps
-
Search for online tutorials on machine learning algorithms, such as linear regression, logistic regression, and decision trees.
-
Follow the tutorials and implement the algorithms using your preferred programming language.
-
Experiment with different algorithms and datasets to understand their strengths and weaknesses.
Predict Student Admission Probabilities
Show steps
This project will allow you to implement and test various machine learning techniques covered in the course, such as linear and logistic regression, to make predictions on student admission probabilities.
Show steps
-
Gather necessary data and pre-process it.
-
Create a training and testing dataset for your models.
-
Build and train machine learning models for classification.
-
Evaluate the performance of your models.
-
Submit your project and present your findings.
Develop an Infographic on Regression Analysis
Show steps
Create a visually engaging infographic that summarizes key concepts and applications of regression analysis to solidify your understanding and make the information accessible to others.
Browse courses on
Regression Analysis
Show steps
-
Gather relevant information on regression analysis.
-
Design an infographic that effectively conveys the information.
-
Use visuals, charts, and graphs to illustrate the concepts.
-
Share your infographic with classmates or online communities.
Design a University Admission Web Application
Show steps
This project will challenge you to combine your knowledge of university admission prediction models with your web development skills to create a tangible application that addresses a real-world problem.
Browse courses on
Machine Learning
Show steps
-
Plan the architecture and design of the web application.
-
Build the front-end and back-end components of the application.
-
Integrate the machine learning models into the application.
-
Deploy the application on a web server.
-
Write documentation and instructions to guide users.
Organize Course Resources
Show steps
Establish a structured system to organize notes, assignments, and materials from the course, ensuring easy access and efficient review when needed.
Show steps
Show steps
Show steps
- Create folders or notebooks for different sections of the course.
- File notes, assignments, and other materials in the appropriate folders.
- Use a color-coding system or tags to categorize the materials.
Recall Regression Analysis
Show steps
Start by refreshing your pre-existing Regression Analysis knowledge to ensure you are all set for this upcoming course.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Review class notes or textbooks on Regression Analysis.
- Look up and review online resources or tutorials on Regression Analysis.
- Participate in online forums or discussions on Regression Analysis.
Practice Regression Calculations
Show steps
Sharpen your computational skills in regression analysis to enhance your understanding of the underlying concepts and improve your ability to solve problems in this domain.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Find practice problems on regression calculations from textbooks or online resources.
- Solve the problems and check your answers.
- Repeat the process to reinforce your understanding.
- Focus on understanding the concepts behind the calculations.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Collaborative Learning Groups
Show steps
Engage in regular discussions with peers to reinforce your understanding of course concepts, share insights, and support each other's learning journey.
Show steps
Show steps
Show steps
- Join or form a study group with classmates.
- Meet regularly to discuss course materials.
- Collaborate on assignments and projects.
Explore Machine Learning Algorithms
Show steps
Take your learning beyond the classroom by exploring additional tutorials and resources to gain a deeper understanding of the machine learning algorithms used in university admission prediction.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Search for online tutorials on machine learning algorithms, such as linear regression, logistic regression, and decision trees.
- Follow the tutorials and implement the algorithms using your preferred programming language.
- Experiment with different algorithms and datasets to understand their strengths and weaknesses.
Predict Student Admission Probabilities
Show steps
This project will allow you to implement and test various machine learning techniques covered in the course, such as linear and logistic regression, to make predictions on student admission probabilities.
Show steps
Show steps
Show steps
- Gather necessary data and pre-process it.
- Create a training and testing dataset for your models.
- Build and train machine learning models for classification.
- Evaluate the performance of your models.
- Submit your project and present your findings.
Develop an Infographic on Regression Analysis
Show steps
Create a visually engaging infographic that summarizes key concepts and applications of regression analysis to solidify your understanding and make the information accessible to others.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Gather relevant information on regression analysis.
- Design an infographic that effectively conveys the information.
- Use visuals, charts, and graphs to illustrate the concepts.
- Share your infographic with classmates or online communities.
Design a University Admission Web Application
Show steps
This project will challenge you to combine your knowledge of university admission prediction models with your web development skills to create a tangible application that addresses a real-world problem.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Plan the architecture and design of the web application.
- Build the front-end and back-end components of the application.
- Integrate the machine learning models into the application.
- Deploy the application on a web server.
- Write documentation and instructions to guide users.
Career center
Learners who complete University Admission Prediction Using Multiple Linear Regression will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data. They use their findings to help businesses understand their customers, improve their products and services, and make better decisions. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Data Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Data Analyst.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. These models can help businesses automate tasks, improve decision-making, and gain a competitive advantage. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Machine Learning Engineer. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Machine Learning Engineer.
Data Scientist
Data Scientist
Data Scientists build models that can make predictions about the future. These models can help businesses make better decisions and improve their bottom line. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Data Scientist. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Data Scientist.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use their findings to help businesses and organizations make better decisions. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Statistician. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Statistician.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment recommendations. They analyze financial data to identify undervalued stocks and bonds. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Financial Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Financial Analyst.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. They develop strategies to mitigate risks and protect organizations from financial losses. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Risk Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Risk Analyst.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand customer behavior and develop marketing campaigns. They track the results of marketing campaigns and make recommendations for improvements. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Marketing Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Marketing Analyst.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products and services. They work with engineers, designers, and marketers to ensure that products meet the needs of customers. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Product Manager. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Product Manager.
Actuary
Actuary
Actuaries use data to assess risk and develop insurance policies. They analyze financial data to determine the likelihood of events such as death, disability, and property damage. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Actuary. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as an Actuary.
Business Analyst
Business Analyst
Business Analysts use data and analysis to help businesses improve their performance. They identify problems, develop solutions, and track results. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Business Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Business Analyst.
Systems Analyst
Systems Analyst
Systems Analysts design, develop, and maintain computer systems. They work with users to understand their needs and develop systems that meet those needs. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Systems Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Systems Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency of business operations. They develop mathematical models to optimize processes and reduce costs. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Operations Research Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as an Operations Research Analyst.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with users to understand their needs and develop software solutions that meet those needs. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Software Engineer. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Software Engineer.
Database Administrator
Database Administrator
Database Administrators design, develop, and maintain databases. They work with users to understand their needs and develop databases that meet those needs. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Database Administrator. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Database Administrator.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data. They use charts, graphs, and other visual aids to help people understand data and make better decisions. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Data Visualization Specialist. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Data Visualization Specialist.
For more career information including salaries, visit:
OpenCourser.com/course/1fggd9/university
Reading list
We've selected 14 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
University Admission Prediction Using Multiple Linear Regression.
Provides a comprehensive introduction to statistical learning methods, including regression, classification, and clustering. It valuable resource for students and practitioners who want to learn about the latest advances in statistical learning.
Provides a comprehensive overview of causal inference, a branch of statistics that deals with the problem of inferring causal relationships from observational data. It valuable resource for students and practitioners who want to learn about the latest advances in causal inference.
Provides a comprehensive overview of statistics, a branch of mathematics that deals with the collection, analysis, interpretation, and presentation of data. It valuable resource for students and practitioners who want to learn about the latest advances in statistics.
Provides a comprehensive overview of statistical learning, a branch of statistics that deals with the problem of learning from data. It valuable resource for students and practitioners who want to learn about the latest advances in statistical learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for students and practitioners who want to learn about the latest advances in machine learning.
Provides a Bayesian approach to statistical modeling, which powerful and flexible approach to modeling data. It valuable resource for students and practitioners who want to learn about the latest advances in Bayesian modeling.
Save
Provides a comprehensive overview of time series analysis and forecasting, a branch of statistics that deals with the problem of analyzing and forecasting time series data. It valuable resource for students and practitioners who want to learn about the latest advances in time series analysis and forecasting.
Save
Provides a comprehensive overview of deep learning, a subfield of machine learning that has been responsible for significant advances in fields such as computer vision, natural language processing, and speech recognition. It valuable resource for students and practitioners who want to learn about the latest advances in deep learning.
Provides a comprehensive overview of machine learning using Python, with a focus on the practical aspects of building and deploying machine learning models. It valuable resource for students and practitioners who want to learn about the latest advances in machine learning in Python.
Save
Provides a comprehensive overview of econometrics, a branch of economics that uses statistical methods to analyze economic data. It valuable resource for students and practitioners who want to learn about the latest advances in econometrics.
Save
Provides a comprehensive overview of forecasting, a branch of statistics that deals with the problem of predicting future events. It valuable resource for students and practitioners who want to learn about the latest advances in forecasting.
Provides a comprehensive overview of probability, a branch of mathematics that deals with the study of uncertainty. It valuable resource for students and practitioners who want to learn about the latest advances in probability.
Provides a practical introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for students and practitioners who want to learn how to build and deploy machine learning models in Python.
Provides a comprehensive overview of machine learning using R, a popular programming language for data analysis and visualization. It valuable resource for students and practitioners who want to learn about the latest advances in machine learning in R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1fggd9/university
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Examines data visualization, which is standard in industry
Taught by Ahmed, who are recognized for their work in data visualization
Involves hands-on labs and interactive materials
Builds a strong foundation for beginners
Develops professional skills in data visualization
Course explicitly requires that this course be taken in serial with others as part of a series
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