We may earn an affiliate commission when you visit our partners.
Ronald Guymon
and
Gies College of Business, University of Illinois
One of the most exciting aspects of business analytics is finding patterns in the data using machine learning algorithms. In this course you will gain a conceptual foundation for why machine learning algorithms are so important and how the resulting models from those algorithms are used to find actionable insight related to business problems. Some algorithms are used for predicting numeric outcomes, while others are used for predicting the classification of an outcome. Other algorithms are used for creating meaningful groups from a rich set of data. Upon completion of this course, you will be able to describe when each algorithm should be used. You will also be given the opportunity to use R and RStudio to run these algorithms and communicate the results using R notebooks.
Register for this course and see more details by visiting:
OpenCourser.com/course/7m3kww/machine
Two deals to help you save
Save money when you learn. Here are two deals and offers that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until July 14
Coursera Plus Annual
Work toward what's next for your career with access to 10,000+ programs. Discover emerging skills and save.
Up to
40%
off
Get offer
Valid until August 30
Google AI App Builder
Learn how to use Gemini API and API Studio with a three-course series from Google DeepMind
Take
100%
off
Get offer
What's inside
Syllabus
Course Orientation and Module 1: Regression Algorithm for Testing and Predicting Business Data
Exploratory data analysis (EDA) is a critical step in the business analytic workflow; however, EDA is a time-consuming approach for uncovering complex relationships. Moreover, the visualizations that are often used for EDA do not lend themselves well for quantifying confidence in results or for making predictions.
Read more
Syllabus
Course Orientation and Module 1: Regression Algorithm for Testing and Predicting Business Data
Exploratory data analysis (EDA) is a critical step in the business analytic workflow; however, EDA is a time-consuming approach for uncovering complex relationships. Moreover, the visualizations that are often used for EDA do not lend themselves well for quantifying confidence in results or for making predictions.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Explores machine learning algorithms and their applications in business, providing a foundation for understanding and using these techniques
Uses R and RStudio for hands-on practice, allowing learners to gain practical experience in applying machine learning algorithms
Covers a range of machine learning algorithms, including regression, classification, and clustering, providing a comprehensive understanding of different techniques
Taught by instructors from the Gies College of Business at the University of Illinois, known for their expertise in business analytics
Emphasizes the use of machine learning models to find actionable insights for business problems, providing practical value for learners
Designed for learners with a basic understanding of statistics and programming, making it accessible to those with different backgrounds
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Practical machine learning for business with r
According to learners, this course offers a highly practical approach to machine learning for business analytics, effectively linking algorithms to real-world business problems. Students praise the instructor's clear and concise explanations of complex topics, making the conceptual foundation accessible. The hands-on activities using R notebooks are consistently highlighted as a strength, providing valuable coding practice. However, a significant portion of feedback suggests that the course assumes a foundational to intermediate understanding of R, which might pose a challenge for absolute R beginners. While some older reviews noted potential issues with outdated R packages, more recent feedback indicates that this concern may have lessened, suggesting continuous improvements to the course content.
Past concerns about outdated R packages; recent feedback is positive.
"Some of the R packages used felt a little dated, or at least the versions presented in the course."
"It was difficult to replicate some steps due to package conflicts or changes in functions."
"The course could benefit from an update to ensure all code examples are fully compatible with current R environments."
R notebooks offer practical coding experience, reinforcing concepts.
"The R notebooks are a huge plus; they make the hands-on practice very effective."
"The use of R notebooks made it easy to follow along and experiment."
"The R coding exercises are well-integrated and reinforce the concepts beautifully."
"The R notebooks were super helpful for coding practice."
Instructor explains complex ML concepts with exceptional clarity.
"The instructor does a phenomenal job of explaining complex topics like logistic regression and decision trees clearly."
"The instructor is clear, concise, and deeply knowledgeable."
"The conceptual explanations of the algorithms were good."
"The conceptual understanding is prioritized, which is great."
Applies ML algorithms to real-world business scenarios effectively.
"The way it links machine learning algorithms directly to business problems is incredibly practical."
"The focus on actionable insights is what truly sets this course apart."
"The examples provided resonated with real-world scenarios, making the learning highly effective."
"This course teaches not just algorithms but more importantly, *when* and *how* to apply them in a business context."
Requires prior intermediate R knowledge; challenging for beginners.
"It assumes a pretty strong existing grasp of R, more than just 'basic'."
"If you're new to R or programming, you might struggle with the coding assignments."
"I struggled with this course. While the business application part was interesting, the R coding was extremely challenging... Not for absolute R beginners."
"The R implementation part a bit rushed, especially for k-means and DBSCAN."
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 Machine Learning Algorithms with R in Business Analytics with these
activities:
Identify Mentors in the Field of Machine Learning
Show steps
Seek guidance and support from experienced professionals to enhance your learning journey.
Browse courses on
Machine Learning
Show steps
-
Network at industry events or online platforms.
-
Reach out to professors or researchers in the field.
-
Identify potential mentors who align with your career goals.
Revisit Statistical Concepts for Machine Learning
Show steps
Enhance your grasp of statistical concepts, which form the foundation of machine learning algorithms.
Browse courses on
Statistical Concepts
Show steps
-
Review your previous coursework or textbooks on statistical concepts.
-
Identify online resources or tutorials that provide refresher materials.
-
Complete practice problems or quizzes to strengthen your understanding.
Review Machine Learning Algorithms
Show steps
Helps you strengthen your understanding of the fundamental algorithms used in machine learning, which are central to the course.
Browse courses on
Machine Learning Algorithms
Show steps
-
Go over your notes and textbook chapters on machine learning algorithms.
-
Work through practice problems related to each algorithm.
-
Create a summary of the key concepts and equations for each algorithm.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Explore R and RStudio for Machine Learning
Show steps
Provides hands-on practice with the tools you will use in the course, allowing you to be more efficient during class time.
Show steps
-
Find online tutorials on R and RStudio for machine learning.
-
Follow the tutorials and complete the exercises.
-
Create a small project using R and RStudio to apply your skills.
Classify Your Business Using Machine Learning Classification Algorithms
Show steps
Engage in guided tutorials to enhance your understanding of various machine learning algorithms for business classification.
Browse courses on
Classification Algorithms
Show steps
-
Review the course materials on machine learning classification algorithms.
-
Select a relevant machine learning tutorial platform, such as Coursera or edX.
-
Choose a tutorial that covers the specific classification algorithm you want to learn about.
-
Follow the tutorial steps and complete the exercises.
-
Apply the learned algorithm to a business use case.
Form a Study Group for the Course
Show steps
Provides a collaborative environment to review concepts, discuss assignments, and prepare for exams.
Show steps
-
Reach out to classmates who are interested in forming a study group.
-
Set regular meeting times and decide on a meeting format.
-
Take turns leading discussions, summarizing key concepts, and working through practice problems.
Practice Implementing K-Means Clustering Algorithms
Show steps
Reinforce your understanding of K-Means clustering by engaging in repetitive practice drills.
Browse courses on
Clustering Algorithms
Show steps
-
Review the course materials on K-Means clustering algorithms.
-
Identify an online platform or textbook that provides practice problems.
-
Solve several practice problems to gain proficiency in implementing K-Means clustering.
-
Experiment with different datasets and parameters to observe the impact on clustering results.
Collaborate on a Machine Learning Project with Peers
Show steps
Engage in collaborative learning by teaming up with peers to tackle a machine learning project.
Show steps
-
Find a group of peers with complementary skills and interests.
-
Brainstorm project ideas and select a feasible topic.
-
Divide responsibilities and work together to implement the project.
-
Share progress updates and provide constructive feedback to each other.
-
Present the final project outcomes and reflect on the learning experience.
Solve Machine Learning Algorithm Problems
Show steps
Solidifies your understanding of machine learning algorithms by applying them to real-world problems.
Browse courses on
Machine Learning Algorithms
Show steps
-
Find practice problems on machine learning algorithms online.
-
Solve the problems using the appropriate algorithm.
-
Check your solutions against the provided answers or discuss them with classmates.
Develop a Machine Learning Model for a Business Problem
Show steps
Demonstrate your mastery by creating a practical machine learning model that addresses a real-world business problem.
Browse courses on
Machine Learning Model
Show steps
-
Identify a business problem that can be addressed using machine learning.
-
Gather and prepare relevant data.
-
Choose and implement an appropriate machine learning algorithm.
-
Evaluate and refine the model's performance.
-
Present your findings and insights to stakeholders.
Create R Notebooks to Showcase Your Machine Learning Skills
Show steps
Helps you demonstrate your proficiency in R and machine learning, which can be valuable for job applications and future projects.
Show steps
-
Choose a machine learning problem or dataset.
-
Write R code to load and preprocess the data.
-
Apply machine learning algorithms to the data and evaluate their performance.
-
Create an R notebook that documents your work, including code, results, and insights.
Develop a Machine Learning Application for Personal Use
Show steps
Apply your knowledge by developing a machine learning application that caters to your personal interests or needs.
Browse courses on
Machine Learning
Show steps
-
Identify a personal problem or task that can be automated or improved using machine learning.
-
Research suitable machine learning algorithms and techniques.
-
Gather and prepare the necessary data.
-
Design and implement the machine learning application.
-
Test and evaluate the application's performance.
Contribute to Open-Source Machine Learning Projects
Show steps
Gives you practical experience with machine learning algorithms and tools, while also contributing to the community.
Show steps
-
Find open-source machine learning projects on platforms like GitHub.
-
Identify areas where you can contribute, such as bug fixes or feature enhancements.
-
Submit pull requests with your contributions.
-
Collaborate with other contributors to improve the project.
Build a Machine Learning Model for a Real-World Problem
Show steps
Allows you to apply the skills learned in the course to a practical problem, deepening your understanding and preparing you for future projects.
Browse courses on
Machine Learning Applications
Show steps
-
Identify a real-world problem that can be solved using machine learning.
-
Gather and preprocess the necessary data.
-
Choose and train appropriate machine learning algorithms.
-
Evaluate the performance of the model and make improvements as needed.
Identify Mentors in the Field of Machine Learning
Show steps
Seek guidance and support from experienced professionals to enhance your learning journey.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Network at industry events or online platforms.
- Reach out to professors or researchers in the field.
- Identify potential mentors who align with your career goals.
Revisit Statistical Concepts for Machine Learning
Show steps
Enhance your grasp of statistical concepts, which form the foundation of machine learning algorithms.
Browse courses on
Statistical Concepts
Show steps
Show steps
Show steps
- Review your previous coursework or textbooks on statistical concepts.
- Identify online resources or tutorials that provide refresher materials.
- Complete practice problems or quizzes to strengthen your understanding.
Review Machine Learning Algorithms
Show steps
Helps you strengthen your understanding of the fundamental algorithms used in machine learning, which are central to the course.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Go over your notes and textbook chapters on machine learning algorithms.
- Work through practice problems related to each algorithm.
- Create a summary of the key concepts and equations for each algorithm.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Explore R and RStudio for Machine Learning
Show steps
Provides hands-on practice with the tools you will use in the course, allowing you to be more efficient during class time.
Show steps
Show steps
Show steps
- Find online tutorials on R and RStudio for machine learning.
- Follow the tutorials and complete the exercises.
- Create a small project using R and RStudio to apply your skills.
Classify Your Business Using Machine Learning Classification Algorithms
Show steps
Engage in guided tutorials to enhance your understanding of various machine learning algorithms for business classification.
Browse courses on
Classification Algorithms
Show steps
Show steps
Show steps
- Review the course materials on machine learning classification algorithms.
- Select a relevant machine learning tutorial platform, such as Coursera or edX.
- Choose a tutorial that covers the specific classification algorithm you want to learn about.
- Follow the tutorial steps and complete the exercises.
- Apply the learned algorithm to a business use case.
Form a Study Group for the Course
Show steps
Provides a collaborative environment to review concepts, discuss assignments, and prepare for exams.
Show steps
Show steps
Show steps
- Reach out to classmates who are interested in forming a study group.
- Set regular meeting times and decide on a meeting format.
- Take turns leading discussions, summarizing key concepts, and working through practice problems.
Practice Implementing K-Means Clustering Algorithms
Show steps
Reinforce your understanding of K-Means clustering by engaging in repetitive practice drills.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Review the course materials on K-Means clustering algorithms.
- Identify an online platform or textbook that provides practice problems.
- Solve several practice problems to gain proficiency in implementing K-Means clustering.
- Experiment with different datasets and parameters to observe the impact on clustering results.
Collaborate on a Machine Learning Project with Peers
Show steps
Engage in collaborative learning by teaming up with peers to tackle a machine learning project.
Show steps
Show steps
Show steps
- Find a group of peers with complementary skills and interests.
- Brainstorm project ideas and select a feasible topic.
- Divide responsibilities and work together to implement the project.
- Share progress updates and provide constructive feedback to each other.
- Present the final project outcomes and reflect on the learning experience.
Solve Machine Learning Algorithm Problems
Show steps
Solidifies your understanding of machine learning algorithms by applying them to real-world problems.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Find practice problems on machine learning algorithms online.
- Solve the problems using the appropriate algorithm.
- Check your solutions against the provided answers or discuss them with classmates.
Develop a Machine Learning Model for a Business Problem
Show steps
Demonstrate your mastery by creating a practical machine learning model that addresses a real-world business problem.
Browse courses on
Machine Learning Model
Show steps
Show steps
Show steps
- Identify a business problem that can be addressed using machine learning.
- Gather and prepare relevant data.
- Choose and implement an appropriate machine learning algorithm.
- Evaluate and refine the model's performance.
- Present your findings and insights to stakeholders.
Create R Notebooks to Showcase Your Machine Learning Skills
Show steps
Helps you demonstrate your proficiency in R and machine learning, which can be valuable for job applications and future projects.
Show steps
Show steps
Show steps
- Choose a machine learning problem or dataset.
- Write R code to load and preprocess the data.
- Apply machine learning algorithms to the data and evaluate their performance.
- Create an R notebook that documents your work, including code, results, and insights.
Develop a Machine Learning Application for Personal Use
Show steps
Apply your knowledge by developing a machine learning application that caters to your personal interests or needs.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a personal problem or task that can be automated or improved using machine learning.
- Research suitable machine learning algorithms and techniques.
- Gather and prepare the necessary data.
- Design and implement the machine learning application.
- Test and evaluate the application's performance.
Contribute to Open-Source Machine Learning Projects
Show steps
Gives you practical experience with machine learning algorithms and tools, while also contributing to the community.
Show steps
Show steps
Show steps
- Find open-source machine learning projects on platforms like GitHub.
- Identify areas where you can contribute, such as bug fixes or feature enhancements.
- Submit pull requests with your contributions.
- Collaborate with other contributors to improve the project.
Build a Machine Learning Model for a Real-World Problem
Show steps
Allows you to apply the skills learned in the course to a practical problem, deepening your understanding and preparing you for future projects.
Browse courses on
Machine Learning Applications
Show steps
Show steps
Show steps
- Identify a real-world problem that can be solved using machine learning.
- Gather and preprocess the necessary data.
- Choose and train appropriate machine learning algorithms.
- Evaluate the performance of the model and make improvements as needed.
Career center
Learners who complete Machine Learning Algorithms with R in Business Analytics will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
**Data Analysts** use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They then use this information to make recommendations and develop solutions. Machine learning algorithms are a powerful tool for data analysts, as they can help to automate the process of data analysis and make it more efficient. This course will teach you the basics of machine learning algorithms and how to use them to solve business problems. This course can help you develop the skills you need to be a successful Data Analyst.
Machine Learning Engineer
Machine Learning Engineer
**Machine Learning Engineers** design, build, and deploy machine learning models. They work closely with data scientists to identify the business problems that can be solved with machine learning and then develop and implement the models that will solve those problems. This course will teach you the basics of machine learning algorithms and how to use them to solve business problems. This course can help you develop the skills you need to be a successful Machine Learning Engineer.
Data Scientist
Data Scientist
**Data Scientists** use data to solve business problems. They use a variety of statistical and machine learning techniques to analyze data and identify trends and patterns. They then use this information to make recommendations and develop solutions. This course will teach you the basics of machine learning algorithms and how to use them to solve business problems. This course can help you develop the skills you need to be a successful Data Scientist.
Business Intelligence Analyst
Business Intelligence Analyst
**Business Intelligence Analysts** use data to help businesses make better decisions. They collect, clean, and analyze data to identify trends and patterns. They then use this information to develop reports and recommendations that can help businesses improve their performance. This course will teach you the basics of machine learning algorithms and how to use them to analyze data. This course can help you develop the skills you need to be a successful Business Intelligence Analyst.
Marketing Analyst
Marketing Analyst
**Marketing Analysts** use data to help businesses understand their customers and develop effective marketing campaigns. They collect, clean, and analyze data to identify trends and patterns. They then use this information to develop reports and recommendations that can help businesses improve their marketing efforts. This course will teach you the basics of machine learning algorithms and how to use them to analyze data. This course can help you develop the skills you need to be a successful Marketing Analyst.
Product Manager
Product Manager
**Product Managers** are responsible for the development and launch of new products. They work closely with engineers, designers, and marketing teams to ensure that products meet the needs of customers. Machine learning algorithms can be used to analyze data and identify trends that can help product managers make better decisions about product development and launch. This course will teach you the basics of machine learning algorithms and how to use them to analyze data. This course can help you develop the skills you need to be a successful Product Manager.
Financial Analyst
Financial Analyst
**Financial Analysts** use data to help businesses make investment decisions. They collect, clean, and analyze data to identify trends and patterns. They then use this information to develop reports and recommendations that can help businesses make better investment decisions. This course will teach you the basics of machine learning algorithms and how to use them to analyze data. This course can help you develop the skills you need to be a successful Financial Analyst.
Operations Research Analyst
Operations Research Analyst
**Operations Research Analysts** use data to help businesses improve their operations. They collect, clean, and analyze data to identify inefficiencies and develop solutions that can improve efficiency and productivity. Machine learning algorithms can be used to analyze data and identify patterns that can help operations research analysts develop better solutions. This course will teach you the basics of machine learning algorithms and how to use them to analyze data. This course can help you develop the skills you need to be a successful Operations Research Analyst.
Risk Analyst
Risk Analyst
**Risk Analysts** use data to help businesses identify and mitigate risks. They collect, clean, and analyze data to identify trends and patterns that can indicate potential risks. They then use this information to develop reports and recommendations that can help businesses reduce their risk exposure. This course will teach you the basics of machine learning algorithms and how to use them to analyze data. This course can help you develop the skills you need to be a successful Risk Analyst.
Sales Analyst
Sales Analyst
**Sales Analysts** use data to help businesses improve their sales performance. They collect, clean, and analyze data to identify trends and patterns that can indicate opportunities for sales growth. They then use this information to develop reports and recommendations that can help businesses improve their sales strategies. This course will teach you the basics of machine learning algorithms and how to use them to analyze data. This course can help you develop the skills you need to be a successful Sales Analyst.
Statistician
Statistician
**Statisticians** use data to analyze trends and patterns. They develop and use statistical models to make inferences about data and to predict future outcomes. Machine learning algorithms are a powerful tool for statisticians, as they can help to automate the process of data analysis and make it more efficient. This course will teach you the basics of machine learning algorithms and how to use them to analyze data. This course can help you develop the skills you need to be a successful Statistician.
Data Engineer
Data Engineer
**Data Engineers** design and build the systems that store and manage data. They work with data scientists and other data professionals to ensure that data is available and accessible for analysis. Machine learning algorithms can be used to automate the process of data engineering and make it more efficient. This course will teach you the basics of machine learning algorithms and how to use them to automate data engineering tasks. This course can help you develop the skills you need to be a successful Data Engineer.
Software Engineer
Software Engineer
**Software Engineers** design, build, and maintain software applications. They work with users to understand their needs and then develop software solutions that meet those needs. Machine learning algorithms can be used to improve the performance and functionality of software applications. This course will teach you the basics of machine learning algorithms and how to use them to develop software applications. This course can help you develop the skills you need to be a successful Software Engineer.
Web Developer
Web Developer
**Web Developers** design and build websites. They work with users to understand their needs and then develop websites that meet those needs. Machine learning algorithms can be used to improve the performance and functionality of websites. This course will teach you the basics of machine learning algorithms and how to use them to develop websites. This course can help you develop the skills you need to be a successful Web Developer.
Database Administrator
Database Administrator
**Database Administrators** design, build, and maintain databases. They work with users to understand their needs and then develop databases that meet those needs. Machine learning algorithms can be used to improve the performance and functionality of databases. This course will teach you the basics of machine learning algorithms and how to use them to develop databases. This course can help you develop the skills you need to be a successful Database Administrator.
For more career information including salaries, visit:
OpenCourser.com/course/7m3kww/machine
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
Machine Learning Algorithms with R in Business Analytics.
Provides a more theoretical introduction to machine learning, with a focus on statistical methods. It covers a wide range of topics, including supervised and unsupervised learning, as well as model selection and evaluation.
Classic reference on statistical learning. It provides a comprehensive overview of the field, with a focus on theoretical foundations and practical applications.
Save
Provides a comprehensive overview of deep learning, with a focus on theoretical foundations and practical applications.
Provides a comprehensive overview of reinforcement learning, with a focus on theoretical foundations and practical applications.
Provides a comprehensive overview of Bayesian data analysis, with a focus on theoretical foundations and practical applications.
Provides a comprehensive overview of pattern recognition and machine learning, with a focus on theoretical foundations and practical applications.
Provides a comprehensive overview of machine learning in finance, with a focus on practical applications.
Provides a comprehensive overview of interpretable machine learning, with a focus on practical applications.
Save
Provides a practical introduction to machine learning for business, with a focus on real-world applications.
Provides a comprehensive overview of machine learning with R, with a focus on practical applications.
Save
Provides a comprehensive overview of econometrics, with a focus on theoretical foundations and practical applications.
Save
Provides a comprehensive overview of time series analysis, with a focus on theoretical foundations and practical applications.
Save
Provides a comprehensive overview of forecasting, with a focus on practical applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/7m3kww/machine
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Effort
4 weeks of study, 5-7 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Illinois at Urbana-Champaign
Instructors
Ronald Guymon
Gies College of Business, University of Illinois
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
magyar nyelv
Bahasa Indonesia
ไทย
қазақша
اُردُو,
فارسی
বাংলা
tiếng Việt
پښتو
آذربایجان دیلی
This course belongs to a
collection
called
Business Analytics. It's comprised of six courses:
- Introduction to Business Analytics: Communicating with Data
- Machine Learning Algorithms with R in Business Analytics (viewing)
- Applying Data Analytics in Marketing
- Introduction to Business Analytics with R
- Tools for Exploratory Data Analysis in Business
- Applying Data Analytics in Accounting
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser