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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
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
Module 2: Framework for Machine Learning and Logistic Regression
Gain an understanding of machine learning in business and logistic regression
Module 3: Classification Algorithms
Classification algorithms in general, K-nearest neighbors, and decision trees.
Module 4: Clustering Algorithms
Clustering algorithms, k-means, and DBSCAN
Good to know
Good to know
Know what's good ,
what to watch for , 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
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Save Machine Learning Algorithms with R in Business Analytics to your list so you can find it easily later:
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Reviews summary
Business analytics with <span>r</span>
Learners say this R course in business analytics provides a good starting point for learners interested in data science. According to students, the course features engaging assignments and excellent professors. However, some students mention that the peer review process is unfair and the quizzes do not provide feedback.
Excellent instructors.
"Excellent course and excellent professors. "
Engaging assignments.
"An awesome course that you'll definitely enjoy."
Good course for beginners.
"A course to learn some notions to apply data science in business. I would have liked more examples, but still, it is a good starting point to specialise in this knowledge."
Peer reviews are unfair.
"The peer review assignment is not arbitrated wich is a little unfair."
Quizzes lack feedback.
"When you misake the answer in the quiz it doesn't give you a feedback, just is marked as wrong."
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
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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
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آذربایجان دیلی
This course belongs to a
collection
called
Business Analytics. It's comprised of six courses:
- Tools for Exploratory Data Analysis in Business
- Introduction to Business Analytics with R
- Introduction to Business Analytics: Communicating with Data
- Machine Learning Algorithms with R in Business Analytics (viewing)
- Applying Data Analytics in Accounting
- Applying Data Analytics in Marketing
Good to know
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
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