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Chris Shockley
In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Random Forest model to the data, and examine the results using RMSE and a Confusion Matrix).
Read more
In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Random Forest model to the data, and examine the results using RMSE and a Confusion Matrix).
Read more
In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Random Forest model to the data, and examine the results using RMSE and a Confusion Matrix).
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/ltbqgk/predict
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What's inside
Syllabus
Project Overview
Here you will describe what the project is about. It should give an overview of what the learner will achieve by completing this project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Designed for beginners who want to learn data science techniques
Provides hands-on practice with R and ggplot2
Taught by Chris Shockley, an experienced data scientist
Covers fundamental data science concepts like data distribution and model evaluation
Suitable for learners interested in exploring data science or upskilling
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Save Predict Diabetes with a Random Forest using R to your list so you can find it easily later:
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Reviews summary
Data science: diabetes prediction
Learners say that this course is a useful data science project for those already familiar with machine learning fundamentals. Students find this project adequate for their needs and skill level and appreciate that it includes code with explanations. However, some say that the explanations for the math are insufficient and that the course doesn't go into enough depth for experienced data scientists.
Appropriate for learners already familiar with ML algorithms.
"It is a great course who are primarily familiar with ML algorithms and confusion matrices."
Useful for intended purpose.
"I got what I needed for my project although I would like more lessons on the topic I have improved a little thanks"
Code explanations are good, but mathematical explanations could be better.
"some parts of the code are not fullly explained"
"Not much explanation, quick run through the code."
"Need a bit more explanation on the mathematics part"
Not enough depth for experienced learners.
"This project provides a great understanding in model training, testing and checking accuracy of model on a relatively easy dataset."
"It would be desirable to learn a bit more out of this course like data pre-processing and correction of data imbalance."
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 Predict Diabetes with a Random Forest using R with these
activities:
Data Preprocessing and Exploration Exercises
Show steps
Enhance your data handling skills and gain insights into your data.
Browse courses on
Random Forest
Show steps
-
Practice data cleaning and transformation techniques.
-
Explore data distributions and identify patterns.
-
Create visualizations to summarize and communicate your findings.
RStudio Tutorial for Beginners
Show steps
Familiarize yourself with the RStudio environment and basic R programming concepts.
Show steps
-
Install and set up RStudio.
-
Complete the RStudio tutorial for beginners.
-
Explore the RStudio interface and tools.
An Introduction to Statistical Learning
Show steps
Expand your knowledge of machine learning algorithms and techniques.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read chapters 9 and 10 of the book.
-
Review the concepts of decision trees, random forests, and boosting.
-
Apply the techniques you learn to solve practical problems.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Machine Learning Community
Show steps
Connect with experienced practitioners and get guidance on your learning journey.
Browse courses on
Random Forest
Show steps
-
Join online forums or discussion groups related to machine learning.
-
Attend local meetups and conferences to network with experts.
-
Seek guidance from a mentor who can provide personalized advice.
Case Study Proposal
Show steps
Draft a proposal for a case study that applies the Random Forest model to a specific business or research problem.
Browse courses on
Random Forest
Show steps
-
Identify a problem or question that can be addressed with a Random Forest model.
-
Research relevant data sources and gather data.
-
Explore data distribution and prepare training and test sets.
-
Build a Random Forest model and tune its parameters.
-
Evaluate the model's performance using RMSE and a confusion matrix.
Random Forest Parameter Tuning Exercises
Show steps
Reinforce your understanding of Random Forest parameter tuning through guided exercises.
Browse courses on
Random Forest
Show steps
-
Experiment with different values for the number of trees and maximum tree depth.
-
Explore the impact of using different feature subsets for each tree.
-
Evaluate the performance of your tuned models using cross-validation.
The Elements of Statistical Learning
Show steps
Advance your knowledge of machine learning theory and algorithms.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
-
Read chapters 7 and 8 of the book.
-
Understand the concepts of model selection and regularization.
-
Apply these techniques to optimize your machine learning models.
Machine Learning Blog Post
Show steps
Demonstrate your understanding of machine learning concepts by creating a blog post.
Browse courses on
Random Forest
Show steps
-
Choose a specific topic related to the course.
-
Research and gather information from credible sources.
-
Organize your content and write in a clear and engaging style.
-
Include examples, illustrations, or code snippets to enhance your explanation.
Data Preprocessing and Exploration Exercises
Show steps
Enhance your data handling skills and gain insights into your data.
Browse courses on
Random Forest
Show steps
Show steps
Show steps
- Practice data cleaning and transformation techniques.
- Explore data distributions and identify patterns.
- Create visualizations to summarize and communicate your findings.
RStudio Tutorial for Beginners
Show steps
Familiarize yourself with the RStudio environment and basic R programming concepts.
Show steps
Show steps
Show steps
- Install and set up RStudio.
- Complete the RStudio tutorial for beginners.
- Explore the RStudio interface and tools.
An Introduction to Statistical Learning
Show steps
Expand your knowledge of machine learning algorithms and techniques.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 9 and 10 of the book.
- Review the concepts of decision trees, random forests, and boosting.
- Apply the techniques you learn to solve practical problems.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Machine Learning Community
Show steps
Connect with experienced practitioners and get guidance on your learning journey.
Browse courses on
Random Forest
Show steps
Show steps
Show steps
- Join online forums or discussion groups related to machine learning.
- Attend local meetups and conferences to network with experts.
- Seek guidance from a mentor who can provide personalized advice.
Case Study Proposal
Show steps
Draft a proposal for a case study that applies the Random Forest model to a specific business or research problem.
Browse courses on
Random Forest
Show steps
Show steps
Show steps
- Identify a problem or question that can be addressed with a Random Forest model.
- Research relevant data sources and gather data.
- Explore data distribution and prepare training and test sets.
- Build a Random Forest model and tune its parameters.
- Evaluate the model's performance using RMSE and a confusion matrix.
Random Forest Parameter Tuning Exercises
Show steps
Reinforce your understanding of Random Forest parameter tuning through guided exercises.
Browse courses on
Random Forest
Show steps
Show steps
Show steps
- Experiment with different values for the number of trees and maximum tree depth.
- Explore the impact of using different feature subsets for each tree.
- Evaluate the performance of your tuned models using cross-validation.
The Elements of Statistical Learning
Show steps
Advance your knowledge of machine learning theory and algorithms.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 7 and 8 of the book.
- Understand the concepts of model selection and regularization.
- Apply these techniques to optimize your machine learning models.
Machine Learning Blog Post
Show steps
Demonstrate your understanding of machine learning concepts by creating a blog post.
Browse courses on
Random Forest
Show steps
Show steps
Show steps
- Choose a specific topic related to the course.
- Research and gather information from credible sources.
- Organize your content and write in a clear and engaging style.
- Include examples, illustrations, or code snippets to enhance your explanation.
Career center
Learners who complete Predict Diabetes with a Random Forest using R will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to extract meaningful insights from data. This course provides a solid foundation in machine learning, which is a key skill for Data Scientists. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Statistician
Statistician
Statisticians use their knowledge of data analysis to help businesses and organizations make informed decisions. This course provides a solid foundation in statistics, which is a key skill for Statisticians. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their knowledge of machine learning to build and deploy machine learning models. This course provides a solid foundation in machine learning, which is a key skill for Machine Learning Engineers. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Data Analyst
Data Analyst
Data Analysts use their knowledge of data analysis to help businesses and organizations make informed decisions. This course provides a solid foundation in data analysis, which is a key skill for Data Analysts. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Business Analyst
Business Analyst
Business Analysts use their knowledge of data analysis to help businesses and organizations make informed decisions. This course provides a solid foundation in data analysis, which is a key skill for Business Analysts. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Actuary
Actuary
Actuaries use their knowledge of mathematics and statistics to analyze financial data. This course provides a solid foundation in statistics, which is a key skill for Actuaries. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics and statistics to analyze financial data. This course provides a solid foundation in statistics, which is a key skill for Quantitative Analysts. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Risk Analyst
Risk Analyst
Risk Analysts use their knowledge of mathematics and statistics to analyze financial data. This course provides a solid foundation in statistics, which is a key skill for Risk Analysts. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Database Administrator
Database Administrator
Database Administrators use their knowledge of computer science to design and manage databases. This course provides a solid foundation in data analysis, which is a key skill for Database Administrators. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Software Engineer
Software Engineer
Software Engineers use their knowledge of computer science to design and develop software applications. This course provides a solid foundation in machine learning, which is a key skill for Software Engineers who want to develop data-driven applications. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Data Engineer
Data Engineer
Data Engineers use their knowledge of computer science to design and develop data pipelines. This course provides a solid foundation in data analysis, which is a key skill for Data Engineers. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of mathematics and statistics to analyze financial data. This course provides a solid foundation in statistics, which is a key skill for Financial Analysts. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics and statistics to solve business problems. This course provides a solid foundation in statistics, which is a key skill for Operations Research Analysts. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use their knowledge of data analysis to help businesses and organizations make informed decisions. This course provides a solid foundation in data analysis, which is a key skill for Business Intelligence Analysts. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
Market Researcher
Market Researcher
Market Researchers use their knowledge of statistics to analyze market data. This course provides a solid foundation in statistics, which is a key skill for Market Researchers. By learning how to apply a Random Forest model to data, learners can develop the skills necessary to build predictive models that can be used to solve real-world problems.
For more career information including salaries, visit:
OpenCourser.com/course/ltbqgk/predict
Reading list
We've selected 15 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
Predict Diabetes with a Random Forest using R.
A comprehensive reference for statistical learning methods, this book provides a solid theoretical foundation for understanding machine learning algorithms. It valuable resource for those who want to delve deeper into the subject.
Provides a theoretical foundation for machine learning from a probabilistic perspective. Covers topics such as Bayesian inference, graphical models, and reinforcement learning.
Save
A practical guide to using R for data science, this book covers data manipulation, visualization, and statistical modeling. It provides a solid foundation for using R for data analysis and machine learning tasks.
Provides a practical approach to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. Covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
A comprehensive guide to deep learning using Python. Provides a solid foundation for understanding deep learning concepts and algorithms.
Provides practical experience with machine learning techniques and algorithms using R. contains numerous examples and exercises that can help you apply machine learning to real-world problems.
A classic textbook on reinforcement learning. Provides a comprehensive overview of reinforcement learning theory and algorithms.
If you are interested in deep learning using R, this book good choice. Provides a comprehensive introduction to deep learning and its applications, including image recognition, natural language processing, and time series analysis.
Save
Focuses on data mining techniques and algorithms using R. Provides a comprehensive overview of data mining concepts and applications, including data preprocessing, feature selection, and model evaluation.
Provides a collection of recipes for solving common machine learning problems using Python. Covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
Covers the basics of machine learning and deep learning using TensorFlow. Provides a practical approach to building and training machine learning models.
Great introduction to machine learning for those with basic R programming skills. Provides a solid foundation for understanding the concepts and algorithms used in machine learning.
While this book focuses on machine learning with Python, it can still be a valuable resource for those who want to learn about the concepts and algorithms used in machine learning. Provides a comprehensive overview of machine learning techniques and applications.
A practical guide to machine learning for those with programming experience. Provides a hands-on approach to building and deploying machine learning models.
Suitable for beginners, this book provides a gentle introduction to machine learning using R. Covers the basics of machine learning and provides practical examples using R code.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ltbqgk/predict
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Effort
2 Hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Chris Shockley
Language
English
Good to know
Designed for beginners who want to learn data science techniques
Provides hands-on practice with R and ggplot2
Taught by Chris Shockley, an experienced data scientist
Covers fundamental data science concepts like data distribution and model evaluation
Suitable for learners interested in exploring data science or upskilling
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