We may earn an affiliate commission when you visit our partners.
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 using the FFTrees package in R, and examine the results using 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 using the FFTrees package in R, and examine the results using 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 using the FFTrees package in R, and examine the results using 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/d05t43/machine
Three deals to help you save
We found three deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until December 12
Annual Subscription Savings
Unlock programs from Google, Microsoft, and IBM and join 77% of learners who reported positive career outcomes.
Only $239!
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
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
Requires learners 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 using the FFTrees package in R, and examine the results using a Confusion Matrix
Save this course
Save Machine Learning: Predict Poisonous Mushrooms using a Random Forest Model and the FFTrees Package in R to your list so you can find it easily later:
Save
Save
Reviews summary
Poisonous mushrooms course
Learners largely find this course to be very good, finding it very useful with fantastic explanations. The engaging assignments and interesting project with R are also well received. However, one review indicated that some datasets and functions were difficult to load.
Engaging assignments and project
""The explanations given were fantastic and I learned a lot.""
""A very interesting project to be done with R.""
""Very insightful project""
Dataset and library loading issues
""Parecía buen curso pero el dataset utilizado nunca pudo cargarse al igual que varias funciones utilizadas no funcionaban correctamente.""
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: Predict Poisonous Mushrooms using a Random Forest Model and the FFTrees Package in R with these
activities:
Review R programming basics
Show steps
Strengthen your foundation in R programming by reviewing basic concepts and syntax to ensure you have a solid base before diving deeper into the course material.
Browse courses on
R Programming
Show steps
-
Go through online resources or tutorials on R basics.
-
Practice writing and running simple R scripts.
-
Test your understanding by solving coding exercises or quizzes.
Organize course materials for easy reference
Show steps
Stay organized and efficient by compiling notes, assignments, and other course materials into a central and easily accessible location, enabling you to focus on learning rather than searching for resources.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Regularly add and organize notes, assignments, and relevant resources.
-
Review and update your compiled materials periodically for better retention.
Join a study group for peer support and collaboration
Show steps
Enhance your learning experience by joining a study group where you can connect with peers, share knowledge, and collaborate on assignments, fostering a supportive and interactive learning environment.
Show steps
-
Find or create a study group with other course participants.
-
Meet regularly to discuss course material, work on assignments together, and support each other's learning.
-
Be an active participant by contributing your ideas and helping others.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice using the FFTrees package
Show steps
Gain hands-on experience with the FFTrees package in R by going through exercises and drills.
Show steps
-
Install the FFTrees package in R.
-
Load the example data provided in the course.
-
Apply the Random Forest model using the FFTrees package.
Explore ggplot2 for data visualization
Show steps
Expand your understanding of data visualization by following guided tutorials on ggplot2, a powerful library for creating informative and visually appealing graphics in R.
Browse courses on
Ggplot2
Show steps
-
Find online tutorials or documentation on ggplot2.
-
Follow the steps in the tutorial to create your own data visualizations.
-
Experiment with different ggplot2 functions to explore different visualization techniques.
Complete practice exercises to reinforce concepts
Show steps
Solidify your understanding of course concepts by engaging in practice exercises that provide hands-on experience and reinforce key ideas.
Show steps
-
Identify practice exercises within the course or search for additional resources online.
-
Complete the exercises thoroughly, focusing on understanding the underlying concepts.
-
Review your answers and seek clarification on any areas where you need support.
Develop a confusion matrix to interpret model results
Show steps
Enhance your understanding of model performance by creating a confusion matrix, a valuable tool for evaluating the accuracy and effectiveness of your machine learning model.
Browse courses on
Confusion Matrix
Show steps
-
Learn about confusion matrix concepts and metrics.
-
Calculate and interpret confusion matrix values for your model.
-
Present your confusion matrix in a clear and organized manner.
Review R programming basics
Show steps
Strengthen your foundation in R programming by reviewing basic concepts and syntax to ensure you have a solid base before diving deeper into the course material.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Go through online resources or tutorials on R basics.
- Practice writing and running simple R scripts.
- Test your understanding by solving coding exercises or quizzes.
Organize course materials for easy reference
Show steps
Stay organized and efficient by compiling notes, assignments, and other course materials into a central and easily accessible location, enabling you to focus on learning rather than searching for resources.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Regularly add and organize notes, assignments, and relevant resources.
- Review and update your compiled materials periodically for better retention.
Join a study group for peer support and collaboration
Show steps
Enhance your learning experience by joining a study group where you can connect with peers, share knowledge, and collaborate on assignments, fostering a supportive and interactive learning environment.
Show steps
Show steps
Show steps
- Find or create a study group with other course participants.
- Meet regularly to discuss course material, work on assignments together, and support each other's learning.
- Be an active participant by contributing your ideas and helping others.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice using the FFTrees package
Show steps
Gain hands-on experience with the FFTrees package in R by going through exercises and drills.
Show steps
Show steps
Show steps
- Install the FFTrees package in R.
- Load the example data provided in the course.
- Apply the Random Forest model using the FFTrees package.
Explore ggplot2 for data visualization
Show steps
Expand your understanding of data visualization by following guided tutorials on ggplot2, a powerful library for creating informative and visually appealing graphics in R.
Browse courses on
Ggplot2
Show steps
Show steps
Show steps
- Find online tutorials or documentation on ggplot2.
- Follow the steps in the tutorial to create your own data visualizations.
- Experiment with different ggplot2 functions to explore different visualization techniques.
Complete practice exercises to reinforce concepts
Show steps
Solidify your understanding of course concepts by engaging in practice exercises that provide hands-on experience and reinforce key ideas.
Show steps
Show steps
Show steps
- Identify practice exercises within the course or search for additional resources online.
- Complete the exercises thoroughly, focusing on understanding the underlying concepts.
- Review your answers and seek clarification on any areas where you need support.
Develop a confusion matrix to interpret model results
Show steps
Enhance your understanding of model performance by creating a confusion matrix, a valuable tool for evaluating the accuracy and effectiveness of your machine learning model.
Browse courses on
Confusion Matrix
Show steps
Show steps
Show steps
- Learn about confusion matrix concepts and metrics.
- Calculate and interpret confusion matrix values for your model.
- Present your confusion matrix in a clear and organized manner.
Career center
Learners who complete Machine Learning: Predict Poisonous Mushrooms using a Random Forest Model and the FFTrees Package in R will develop knowledge and skills
that may be useful to these careers:
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts apply machine learning models to medical data to improve patient outcomes and optimize healthcare processes. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding how to build and evaluate these models. By learning how to apply Random Forest to real-world healthcare data, you will gain valuable skills for a career in Healthcare Data Analytics.
Data Scientist
Data Scientist
Data Scientists use machine learning to extract insights from data and solve business problems. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to real-world data. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Data Science.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models to solve real-world problems. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to real-world data. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Machine Learning Engineering.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use machine learning to model financial data and make investment decisions. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to financial data. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Quantitative Analysis.
Biostatistician
Biostatistician
Biostatisticians use machine learning to analyze medical data and design clinical trials. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to medical data. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Biostatistics.
Epidemiologist
Epidemiologist
Epidemiologists use machine learning to track and predict the spread of disease. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to epidemiological data. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Epidemiology.
Market Researcher
Market Researcher
Market Researchers use machine learning to understand consumer behavior and predict market trends. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to market research data. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Market Research.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use machine learning to optimize business processes and improve efficiency. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to business data. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Operations Research.
Risk Analyst
Risk Analyst
Risk Analysts use machine learning to assess and manage risk. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to risk data. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Risk Analysis.
Statistician
Statistician
Statisticians use machine learning to analyze data and draw conclusions. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to real-world data. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Statistics.
Data Analyst
Data Analyst
Data Analysts use machine learning to extract insights from data and solve business problems. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to real-world data. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Data Analysis.
Business Analyst
Business Analyst
Business Analysts use machine learning to understand business processes and improve efficiency. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to business data. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Business Analysis.
Software Engineer
Software Engineer
Software Engineers use machine learning to develop new software products and features. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to software development. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Software Engineering.
Computer Scientist
Computer Scientist
Computer Scientists use machine learning to develop new algorithms and technologies. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to computer science problems. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Computer Science.
Actuary
Actuary
Actuaries use machine learning to assess and manage risk. This course in Machine Learning, specifically using the Random Forest model and FFTrees package in R, provides a solid foundation for understanding the principles of machine learning and how to apply them to risk data. By learning how to build and evaluate Random Forest models, you will gain valuable skills for a career in Actuarial Science.
For more career information including salaries, visit:
OpenCourser.com/course/d05t43/machine
Reading list
We've selected 11 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: Predict Poisonous Mushrooms using a Random Forest Model and the FFTrees Package in R.
Provides a comprehensive introduction to statistical learning, covering the fundamental concepts and algorithms. It valuable resource for learners who want to gain a deep understanding of statistical learning theory and practice.
Provides a comprehensive introduction to pattern recognition and machine learning. It valuable resource for learners who want to gain a deep understanding of the theory and practice of pattern recognition and machine learning.
Provides a comprehensive introduction to machine learning using the R programming language. It valuable resource for learners who want to gain a deep understanding of machine learning theory and practice.
Provides a practical introduction to machine learning using the R programming language. It valuable resource for learners who want to apply machine learning techniques to real-world problems.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It valuable resource for learners who want to gain a deep understanding of the theory and practice of machine learning.
Save
Provides a comprehensive introduction to deep learning. It valuable resource for learners who want to gain a deep understanding of the theory and practice of deep learning.
Provides a comprehensive introduction to information theory, inference, and learning algorithms. It valuable resource for learners who want to gain a deep understanding of the theory and practice of information theory, inference, and learning algorithms.
Provides a comprehensive introduction to machine learning using the Python programming language. It valuable resource for learners who want to gain a practical understanding of machine learning using the Python programming language.
Provides a comprehensive introduction to statistical methods for machine learning. It valuable resource for learners who want to gain a deep understanding of the theory and practice of statistical methods for machine learning.
Provides a comprehensive introduction to reinforcement learning. It valuable resource for learners who want to gain a deep understanding of the theory and practice of reinforcement learning.
Provides a comprehensive introduction to convex optimization. It valuable resource for learners who want to gain a deep understanding of the theory and practice of convex optimization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/d05t43/machine
Share
Similar courses
Here are nine courses similar to
Machine Learning: Predict Poisonous Mushrooms using a Random Forest Model and the FFTrees Package in R.
Handling Missing Values in R using tidyr
OpenCourser.com/course/jsfzz2/handling
Handling Missing Values in R using tidyr
Tidy Messy Data using tidyr in R
OpenCourser.com/course/0az7w9/tidy
Tidy Messy Data using tidyr in R
Data Analytics: Scraping Data using Hadley Wickam's Rvest package in R
OpenCourser.com/course/qhji8f/data
Data Analytics: Scraping Data using Hadley Wickam's...
Joining Data in R using dplyr
OpenCourser.com/course/kfe8uv/joining
Joining Data in R using dplyr
Customer Segmentation using K-Means Clustering in R
OpenCourser.com/course/u5z5j1/customer
Customer Segmentation using K-Means Clustering in R
Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set
OpenCourser.com/course/14z6r4/predict
Predict Gas Guzzlers using a Neural Net Model on the MPG...
Predict Housing Prices in R on Boston Housing Data
OpenCourser.com/course/fsuvyc/predict
Predict Housing Prices in R on Boston Housing Data
Predict Diabetes with a Random Forest using R
OpenCourser.com/course/ltbqgk/predict
Predict Diabetes with a Random Forest using R
Predict Ideal Diamonds over Good Diamonds using a Random Forest using R
OpenCourser.com/course/eus2uv/predict
Predict Ideal Diamonds over Good Diamonds using a Random...
Effort
2 Hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Chris Shockley
Language
English
Good to know
Requires learners 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 using the FFTrees package in R, and examine the results using a Confusion Matrix
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 - 2024 OpenCourser