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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/eus2uv/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
Course duration is just 1 hour
Requires no prior programming knowledge
Best suited for those in North America
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Reviews summary
Well-received materials and assignments
According to students, all lectures, assignments, and tests in this course are straight forward. However, learners remark that they wish there were more opportunities for extra credit and an optional objective essay final exam.
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 Ideal Diamonds over Good Diamonds using a Random Forest using R with these
activities:
Review data analysis fundamentals
Show steps
Ensure a strong foundation in data analysis before beginning the course.
Browse courses on
Data Analysis
Show steps
-
Review materials on data visualization, data cleaning, and statistical analysis.
-
Practice using data analysis tools, such as R or Python.
Read Hands-On Machine Learning with R
Show steps
Reviewing this book will provide a deeper understanding of machine learning and data analysis.
View
Hands-On Machine Learning with R
on Amazon
Show steps
-
Obtain a copy of the book.
-
Read the book and take notes on key concepts.
Complete practice exercises
Show steps
Practice exercises will solidify learning and increase comfort with the material.
Browse courses on
Machine Learning
Show steps
-
Download the practice exercises.
-
Complete the practice exercises using R and ggplot2.
-
Review your results and identify areas for improvement.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Watch tutorials for using Random Forest
Show steps
Solidify understanding of Random Forest by reviewing tutorial videos.
Browse courses on
Random Forest
Show steps
-
Find tutorials on Random Forest.
-
Watch tutorials and follow along with examples.
-
Attempt to implement Random Forest on a dataset.
Join study groups and engage in discussions
Show steps
Facilitate understanding through discussions and shared perspectives.
Browse courses on
Machine Learning
Show steps
-
Join study groups or online forums.
-
Participate in discussions and ask questions.
-
Share your knowledge and assist other learners.
Build a Random Forest model
Show steps
Applying knowledge by building a Random Forest model will enhance understanding and retention.
Browse courses on
Random Forest
Show steps
-
Gather a dataset appropriate for Random Forest.
-
Preprocess and explore the data.
-
Train and evaluate a Random Forest model.
-
Interpret the results and make predictions.
Attend workshops on Random Forest
Show steps
Workshops provide opportunities to engage with experts and gain practical experience.
Browse courses on
Random Forest
Show steps
-
Research and identify relevant workshops.
-
Register for and attend workshops.
-
Actively participate in discussions and exercises.
Review data analysis fundamentals
Show steps
Ensure a strong foundation in data analysis before beginning the course.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Review materials on data visualization, data cleaning, and statistical analysis.
- Practice using data analysis tools, such as R or Python.
Read Hands-On Machine Learning with R
Show steps
Reviewing this book will provide a deeper understanding of machine learning and data analysis.
View
Hands-On Machine Learning with R
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book.
- Read the book and take notes on key concepts.
Complete practice exercises
Show steps
Practice exercises will solidify learning and increase comfort with the material.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Download the practice exercises.
- Complete the practice exercises using R and ggplot2.
- Review your results and identify areas for improvement.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Watch tutorials for using Random Forest
Show steps
Solidify understanding of Random Forest by reviewing tutorial videos.
Browse courses on
Random Forest
Show steps
Show steps
Show steps
- Find tutorials on Random Forest.
- Watch tutorials and follow along with examples.
- Attempt to implement Random Forest on a dataset.
Join study groups and engage in discussions
Show steps
Facilitate understanding through discussions and shared perspectives.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Join study groups or online forums.
- Participate in discussions and ask questions.
- Share your knowledge and assist other learners.
Build a Random Forest model
Show steps
Applying knowledge by building a Random Forest model will enhance understanding and retention.
Browse courses on
Random Forest
Show steps
Show steps
Show steps
- Gather a dataset appropriate for Random Forest.
- Preprocess and explore the data.
- Train and evaluate a Random Forest model.
- Interpret the results and make predictions.
Attend workshops on Random Forest
Show steps
Workshops provide opportunities to engage with experts and gain practical experience.
Browse courses on
Random Forest
Show steps
Show steps
Show steps
- Research and identify relevant workshops.
- Register for and attend workshops.
- Actively participate in discussions and exercises.
Career center
Learners who complete Predict Ideal Diamonds over Good Diamonds using 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 expertise in data analysis, modeling, and machine learning to extract insights from data and solve complex business problems. Data Scientists work in various industries, including finance, healthcare, technology, and retail, and are in high demand due to the increasing volume and importance of data in today's world. This course will provide aspiring Data Scientists with a solid foundation in data analysis and R programming, giving them a competitive edge in the job market.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning models to solve complex business problems. These models can automate tasks, improve decision-making, and create new products and services. Machine Learning Engineers must have a strong foundation in data science, modeling, and R programming, which makes this course a valuable addition to their skill set.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematics, statistics, and modeling to solve financial problems. They work in the financial industry, hedge funds, and investment banks, and are responsible for developing and implementing investment strategies. This course will help aspiring Quantitative Analysts to build a strong foundation in data analysis, modeling, and R programming. These skills are essential for success in the financial industry, and will help aspiring Quantitative Analysts to stand out in the job market.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make predictions. They work in a variety of industries, including medicine, education, and business. This course will help aspiring Statisticians to build a strong foundation in data analysis and modeling. These skills are essential for success in the statistics field, and will help aspiring Statisticians to stand out in the job market.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of data analysis and modeling to evaluate financial performance and make investment recommendations. They work with portfolio managers, investors, and other stakeholders to develop and implement investment strategies. This course will help aspiring Financial Analysts to build a strong foundation in data analysis and modeling. These skills are essential for success in the financial analysis field, and will help aspiring Financial Analysts to stand out in the job market.
Data Architect
Data Architect
Data Architects design and build data management systems. They work with data analysts, engineers, and other stakeholders to ensure that data is stored, managed, and used effectively. This course will help aspiring Data Architects to build a strong foundation in data analysis and modeling. These skills are essential for success in the data management field, and will help aspiring Data Architects to stand out in the job market.
Data Analyst
Data Analyst
A Data Analyst uses their knowledge of data collection, organization, and analysis to identify trends, solve business problems, and inform decisions for an organization or company. Businesses today use vast amounts of data to inform their strategy and planning, making Data Analysts in high demand. This course, "Predict Ideal Diamonds over Good Diamonds using a Random Forest using R," would be particularly useful for aspiring Data Analysts, as it will help build a foundation in data analysis and modeling techniques using R.
Risk Analyst
Risk Analyst
Risk Analysts use data analysis and modeling to assess and manage risk. They work in a variety of industries, including finance, insurance, and healthcare. This course will help aspiring Risk Analysts to build a strong foundation in data analysis, modeling, and R programming. These skills are essential for success in the risk management industry, and will help aspiring Risk Analysts to stand out in the job market.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines that collect, process, and store data. They work with data analysts, scientists, and other stakeholders to ensure that data is available and accessible for analysis. This course will help aspiring Data Engineers to build a strong foundation in data analysis and modeling. These skills are essential for success in the data engineering field, and will help aspiring Data Engineers to stand out in the job market.
Actuary
Actuary
Actuaries use their knowledge of mathematics, statistics, and modeling to assess risk and uncertainty. They work with insurance companies, pension funds, and other financial institutions to develop and implement risk management strategies. This course will help aspiring Actuaries to build a strong foundation in data analysis and modeling. These skills are essential for success in the actuarial field, and will help aspiring Actuaries to stand out in the job market.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business processes and data analysis to help organizations identify and solve problems, improve efficiency, and make better decisions. The skills taught in this course, such as data analysis, modeling, and R programming, are highly valuable for Business Analysts, and will help them to stand out in the job market.
Customer Analyst
Customer Analyst
Customer Analysts use their knowledge of data analysis and modeling to understand customer behavior and identify trends. They work with marketers, product managers, and other stakeholders to develop and implement strategies to improve customer satisfaction and loyalty. This course will help aspiring Customer Analysts to build a strong foundation in data analysis and modeling. These skills are essential for success in the customer analysis field, and will help aspiring Customer Analysts to stand out in the job market.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They ensure that data is stored, managed, and used effectively. This course will help aspiring Database Administrators to build a strong foundation in data analysis and modeling. These skills are essential for success in the database administration field, and will help aspiring Database Administrators to stand out in the job market.
Market Researcher
Market Researcher
Market Researchers conduct research to understand the needs and wants of customers. They use a variety of methods, including surveys, interviews, and data analysis, to collect and analyze data about consumer behavior. This course will help aspiring Market Researchers to build a strong foundation in data analysis and R programming. These skills are essential for success in the market research industry, and will help aspiring Market Researchers to stand out in the job market.
Software Developer
Software Developer
Software Developers design, build, and maintain software applications. They use a variety of programming languages and tools to create software that meets the needs of users. This course will help aspiring Software Developers to build a strong foundation in R programming. R is a popular programming language for data analysis and modeling, and is widely used in the software industry. The knowledge and skills gained in this course will make aspiring Software Developers more competitive in the job market.
For more career information including salaries, visit:
OpenCourser.com/course/eus2uv/predict
Reading list
We've selected eight 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 Ideal Diamonds over Good Diamonds using a Random Forest using R.
Provides a practical introduction to machine learning using R. It covers the basics of machine learning, as well as more advanced topics such as deep learning and natural language processing.
Save
Provides a comprehensive overview of the R programming language, with a focus on data science applications. It covers a wide range of topics, including data manipulation, visualization, and modeling.
Provides a comprehensive overview of statistical learning methods, with a focus on theoretical foundations. It covers a wide range of topics, including linear regression, classification, and clustering.
Provides a practical introduction to deep learning using R. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks and recurrent neural networks.
Provides a practical introduction to machine learning using Python. It covers the basics of machine learning, as well as more advanced topics such as deep learning and natural language processing.
Provides a comprehensive overview of statistical learning methods, with a focus on practical applications. It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection.
Provides a practical introduction to data science for business applications. It covers a wide range of topics, including data management, visualization, and modeling.
Provides a practical introduction to machine learning using Python. It covers a wide range of topics, including data preprocessing, model selection, and evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/eus2uv/predict
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Effort
2 Hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Chris Shockley
Language
English
Good to know
Course duration is just 1 hour
Requires no prior programming knowledge
Best suited for those in North America
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