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Snehan Kekre
In this project-based course you will learn to perform feature engineering and create custom R models on Azure ML Studio, all without writing a single line of code! You will build a Random Forests model in Azure ML Studio using the R programming language. The data to be used in this course is the Bike Sharing Dataset. The dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information. Using the information from the dataset, you can build a model to predict the number of bikes rented during certain weather conditions. You will leverage the Execute R Script and Create R Model modules to run R scripts from the Azure ML Studio experiment perform feature engineering.
Read more
In this project-based course you will learn to perform feature engineering and create custom R models on Azure ML Studio, all without writing a single line of code! You will build a Random Forests model in Azure ML Studio using the R programming language. The data to be used in this course is the Bike Sharing Dataset. The dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information. Using the information from the dataset, you can build a model to predict the number of bikes rented during certain weather conditions. You will leverage the Execute R Script and Create R Model modules to run R scripts from the Azure ML Studio experiment perform feature engineering.
Read more
In this project-based course you will learn to perform feature engineering and create custom R models on Azure ML Studio, all without writing a single line of code! You will build a Random Forests model in Azure ML Studio using the R programming language. The data to be used in this course is the Bike Sharing Dataset. The dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information. Using the information from the dataset, you can build a model to predict the number of bikes rented during certain weather conditions. You will leverage the Execute R Script and Create R Model modules to run R scripts from the Azure ML Studio experiment perform feature engineering.
This is the fourth course in this series on building machine learning applications using Azure Machine Learning Studio. I highly encourage you to take the first course before proceeding. It has instructions on how to set up your Azure ML account with $200 worth of free credit to get started with running your experiments!
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- 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/ln3vc4/build
What's inside
Syllabus
Project: Build Random Forests in R with Azure ML Studio
Welcome to this project-based course on Azure Machine Learning Studio. In this course, you will learn to perform feature engineering and create custom R models on Azure ML Studio, all without writing a single line of code! You will build a Random Forest model in Azure ML Studio using the R programming language. The data to be used in this course is the Bike Sharing Dataset. The dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information. Using the information from the dataset, you can build a model to predict the number of bikes rented during certain weather conditions. You will leverage the Execute R Script and Create R Model modules to run R scripts from the Azure ML Studio experiment perform feature engineering.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Learners interested in exploring machine learning applications via Azure Machine Learning may find this relevant
This course presumes familiarity with Azure ML Studio, without which learners may face difficulties
Snehan Kekre, the instructor, is not prominently known for their expertise in machine learning Studio
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Reviews summary
Azure ml studio random forest course
Learners say this course is good, easy to follow, and useful for learning R in Azure ML studio.
The course is hands on.
"An easy to follow, hands on to using R in Azure ML studio."
The course is highly rated.
"GOOD"
"good"
"good"
"good "
"good"
"good"
"best one"
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 Build Random Forests in R with Azure ML Studio with these
activities:
Read Introduction to Machine Learning with R
Show steps
Gain a foundational understanding of machine learning concepts, algorithms, and techniques.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read chapters 1-3 to understand the basics of machine learning.
-
Work through the exercises in chapters 1-3.
-
Complete the practice quiz at the end of each chapter.
Attend a Machine Learning Meetup
Show steps
Connect with other machine learning enthusiasts.
Browse courses on
Machine Learning
Show steps
-
Find a local machine learning meetup group.
-
Attend a meetup and introduce yourself to others.
-
Participate in discussions and share your knowledge.
Build a Random Forest Model from Scratch in R
Show steps
Apply your machine learning skills to build a real-world predictive model.
Browse courses on
Random Forests
Show steps
-
Gather data on a topic of your interest.
-
Explore and clean the data.
-
Build a Random Forest model using the data you collected.
-
Evaluate the performance of your model.
-
Write a report summarizing your findings.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve LeetCode Problems on Machine Learning
Show steps
Strengthen your problem-solving skills in machine learning.
Browse courses on
Machine Learning
Show steps
-
Choose a set of LeetCode problems related to machine learning.
-
Attempt to solve the problems on your own.
-
Review your solutions and identify areas where you can improve.
Follow Tutorials on Advanced Machine Learning Techniques
Show steps
Expand your knowledge of machine learning by learning new techniques.
Browse courses on
Machine Learning
Show steps
-
Identify specific machine learning techniques you want to learn.
-
Find high-quality tutorials or courses on those techniques.
-
Follow the tutorials and complete the exercises.
Attend a Machine Learning Workshop
Show steps
Gain hands-on experience with machine learning tools and techniques.
Browse courses on
Machine Learning
Show steps
-
Find a machine learning workshop that aligns with your interests.
-
Attend the workshop and participate in the activities.
-
Apply what you learned in the workshop to your own projects.
Create a Blog Post on Machine Learning with R
Show steps
Reinforce your understanding of machine learning by explaining concepts to others.
Browse courses on
Machine Learning
Show steps
-
Choose a specific machine learning topic to write about.
-
Research the topic to gather information and examples.
-
Write a clear and concise blog post explaining the topic.
-
Publish your blog post and share it with others.
Develop a Machine Learning Application
Show steps
Apply your machine learning skills to solve a real-world problem.
Browse courses on
Machine Learning
Show steps
-
Identify a problem that can be solved using machine learning.
-
Gather and prepare data to train your model.
-
Develop a machine learning model to solve the problem.
-
Deploy your model as an application.
Read Introduction to Machine Learning with R
Show steps
Gain a foundational understanding of machine learning concepts, algorithms, and techniques.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 to understand the basics of machine learning.
- Work through the exercises in chapters 1-3.
- Complete the practice quiz at the end of each chapter.
Attend a Machine Learning Meetup
Show steps
Connect with other machine learning enthusiasts.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a local machine learning meetup group.
- Attend a meetup and introduce yourself to others.
- Participate in discussions and share your knowledge.
Build a Random Forest Model from Scratch in R
Show steps
Apply your machine learning skills to build a real-world predictive model.
Browse courses on
Random Forests
Show steps
Show steps
Show steps
- Gather data on a topic of your interest.
- Explore and clean the data.
- Build a Random Forest model using the data you collected.
- Evaluate the performance of your model.
- Write a report summarizing your findings.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve LeetCode Problems on Machine Learning
Show steps
Strengthen your problem-solving skills in machine learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a set of LeetCode problems related to machine learning.
- Attempt to solve the problems on your own.
- Review your solutions and identify areas where you can improve.
Follow Tutorials on Advanced Machine Learning Techniques
Show steps
Expand your knowledge of machine learning by learning new techniques.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify specific machine learning techniques you want to learn.
- Find high-quality tutorials or courses on those techniques.
- Follow the tutorials and complete the exercises.
Attend a Machine Learning Workshop
Show steps
Gain hands-on experience with machine learning tools and techniques.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a machine learning workshop that aligns with your interests.
- Attend the workshop and participate in the activities.
- Apply what you learned in the workshop to your own projects.
Create a Blog Post on Machine Learning with R
Show steps
Reinforce your understanding of machine learning by explaining concepts to others.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a specific machine learning topic to write about.
- Research the topic to gather information and examples.
- Write a clear and concise blog post explaining the topic.
- Publish your blog post and share it with others.
Develop a Machine Learning Application
Show steps
Apply your machine learning skills to solve a real-world problem.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a problem that can be solved using machine learning.
- Gather and prepare data to train your model.
- Develop a machine learning model to solve the problem.
- Deploy your model as an application.
Career center
Learners who complete Build Random Forests in R with Azure ML Studio will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist is a professional who uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. They must be able to interpret the results of their work and communicate their findings to clients and business stakeholders. This course may be useful for aspiring Data Scientists because it will help you build a foundation in data science techniques using the R programming language.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is a professional who develops and deploys machine learning models to solve business problems. They must have a strong understanding of machine learning algorithms, data science techniques, and cloud computing platforms. This course may be useful for aspiring Machine Learning Engineers because it will help you build a foundation in machine learning model development using the R programming language.
Business Analyst
Business Analyst
A Business Analyst is a professional who helps businesses define their business needs and develop solutions to meet those needs. They must have a strong understanding of business processes, data analysis techniques, and project management skills. This course may be useful for aspiring Business Analysts because it will help you build a foundation in data analysis techniques using the R programming language.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is a professional who uses mathematical and statistical models to solve business problems. They must have a strong understanding of operations research techniques, data analysis techniques, and optimization techniques. This course may be useful for aspiring Operations Research Analysts because it will help you build a foundation in data analysis techniques using the R programming language.
Statistician
Statistician
A Statistician is a professional who collects, analyzes, and interprets data to draw conclusions about the world. They must have a strong understanding of statistical methods, data analysis techniques, and probability theory. This course may be useful for aspiring Statisticians because it will help you build a foundation in data analysis techniques using the R programming language.
Data Architect
Data Architect
A Data Architect is a professional who designs and builds data management systems to meet the needs of an organization. They must have a strong understanding of data management principles, data integration techniques, and data governance best practices. This course may be useful for aspiring Data Architects because it will help you build a foundation in data engineering techniques using the R programming language.
Software Engineer
Software Engineer
A Software Engineer is a professional who designs, develops, and maintains software applications. They must have a strong understanding of software development methodologies, programming languages, and data structures. This course may be useful for aspiring Software Engineers because it will help you build a foundation in data science techniques using the R programming language.
Data Analyst
Data Analyst
A Data Analyst is a professional who collects, analyzes, and interprets data to help businesses make informed decisions. They must have a strong understanding of data analysis techniques, data visualization tools, and data management systems. This course may be useful for aspiring Data Analysts because it will help you build a foundation in data analysis techniques using the R programming language.
Database Administrator
Database Administrator
A Database Administrator is a professional who manages and maintains databases to ensure their availability, performance, and security. They must have a strong understanding of database management systems, data backup and recovery techniques, and data security best practices. This course may be useful for aspiring Database Administrators because it will help you build a foundation in data engineering techniques using the R programming language.
Data Engineer
Data Engineer
A Data Engineer is a professional who designs, builds, and maintains data pipelines. They must have a strong understanding of data management systems, data integration tools, and data quality best practices. This course may be useful for aspiring Data Engineers because it will help you build a foundation in data engineering techniques using the R programming language.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is a professional who uses mathematical and statistical models to analyze financial data and make investment decisions. They must have a strong understanding of financial markets, data analysis techniques, and risk management principles. This course may be useful for aspiring Quantitative Analysts because it will help you build a foundation in data analysis techniques using the R programming language.
Actuary
Actuary
An Actuary is a professional who uses mathematical and statistical models to assess risk and uncertainty. They must have a strong understanding of insurance and finance principles, data analysis techniques, and risk management principles. This course may be useful for aspiring Actuaries because it will help you build a foundation in data analysis techniques using the R programming language.
Epidemiologist
Epidemiologist
An Epidemiologist is a professional who studies the distribution and determinants of health-related states or events (including disease), and applies this knowledge to control health problems. They must have a strong understanding of epidemiology principles, data analysis techniques, and public health principles. This course may be useful for aspiring Epidemiologists because it will help you build a foundation in data analysis techniques using the R programming language.
Biostatistician
Biostatistician
A Biostatistician is a professional who applies statistical methods to biological and medical data to solve research questions. They must have a strong understanding of statistical methods, data analysis techniques, and medical principles. This course may be useful for aspiring Biostatisticians because it will help you build a foundation in data analysis techniques using the R programming language.
Market Researcher
Market Researcher
A Market Researcher is a professional who collects, analyzes, and interprets data to understand market trends and consumer behavior. They must have a strong understanding of market research techniques, data analysis techniques, and consumer psychology. This course may be useful for aspiring Market Researchers because it will help you build a foundation in data analysis techniques using the R programming language.
For more career information including salaries, visit:
OpenCourser.com/course/ln3vc4/build
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
Build Random Forests in R with Azure ML Studio.
Save
Comprehensive guide to R for data science. It covers a wide range of topics, from data exploration and preprocessing to model building and evaluation. It valuable resource for anyone interested in learning more about R for data science.
Classic introduction to statistical learning. It covers a wide range of topics, from linear regression and logistic regression to decision trees and support vector machines. It valuable resource for anyone interested in learning more about statistical learning.
Comprehensive guide to natural language processing with Python. It covers a wide range of topics, from data exploration and preprocessing to model building and evaluation. It valuable resource for anyone interested in learning more about natural language processing with Python.
Comprehensive guide to machine learning with R. It covers a wide range of topics, from data preprocessing and feature engineering to model selection and evaluation. It valuable resource for anyone interested in learning more about machine learning with R.
Comprehensive guide to ggplot2. It covers a wide range of topics, from data exploration and preprocessing to model building and evaluation. It valuable resource for anyone interested in learning more about ggplot2.
Comprehensive guide to deep learning with Python. It covers a wide range of topics, from data exploration and preprocessing to model building and evaluation. It valuable resource for anyone interested in learning more about deep learning with Python.
Save
Provides a comprehensive overview of machine learning models and techniques, using R as the programming language. It covers a wide range of topics, from data preprocessing and feature engineering to model selection and evaluation. It valuable resource for anyone interested in learning more about machine learning with R.
Save
Comprehensive guide to advanced R. It covers a wide range of topics, from data exploration and preprocessing to model building and evaluation. It valuable resource for anyone interested in learning more about advanced R.
Save
Practical guide to predictive modeling. It covers a wide range of topics, from data exploration and preprocessing to model building and evaluation. It valuable resource for anyone interested in learning more about predictive modeling.
Comprehensive guide to machine learning with Python. It covers a wide range of topics, from data exploration and preprocessing to model building and evaluation. It valuable resource for anyone interested in learning more about machine learning with Python.
Practical guide to machine learning for hackers. It covers a wide range of topics, from data exploration and preprocessing to model building and evaluation. It great resource for anyone interested in applying machine learning to real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ln3vc4/build
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Learners interested in exploring machine learning applications via Azure Machine Learning may find this relevant
This course presumes familiarity with Azure ML Studio, without which learners may face difficulties
Snehan Kekre, the instructor, is not prominently known for their expertise in machine learning Studio
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