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Ikechukwu Nigel Ogbuchi
In this 90 minutes long project-based course, you will learn how to create a Microsoft Azure ML Studio account, a Kaggle account for competitions and use both of them to build a machine learning model which we will be using to make predictions.
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/gvht6u/how
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches data science using Microsoft's Azure and Kaggle, which are industry standard
Builds hands-on experience, which is useful in personal growth and development
Appears to focus on a single aspect of machine learning, which may limit the breadth of knowledge acquired by learners
Assumes proficiency in Azure and Kaggle, which may be a barrier to learners who are not familiar with these tools
Targeted towards learners in North America, which may be inconvenient for learners in other regions
May not be suitable for complete beginners in machine learning, as it assumes some prior knowledge
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Reviews summary
Ml studio for kaggle
This course is well reviewed and well received, especially for users who are located in the North American region. Learners say that it provides a good hands-on experience for building machine learning models for Kaggle competitions.
Great hands-on learning experience.
"Build Machine Learning Models without writing code for Kaggle competitions."
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 How to Use Microsoft Azure ML Studio for Kaggle Competitions with these
activities:
Find a mentor in the field of machine learning
Show steps
Finding a mentor in the field of machine learning will give you access to valuable guidance and support.
Browse courses on
Machine Learning
Show steps
-
Attend industry events and conferences.
-
Network with professionals on LinkedIn.
-
Reach out to professors or researchers in your field.
Review linear algebra and calculus concepts
Show steps
Review the essential concepts of linear algebra and calculus to strengthen your foundation for understanding machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
-
Read textbooks or notes on linear algebra and calculus
-
Solve practice problems
Brush up on Python programming
Show steps
Review the basics of Python programming to ensure you have a solid foundation for working with data science tools and libraries.
Browse courses on
Python
Show steps
-
Review Python syntax and data structures
-
Practice writing simple Python programs
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Review basic programming concepts
Show steps
Reviewing basic programming concepts will help you refresh your memory and prepare for the course material.
Browse courses on
Algorithm Development
Show steps
-
Read through your old notes or textbooks from previous programming courses.
-
Take practice quizzes or coding challenges online.
-
Complete coding tutorials or exercises.
Create a collection of resources on machine learning
Show steps
Compile a collection of resources on machine learning, such as articles, tutorials, videos, and tools, to support your learning and reference.
Show steps
-
Search for and identify relevant resources on machine learning
-
Organize and store the resources in a central location
Mentor other students in the course
Show steps
Mentoring other students will help you solidify your understanding of the course material and improve your communication skills.
Browse courses on
Machine Learning
Show steps
-
Join the course discussion forums.
-
Offer help to other students who are struggling with the material.
-
Organize study groups or review sessions.
Follow online tutorials on machine learning
Show steps
Following online tutorials on machine learning will give you a deeper understanding of the concepts and techniques covered in the course.
Browse courses on
Machine Learning
Show steps
-
Find online tutorials that cover the topics you are learning in the course.
-
Follow the tutorials step-by-step and complete the exercises.
-
Take notes and ask questions in the discussion forums.
Complete the Kaggle tutorial on machine learning
Show steps
Follow the Kaggle tutorial on machine learning to practice building and evaluating machine learning models.
Show steps
-
Sign up for a Kaggle account
-
Enroll in the Kaggle tutorial on machine learning
-
Complete the tutorial modules
Attend a workshop on Azure ML Studio
Show steps
Attend a workshop on Azure ML Studio to learn about its features and how to use it for building and deploying machine learning models.
Browse courses on
Azure ML Studio
Show steps
-
Identify a relevant workshop on Azure ML Studio
-
Register for and attend the workshop
Solve practice problems on Kaggle
Show steps
Solving practice problems on Kaggle will help you apply your machine learning skills and improve your problem-solving abilities.
Browse courses on
Machine Learning
Show steps
-
Create an account on Kaggle.
-
Find practice problems that are related to the course topics.
-
Submit your solutions and compare them to others.
Participate in a Kaggle competition
Show steps
Test your skills and knowledge by participating in a Kaggle competition, where you can build and evaluate machine learning models against other participants.
Show steps
-
Choose a Kaggle competition that aligns with your interests
-
Download the competition data
-
Build and evaluate a machine learning model
-
Submit your model to the competition
Build a machine learning model using Azure ML Studio
Show steps
Building a machine learning model using Azure ML Studio will give you hands-on experience with the tools and techniques used in the course.
Browse courses on
Machine Learning
Show steps
-
Create an account on Azure ML Studio.
-
Follow the course instructions to build a machine learning model.
-
Test your model and make predictions.
Mentor junior learners in machine learning
Show steps
Share your knowledge and experience by mentoring junior learners in machine learning, providing guidance and support to help them succeed.
Show steps
-
Identify a platform or organization where you can mentor junior learners
-
Prepare materials and resources to support your mentees
-
Connect with and provide guidance to your mentees
Participate in machine learning competitions
Show steps
Participating in machine learning competitions will challenge you to apply your skills and learn from others.
Browse courses on
Machine Learning
Show steps
-
Find machine learning competitions that are related to the course topics.
-
Team up with other students or participate individually.
-
Submit your solutions and track your progress.
Build a portfolio of machine learning projects
Show steps
Create a portfolio of machine learning projects to showcase your skills and knowledge to potential employers or clients.
Show steps
-
Identify a problem statement that you can solve with machine learning
-
Collect and prepare data
-
Build and evaluate a machine learning model
-
Create a presentation or documentation to showcase your project
Find a mentor in the field of machine learning
Show steps
Finding a mentor in the field of machine learning will give you access to valuable guidance and support.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Attend industry events and conferences.
- Network with professionals on LinkedIn.
- Reach out to professors or researchers in your field.
Review linear algebra and calculus concepts
Show steps
Review the essential concepts of linear algebra and calculus to strengthen your foundation for understanding machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Read textbooks or notes on linear algebra and calculus
- Solve practice problems
Brush up on Python programming
Show steps
Review the basics of Python programming to ensure you have a solid foundation for working with data science tools and libraries.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python syntax and data structures
- Practice writing simple Python programs
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Review basic programming concepts
Show steps
Reviewing basic programming concepts will help you refresh your memory and prepare for the course material.
Browse courses on
Algorithm Development
Show steps
Show steps
Show steps
- Read through your old notes or textbooks from previous programming courses.
- Take practice quizzes or coding challenges online.
- Complete coding tutorials or exercises.
Create a collection of resources on machine learning
Show steps
Compile a collection of resources on machine learning, such as articles, tutorials, videos, and tools, to support your learning and reference.
Show steps
Show steps
Show steps
- Search for and identify relevant resources on machine learning
- Organize and store the resources in a central location
Mentor other students in the course
Show steps
Mentoring other students will help you solidify your understanding of the course material and improve your communication skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Join the course discussion forums.
- Offer help to other students who are struggling with the material.
- Organize study groups or review sessions.
Follow online tutorials on machine learning
Show steps
Following online tutorials on machine learning will give you a deeper understanding of the concepts and techniques covered in the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find online tutorials that cover the topics you are learning in the course.
- Follow the tutorials step-by-step and complete the exercises.
- Take notes and ask questions in the discussion forums.
Complete the Kaggle tutorial on machine learning
Show steps
Follow the Kaggle tutorial on machine learning to practice building and evaluating machine learning models.
Show steps
Show steps
Show steps
- Sign up for a Kaggle account
- Enroll in the Kaggle tutorial on machine learning
- Complete the tutorial modules
Attend a workshop on Azure ML Studio
Show steps
Attend a workshop on Azure ML Studio to learn about its features and how to use it for building and deploying machine learning models.
Browse courses on
Azure ML Studio
Show steps
Show steps
Show steps
- Identify a relevant workshop on Azure ML Studio
- Register for and attend the workshop
Solve practice problems on Kaggle
Show steps
Solving practice problems on Kaggle will help you apply your machine learning skills and improve your problem-solving abilities.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Create an account on Kaggle.
- Find practice problems that are related to the course topics.
- Submit your solutions and compare them to others.
Participate in a Kaggle competition
Show steps
Test your skills and knowledge by participating in a Kaggle competition, where you can build and evaluate machine learning models against other participants.
Show steps
Show steps
Show steps
- Choose a Kaggle competition that aligns with your interests
- Download the competition data
- Build and evaluate a machine learning model
- Submit your model to the competition
Build a machine learning model using Azure ML Studio
Show steps
Building a machine learning model using Azure ML Studio will give you hands-on experience with the tools and techniques used in the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Create an account on Azure ML Studio.
- Follow the course instructions to build a machine learning model.
- Test your model and make predictions.
Mentor junior learners in machine learning
Show steps
Share your knowledge and experience by mentoring junior learners in machine learning, providing guidance and support to help them succeed.
Show steps
Show steps
Show steps
- Identify a platform or organization where you can mentor junior learners
- Prepare materials and resources to support your mentees
- Connect with and provide guidance to your mentees
Participate in machine learning competitions
Show steps
Participating in machine learning competitions will challenge you to apply your skills and learn from others.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find machine learning competitions that are related to the course topics.
- Team up with other students or participate individually.
- Submit your solutions and track your progress.
Build a portfolio of machine learning projects
Show steps
Create a portfolio of machine learning projects to showcase your skills and knowledge to potential employers or clients.
Show steps
Show steps
Show steps
- Identify a problem statement that you can solve with machine learning
- Collect and prepare data
- Build and evaluate a machine learning model
- Create a presentation or documentation to showcase your project
Career center
Learners who complete How to Use Microsoft Azure ML Studio for Kaggle Competitions will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist helps build and develop machine learning models, the core of this course. This course will help you begin to advance your career in this field. The course will get you familiar with Microsoft Azure ML Studio and how to use it for machine learning competitions on Kaggle, a popular platform for such competitions. As you progress in your career, additional courses may cover more complex aspects of machine learning, as well as other software and platforms.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer focuses on building, testing, and deploying machine learning models, skills you will use regularly in this course. After completing this course and others like it, you will have a firm foundation to continue developing your career in machine learning and engineering.
Data Analyst
Data Analyst
Data Analysts build and improve data collection and analysis processes to support decision-making, often in a data-driven context. This course will help you gain familiarity with Microsoft Azure ML Studio and how to develop machine learning models which may be useful to you in this role.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts leverage statistical models to analyze financial data and make trading decisions. This course will help you build a foundation for using machine learning, including the type you will build in this course, in financial trading.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. The skills and knowledge you will learn in this course may be helpful as you progress in this career, especially if you work on machine learning-related projects.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems and infrastructure. This course may be helpful as you begin or progress in this career by introducing you to Microsoft Azure ML Studio, which you may use on the job.
Business Analyst
Business Analyst
Business Analysts analyze business processes and develop solutions to improve efficiency. This course may be helpful to you in this career by introducing you to machine learning, which may be used in the solutions you propose.
Market Researcher
Market Researcher
Market Researchers analyze market trends and customer behavior. This course may be helpful in this career by providing an introduction to machine learning, including building a machine learning model from start to finish.
Financial Analyst
Financial Analyst
Financial Analysts provide advice and guidance on financial matters. This course may be helpful by introducing you to machine learning, which is used in financial analysis in various ways.
Product Manager
Product Manager
Product Managers oversee the development and launch of products. This course may be helpful in this career by introducing you to machine learning, which may be used in developing or improving products.
Consultant
Consultant
Consultants provide advice and guidance on a variety of topics. This course may be helpful if you are interested in consulting in a field related to machine learning, or if you wish to use machine learning to improve your consulting projects.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course may be helpful by introducing you to machine learning, which uses statistics in its algorithms and models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to improve organizational efficiency. This course may be helpful in this career by introducing you to machine learning, which may be used in optimization and modeling.
Actuary
Actuary
Actuaries analyze and manage financial risks. This course may be helpful by providing an introduction to machine learning, which is used in risk assessment and modeling.
Economist
Economist
Economists study and analyze economic data. This course may be helpful by providing an introduction to machine learning, which is used in economic modeling and forecasting.
For more career information including salaries, visit:
OpenCourser.com/course/gvht6u/how
Reading list
We've selected ten 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
How to Use Microsoft Azure ML Studio for Kaggle Competitions.
Provides a comprehensive overview of statistical learning methods, covering topics such as linear regression, logistic regression, decision trees, and support vector machines. It valuable resource for learners who want to gain a deeper understanding of the statistical foundations of machine learning.
Provides a comprehensive overview of machine learning from a Bayesian and optimization perspective. It covers topics such as statistical inference, Bayesian modeling, and optimization methods. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of machine learning.
Teaches the fundamental concepts of data science from scratch, covering topics such as data exploration, data cleaning, feature engineering, and modeling. It valuable resource for learners who are new to data science and want to gain a solid foundation in the field.
Provides a hands-on introduction to machine learning using Python. It covers topics such as data preprocessing, model selection, and model evaluation. It valuable resource for learners who want to gain practical experience in machine learning.
Provides a beginner-friendly introduction to machine learning. It covers topics such as the basics of machine learning, data preprocessing, and model training. It valuable resource for learners who are new to machine learning and want to gain a solid foundation.
Provides a comprehensive introduction to machine learning using R. It covers topics such as data preparation, model training, and model evaluation. It valuable resource for learners who want to gain proficiency in using R for machine learning tasks.
Provides a comprehensive introduction to Python for data analysis. It covers topics such as data manipulation, data visualization, and statistical modeling. It valuable resource for learners who want to gain proficiency in using Python for data analysis tasks.
Introduces the core concepts of machine learning using TensorFlow. It covers topics such as neural networks, deep learning, and image processing. It valuable resource for learners who want to explore the field of machine learning using TensorFlow.
Introduces the core concepts of deep learning and provides practical guidance on building and training deep learning models using Python. It covers topics such as convolutional neural networks, recurrent neural networks, and image processing. It valuable resource for learners who want to explore the field of deep learning.
Provides a non-technical introduction to artificial intelligence. It covers topics such as the history of AI, the different types of AI, and the ethical implications of AI. It valuable resource for learners who want to gain a general understanding of AI.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gvht6u/how
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Ikechukwu Nigel Ogbuchi
Good to know
Teaches data science using Microsoft's Azure and Kaggle, which are industry standard
Builds hands-on experience, which is useful in personal growth and development
Appears to focus on a single aspect of machine learning, which may limit the breadth of knowledge acquired by learners
Assumes proficiency in Azure and Kaggle, which may be a barrier to learners who are not familiar with these tools
Targeted towards learners in North America, which may be inconvenient for learners in other regions
May not be suitable for complete beginners in machine learning, as it assumes some prior knowledge
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