In this project-based course, you will use the Multiclass Neural Network module in Azure Machine Learning Studio to train a neural network to recognize handwritten digits. Microsoft Azure Machine Learning Studio is a drag-and-drop tool you can use to rapidly build and deploy machine learning models on Azure. The data used in this course is the popular MNIST data set consisting of 70,000 grayscale images of hand-written digits. You are going to deploy the trained neural network model as an Azure Web service. Azure Web Services provide an interface between an application and a Machine Learning Studio workflow scoring model. You will write a Python application to use the Batch Execution Service and predict the class labels of handwritten digits.
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In this project-based course, you will use the Multiclass Neural Network module in Azure Machine Learning Studio to train a neural network to recognize handwritten digits. Microsoft Azure Machine Learning Studio is a drag-and-drop tool you can use to rapidly build and deploy machine learning models on Azure. The data used in this course is the popular MNIST data set consisting of 70,000 grayscale images of hand-written digits. You are going to deploy the trained neural network model as an Azure Web service. Azure Web Services provide an interface between an application and a Machine Learning Studio workflow scoring model. You will write a Python application to use the Batch Execution Service and predict the class labels of handwritten digits.
In this project-based course, you will use the Multiclass Neural Network module in Azure Machine Learning Studio to train a neural network to recognize handwritten digits. Microsoft Azure Machine Learning Studio is a drag-and-drop tool you can use to rapidly build and deploy machine learning models on Azure. The data used in this course is the popular MNIST data set consisting of 70,000 grayscale images of hand-written digits. You are going to deploy the trained neural network model as an Azure Web service. Azure Web Services provide an interface between an application and a Machine Learning Studio workflow scoring model. You will write a Python application to use the Batch Execution Service and predict the class labels of handwritten digits.
This is the third 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.
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Activities
Check Basic ML Terminology
Show steps
Refresh the student's understanding of fundamental machine learning terminology, ensuring a common ground for the course.
Browse courses on
Azure ML Studio
Show steps
-
Review key concepts such as supervised learning, unsupervised learning, and model evaluation.
-
Complete online quizzes or exercises to test their understanding.
Emphasize the Role of Neural Networks in Machine Learning
Show steps
Reinforce the importance of neural networks in the field of machine learning, setting the stage for the course's focus on Azure ML Studio's capabilities.
Browse courses on
Neural Networks
Show steps
-
Describe the historical evolution of neural networks and their impact on machine learning.
-
Provide real-world examples of how neural networks have revolutionized various industries.
Attend an Azure Workshop
Show steps
Offer hands-on experience with Azure ML Studio, allowing students to apply their knowledge and gain proficiency in the tool.
Browse courses on
Azure ML Studio
Show steps
-
Identify and register for an Azure workshop focused on machine learning or neural networks.
-
Attend the workshop and actively participate in hands-on exercises.
Five other activities
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Explore Practical Applications of Neural Networks
Show steps
Provide a deeper dive into practical applications of neural networks, supplementing the course's focus on Azure ML Studio with real-world examples.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read selected chapters or sections of the book to gain insights into specific applications.
-
Work through code examples and exercises provided in the book to implement neural networks for different tasks.
Participate in a Machine Learning Challenge
Show steps
Challenge students to apply their skills in a competitive environment, fostering problem-solving abilities and driving deeper engagement.
Show steps
-
Identify and register for a machine learning competition on platforms like Kaggle.
-
Work through the challenge, using the techniques and knowledge gained in the course.
Organize Acquired Knowledge
Show steps
Facilitate the student's ability to retain and recall information by encouraging active organization and review of course materials.
Show steps
-
Create a dedicated notebook or document to compile notes, assignments, and quizzes.
-
Review and summarize key concepts regularly to reinforce understanding.
-
Utilize flashcards or online tools for active recall and spaced repetition.
Demonstrate Hands-on Implementation
Show steps
Assess students' ability to apply their knowledge by creating a deliverable that showcases their proficiency in using Azure ML Studio and Python.
Browse courses on
Python
Show steps
-
Develop a machine learning model or application using Azure ML Studio and Python.
-
Write a report or create a presentation to document the process, results, and insights.
Contribute to Open-Source Machine Learning Projects
Show steps
Encourage students to engage with the broader machine learning community by contributing to open-source projects, fostering collaboration and exposure to real-world applications.
Browse courses on
GitHub
Show steps
-
Identify open-source machine learning projects or initiatives on platforms like GitHub.
-
Contribute to the project by submitting code, reporting issues, or providing documentation.
Check Basic ML Terminology
Show steps
Refresh the student's understanding of fundamental machine learning terminology, ensuring a common ground for the course.
Browse courses on
Azure ML Studio
Show steps
- Review key concepts such as supervised learning, unsupervised learning, and model evaluation.
- Complete online quizzes or exercises to test their understanding.
Emphasize the Role of Neural Networks in Machine Learning
Show steps
Reinforce the importance of neural networks in the field of machine learning, setting the stage for the course's focus on Azure ML Studio's capabilities.
Browse courses on
Neural Networks
Show steps
- Describe the historical evolution of neural networks and their impact on machine learning.
- Provide real-world examples of how neural networks have revolutionized various industries.
Attend an Azure Workshop
Show steps
Offer hands-on experience with Azure ML Studio, allowing students to apply their knowledge and gain proficiency in the tool.
Browse courses on
Azure ML Studio
Show steps
- Identify and register for an Azure workshop focused on machine learning or neural networks.
- Attend the workshop and actively participate in hands-on exercises.
Explore Practical Applications of Neural Networks
Show steps
Provide a deeper dive into practical applications of neural networks, supplementing the course's focus on Azure ML Studio with real-world examples.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
- Read selected chapters or sections of the book to gain insights into specific applications.
- Work through code examples and exercises provided in the book to implement neural networks for different tasks.
Participate in a Machine Learning Challenge
Show steps
Challenge students to apply their skills in a competitive environment, fostering problem-solving abilities and driving deeper engagement.
Show steps
- Identify and register for a machine learning competition on platforms like Kaggle.
- Work through the challenge, using the techniques and knowledge gained in the course.
Organize Acquired Knowledge
Show steps
Facilitate the student's ability to retain and recall information by encouraging active organization and review of course materials.
Show steps
- Create a dedicated notebook or document to compile notes, assignments, and quizzes.
- Review and summarize key concepts regularly to reinforce understanding.
- Utilize flashcards or online tools for active recall and spaced repetition.
Demonstrate Hands-on Implementation
Show steps
Assess students' ability to apply their knowledge by creating a deliverable that showcases their proficiency in using Azure ML Studio and Python.
Browse courses on
Python
Show steps
- Develop a machine learning model or application using Azure ML Studio and Python.
- Write a report or create a presentation to document the process, results, and insights.
Contribute to Open-Source Machine Learning Projects
Show steps
Encourage students to engage with the broader machine learning community by contributing to open-source projects, fostering collaboration and exposure to real-world applications.
Browse courses on
GitHub
Show steps
- Identify open-source machine learning projects or initiatives on platforms like GitHub.
- Contribute to the project by submitting code, reporting issues, or providing documentation.
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