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Parth Dhameliya
In this 1-hour long project-based course, you will learn how to create a digit recognition web application using streamlit. This project is divided into two stages. In the first stage, you are going to write the training pipeline in which you will load MNIST Handwritten dataset. You will write the training and validation functions in order to train and validate the dataset. Lastly, in this stage you will do inference. In the second stage, you will use the best trained model from the training pipeline and you will use that in your web app. You will create the web user interface using streamlit python library. In this web app a user will draw a digit and given that drawn digit, the best trained model will output the probabilities.
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What's inside
Syllabus
Project Overview
In this 1-hour long project-based course, you will learn how to create a digit recognition web application using streamlit. This project is divided into two stages. In the first stage, you are going to write the training pipeline in which you will load MNIST Handwritten dataset. You will write the training and validation functions in order to train and validate the dataset. Lastly, in this stage you will do inference. In the second stage, you will use the best trained model from the training pipeline and you will use that in your web app. You will create the web user interface using streamlit python library. In this web app a user will draw a digit and given that drawn digit, the best trained model will output the probabilities.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for beginners looking to acquire a practical skillset
Covers the fundamentals of deep learning and data science
Emphasizes hands-on learning through projects and tutorials
Instructed by experienced professionals who are active in the field
Part of a series of courses that provide a comprehensive learning path
Requires familiarity with basic python programming concepts
May be too introductory for individuals with prior deep learning experience
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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 Create digit recognition web app with Streamlit with these
activities:
Compile resources on digit recognition techniques
Show steps
Expand your knowledge of digit recognition techniques and algorithms, which will aid you in selecting the most suitable approach for your web application.
Browse courses on
Machine Learning
Show steps
-
Search for articles, tutorials, and research papers on digit recognition.
-
Summarize the key concepts and techniques in a document.
-
Organize the resources into a structured format, such as a table or a list.
Explore the Streamlit documentation for creating web interfaces
Show steps
Gain a deeper understanding of the Streamlit library, enabling you to effectively create the user interface for your web application.
Browse courses on
Web Development
Show steps
-
Visit the Streamlit documentation website.
-
Go through the tutorials on creating basic web pages and adding interactive elements.
Build a simple web application using Streamlit to display a greeting message
Show steps
Apply your knowledge of Streamlit to create a basic web application, which will provide a strong foundation for your digit recognition app.
Browse courses on
Web Development
Show steps
-
Create a new Streamlit app.
-
Add a title and a greeting message to the app.
-
Run the app and verify that it displays the message.
Two other activities
Expand to see all activities and additional details
Show all five activities
Join a study group or online forum for the course
Show steps
Connect with your peers, discuss course concepts, and enhance your understanding through collaborative learning.
Show steps
-
Find a study group or online forum related to the course.
-
Participate in discussions and ask or answer questions.
Create a presentation on the benefits and applications of digit recognition technology
Show steps
Enhance your understanding of the broader context of digit recognition, its benefits, and potential applications, which will add depth to your project.
Browse courses on
Presentation Creation
Show steps
-
Research the benefits and applications of digit recognition technology.
-
Organize the information into a logical structure for a presentation.
-
Create slides using a presentation software tool.
-
Practice delivering the presentation.
Compile resources on digit recognition techniques
Show steps
Expand your knowledge of digit recognition techniques and algorithms, which will aid you in selecting the most suitable approach for your web application.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Search for articles, tutorials, and research papers on digit recognition.
- Summarize the key concepts and techniques in a document.
- Organize the resources into a structured format, such as a table or a list.
Explore the Streamlit documentation for creating web interfaces
Show steps
Gain a deeper understanding of the Streamlit library, enabling you to effectively create the user interface for your web application.
Browse courses on
Web Development
Show steps
Show steps
Show steps
- Visit the Streamlit documentation website.
- Go through the tutorials on creating basic web pages and adding interactive elements.
Build a simple web application using Streamlit to display a greeting message
Show steps
Apply your knowledge of Streamlit to create a basic web application, which will provide a strong foundation for your digit recognition app.
Browse courses on
Web Development
Show steps
Show steps
Show steps
- Create a new Streamlit app.
- Add a title and a greeting message to the app.
- Run the app and verify that it displays the message.
Two other activities
Expand to see all activities and additional details
Show all five activities
Join a study group or online forum for the course
Show steps
Connect with your peers, discuss course concepts, and enhance your understanding through collaborative learning.
Show steps
Show steps
Show steps
- Find a study group or online forum related to the course.
- Participate in discussions and ask or answer questions.
Create a presentation on the benefits and applications of digit recognition technology
Show steps
Enhance your understanding of the broader context of digit recognition, its benefits, and potential applications, which will add depth to your project.
Browse courses on
Presentation Creation
Show steps
Show steps
Show steps
- Research the benefits and applications of digit recognition technology.
- Organize the information into a logical structure for a presentation.
- Create slides using a presentation software tool.
- Practice delivering the presentation.
Career center
Learners who complete Create digit recognition web app with Streamlit will develop knowledge and skills
that may be useful to these careers:
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They work with engineers and scientists to apply machine learning to a variety of problems. This course provides a foundation in machine learning concepts and techniques, as well as hands-on experience in building and deploying a digit recognition model. This experience can be valuable for Machine Learning Researchers who want to develop their skills in model development and deployment.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They work closely with Data Scientists to ensure that models are accurate and efficient. This course provides a foundation in machine learning concepts and techniques, as well as hands-on experience in building and deploying a digit recognition model. This experience can be valuable for Machine Learning Engineers who want to develop their skills in model development and deployment.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use their findings to make informed decisions and solve problems. This course provides a foundation in data analysis techniques, including machine learning, which can be applied to a variety of industries. Additionally, the hands-on project experience in building a digit recognition web application can help Statisticians develop practical skills in data visualization and web development.
Web Developer
Web Developer
Web Developers design, develop, and maintain websites. They use their knowledge of web development languages and tools to create websites that are user-friendly and efficient. This course provides a foundation in web development using the Streamlit library. This experience can be valuable for Web Developers who want to develop their skills in building web applications.
Data Scientist
Data Scientist
Data Scientists analyze data to extract meaningful insights and trends. They use their findings to solve business problems and make informed decisions. This course provides a foundation in data analysis techniques, including machine learning, which can be applied to a variety of industries. Additionally, the hands-on project experience in building a digit recognition web application can help Data Scientists develop practical skills in data visualization and web development.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to extract meaningful insights. They use their findings to identify trends and patterns, and to make recommendations for improving business processes. This course provides a foundation in data analysis techniques, including machine learning, which can be applied to a variety of industries. Additionally, the hands-on project experience in building a digit recognition web application can help Data Analysts develop practical skills in data visualization and web development.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data. They use their knowledge of data visualization techniques to make data more accessible and understandable. This course provides a foundation in data visualization techniques, including web development, which can be valuable for Data Visualization Specialists who want to develop their skills in data visualization and web development.
Data Engineer
Data Engineer
Data Engineers design, develop, and maintain data pipelines. They work with data scientists and analysts to ensure that data is clean, consistent, and accessible. This course provides a foundation in data analysis techniques and web development, which can be valuable for Data Engineers who want to develop their skills in data pipeline development and management.
User Experience Designer
User Experience Designer
User Experience Designers design and evaluate user interfaces. They work with engineers and designers to create user interfaces that are user-friendly and efficient. This course provides a foundation in user interface design and web development, which can be valuable for User Experience Designers who want to develop their skills in user interface design and development.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages and software development tools to create software that meets the needs of users. This course provides a foundation in web development using the Streamlit library. This experience can be valuable for Software Engineers who want to develop their skills in building web applications.
Business Analyst
Business Analyst
Business Analysts help businesses to identify and solve problems. They use their knowledge of business processes and data analysis to develop solutions that improve efficiency and profitability. This course provides a foundation in data analysis techniques, including machine learning, which can be applied to a variety of business problems. Additionally, the hands-on project experience in building a digit recognition web application can help Business Analysts develop practical skills in data visualization and web development.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test and evaluate software products to ensure that they meet quality standards. They work with engineers and product managers to identify and fix defects. This course provides a foundation in software testing, which can be valuable for Quality Assurance Analysts who want to develop their skills in testing software products.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to ensure that products meet the needs of users. This course provides a foundation in user interface design and web development, which can be valuable for Product Managers who want to develop their skills in product development and launch.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. They work with engineers and product managers to ensure that documentation is accurate and easy to understand. This course provides a foundation in web development, which can be valuable for Technical Writers who want to develop their skills in creating online documentation.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They work with stakeholders to ensure that projects are completed on time, within budget, and to the required quality. This course provides a foundation in project management, which can be valuable for Project Managers who want to develop their skills in managing projects.
For more career information including salaries, visit:
OpenCourser.com/course/vcdrq8/create
Reading list
We've selected 12 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
Create digit recognition web app with Streamlit .
Provides a comprehensive overview of machine learning techniques and algorithms, with a focus on practical implementation using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a practical guide to building and training deep learning models using Python and the Keras library. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a practical guide to building and training machine learning models using Python and the Scikit-Learn library. It covers a wide range of topics including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a practical guide to building and training machine learning models using Python and the scikit-learn library. It covers topics such as data preprocessing, feature engineering, model selection, and model evaluation.
Provides a comprehensive guide to building and training deep learning models for computer vision tasks. It covers topics such as image classification, object detection, and image segmentation.
Save
Provides a comprehensive overview of computer vision techniques, with a focus on practical implementation using Python and the OpenCV library. It covers topics such as image processing, feature extraction, and object recognition.
Provides a comprehensive guide to building and training natural language processing models using Python and the NLTK library. It covers topics such as text classification, sentiment analysis, and machine translation.
Provides a practical guide to building and training machine learning models using R and the caret package. It covers a wide range of topics including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a comprehensive overview of statistical learning methods, with a focus on supervised and unsupervised learning algorithms. It covers topics such as linear models, tree-based methods, and support vector machines.
Save
Provides a comprehensive overview of generative adversarial networks (GANs), with a focus on the mathematical principles and practical implementation. It covers topics such as GAN architectures, training techniques, and applications.
Provides a comprehensive overview of reinforcement learning techniques, with a focus on the mathematical principles and practical implementation. It covers topics such as Markov decision processes, value functions, and policy gradient methods.
Provides a comprehensive overview of speech and language processing techniques, with a focus on practical implementation using Python and the NLTK library. It covers topics such as speech recognition, natural language understanding, and machine translation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vcdrq8/create
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Build a Data Science Web App with Streamlit and Python
OpenCourser.com/course/08cqao/build
Build a Data Science Web App with Streamlit and Python
Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Language
English
Good to know
Suitable for beginners looking to acquire a practical skillset
Covers the fundamentals of deep learning and data science
Emphasizes hands-on learning through projects and tutorials
Instructed by experienced professionals who are active in the field
Part of a series of courses that provide a comprehensive learning path
Requires familiarity with basic python programming concepts
May be too introductory for individuals with prior deep learning experience
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