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Snehan Kekre
Welcome to this hands-on project on building your first machine learning web app with the Streamlit library in Python. By the end of this project, you are going to be comfortable with using Python and Streamlit to build beautiful and interactive ML web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code! Our web application will allows users to choose what classification algorithm they want to use and let them interactively set hyper-parameter values, all without them knowing to code!
Read more
Welcome to this hands-on project on building your first machine learning web app with the Streamlit library in Python. By the end of this project, you are going to be comfortable with using Python and Streamlit to build beautiful and interactive ML web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code! Our web application will allows users to choose what classification algorithm they want to use and let them interactively set hyper-parameter values, all without them knowing to code!
Read more
Welcome to this hands-on project on building your first machine learning web app with the Streamlit library in Python. By the end of this project, you are going to be comfortable with using Python and Streamlit to build beautiful and interactive ML web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code! Our web application will allows users to choose what classification algorithm they want to use and let them interactively set hyper-parameter values, all without them knowing to code!
Prior experience with writing simple Python scripts and using pandas for data manipulation is recommended. It is required that you have an understanding of Logistic Regression, Support Vector Machines, and Random Forest Classifiers and how to use them in scikit-learn.
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/v51c57/build
What's inside
Syllabus
Build a Machine Learning Web App with Streamlit and Python
Welcome to this hands-on project on building your first machine learning web app with the Streamlit library in Python. By the end of this project, you are going to be comfortable with using Python and Streamlit to build beautiful and interactive ML web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code! Our web application will allows users to choose what classification algorithm they want to use and let them interactively set hyper-parameter values, all without them knowing to code!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops real-world applicable skills you can use in job interviews and in your professional life
Builds a strong foundation for beginners looking to explore machine learning
Designed for learners with no web development experience who want to build interactive ML web apps
Taught by experienced instructors who are recognized for their work in machine learning and web development
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Reviews summary
Streamlit web app with ml
Learners say this course is well received and great for beginners in machine learning who want to deploy an ML model as a web app. Students describe it as engaging and well-taught, with an excellent instructor who explains concepts clearly. Reviews mention that the course material is good, too. However, some learners say that the course is outdated.
Assignments are engaging and hands-on.
"An amazing guided project, very well-taught and very useful."
"A great introductory guided project that fueled my enthusiasm for data science and machine learning with knowledge and practical skills."
"Great short and concise. Engaging, and the instructor is very good."
Suitable for beginners in ML.
"I feel confident in using Streamlit after going through this Guided Project."
"Highly recommended course for Machine Beginners and Experts alike."
"Builds a very strong foundation in order to use Streamlit package for building Machine Learning Web Apps"
Course material is outdated.
"Could do a lot better."
"It was helpful to learn the capabilities of Streamlit. However, the exercise is too simple and it does not test one's retention of the subject."
"Lot's of errors and cloud desktop was too slow"
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 a Machine Learning Web App with Streamlit and Python with these
activities:
Review Python programming
Show steps
This refresher will help you to prepare for the Python programming material covered in the course.
Browse courses on
Python
Show steps
-
Review the basics of Python
-
Solve practice problems on Python
Review data manipulation using Pandas
Show steps
This refresher will help you to prepare for the data manipulation material covered in the course.
Browse courses on
Pandas
Show steps
-
Review the basics of Pandas
-
Solve practice problems on Pandas
Understand Logistic Regression concepts
Show steps
These exercises in Logistic Regression will help you prepare for the material covered in the course.
Browse courses on
Logistic Regression
Show steps
-
Review the basics of Logistic Regression
-
Solve practice problems on Logistic Regression
-
Implement Logistic Regression in Python
Four other activities
Expand to see all activities and additional details
Show all seven activities
Understand Support Vector Machines concepts
Show steps
These exercises in Support Vector Machines will help you prepare for the material covered in the course.
Browse courses on
Support Vector Machines
Show steps
-
Review the basics of Support Vector Machines
-
Solve practice problems on Support Vector Machines
-
Implement Support Vector Machines in Python
Understand Random Forest Classifiers concepts
Show steps
These exercises in Random Forest Classifiers will help you prepare for the material covered in the course.
Browse courses on
Random Forest
Show steps
-
Review the basics of Random Forest Classifiers
-
Solve practice problems on Random Forest Classifiers
-
Implement Random Forest Classifiers in Python
Build a machine learning web app using Streamlit
Show steps
This tutorial will help you build a machine learning web app using the Streamlit library in Python
Browse courses on
Streamlit
Show steps
-
Follow the guided tutorial on building a machine learning web app using Streamlit
-
Create your own machine learning web app using Streamlit
Build a machine learning web app
Show steps
This project will help you to learn how to build and deploy a machine learning web app using your knowledge of Python, Streamlit and ML algorithms
Browse courses on
Machine Learning
Show steps
-
Design the web app
-
Collect and prepare the data
-
Train and evaluate different machine learning models
-
Deploy the web app
Review Python programming
Show steps
This refresher will help you to prepare for the Python programming material covered in the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the basics of Python
- Solve practice problems on Python
Review data manipulation using Pandas
Show steps
This refresher will help you to prepare for the data manipulation material covered in the course.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Review the basics of Pandas
- Solve practice problems on Pandas
Understand Logistic Regression concepts
Show steps
These exercises in Logistic Regression will help you prepare for the material covered in the course.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Review the basics of Logistic Regression
- Solve practice problems on Logistic Regression
- Implement Logistic Regression in Python
Four other activities
Expand to see all activities and additional details
Show all seven activities
Understand Support Vector Machines concepts
Show steps
These exercises in Support Vector Machines will help you prepare for the material covered in the course.
Browse courses on
Support Vector Machines
Show steps
Show steps
Show steps
- Review the basics of Support Vector Machines
- Solve practice problems on Support Vector Machines
- Implement Support Vector Machines in Python
Understand Random Forest Classifiers concepts
Show steps
These exercises in Random Forest Classifiers will help you prepare for the material covered in the course.
Browse courses on
Random Forest
Show steps
Show steps
Show steps
- Review the basics of Random Forest Classifiers
- Solve practice problems on Random Forest Classifiers
- Implement Random Forest Classifiers in Python
Build a machine learning web app using Streamlit
Show steps
This tutorial will help you build a machine learning web app using the Streamlit library in Python
Browse courses on
Streamlit
Show steps
Show steps
Show steps
- Follow the guided tutorial on building a machine learning web app using Streamlit
- Create your own machine learning web app using Streamlit
Build a machine learning web app
Show steps
This project will help you to learn how to build and deploy a machine learning web app using your knowledge of Python, Streamlit and ML algorithms
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Design the web app
- Collect and prepare the data
- Train and evaluate different machine learning models
- Deploy the web app
Career center
Learners who complete Build a Machine Learning Web App with Streamlit and Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. This course may be useful for Data Scientists because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Data Scientists can more effectively communicate data insights to stakeholders and make better data-driven decisions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models to solve business problems. This course may be useful for Machine Learning Engineers because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Machine Learning Engineers can more effectively communicate data insights to stakeholders and make better data-driven decisions.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. This course may be useful for Business Analysts because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Business Analysts can more effectively communicate data insights to stakeholders and make better data-driven decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. This course may be useful for Operations Research Analysts because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Operations Research Analysts can more effectively communicate data insights to stakeholders and make better data-driven decisions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Software Engineers can more effectively build and maintain data-driven applications.
Data Analyst
Data Analyst
Data Analysts help build better products and services by providing insights into data. This course may be useful for Data Analysts because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Data Analysts can more effectively communicate data insights to stakeholders and make better data-driven decisions.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. This course may be useful for Financial Analysts because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Financial Analysts can more effectively communicate data insights to stakeholders and make better data-driven decisions.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be useful for Product Managers because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Product Managers can more effectively understand and communicate data insights to stakeholders and make better data-driven decisions.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines to manage and process data. This course may be useful for Data Engineers because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Data Engineers can more effectively build and maintain data pipelines to support data-driven applications.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists use data to create visual representations of data. This course may be useful for Data Visualization Specialists because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Data Visualization Specialists can more effectively communicate data insights to stakeholders and make better data-driven decisions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course may be useful for Quantitative Analysts because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Quantitative Analysts can more effectively communicate data insights to stakeholders and make better data-driven decisions.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. This course may be useful for Actuaries because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Actuaries can more effectively communicate data insights to stakeholders and make better data-driven decisions.
Epidemiologist
Epidemiologist
Epidemiologists use data to investigate the causes of disease. This course may be useful for Epidemiologists because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Epidemiologists can more effectively communicate data insights to stakeholders and make better data-driven decisions.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze biological data. This course may be useful for Biostatisticians because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Biostatisticians can more effectively communicate data insights to stakeholders and make better data-driven decisions.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. This course may be useful for Market Researchers because it helps build a foundation in using Python and Streamlit to build interactive data visualizations and machine learning models. With these skills, Market Researchers can more effectively communicate data insights to stakeholders and make better data-driven decisions.
For more career information including salaries, visit:
OpenCourser.com/course/v51c57/build
Reading list
We've selected nine 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 a Machine Learning Web App with Streamlit and Python.
Provides a comprehensive introduction to machine learning with Python. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation. It also includes a number of practical examples that show how to use machine learning to solve real-world problems.
Provides a comprehensive introduction to deep learning with Python. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It also includes a number of practical examples that show how to use deep learning to solve real-world problems.
Provides a comprehensive introduction to data analysis with Python. It covers a wide range of topics, including data cleaning, data exploration, data visualization, and machine learning. It also includes a number of practical examples that show how to use Python to solve real-world data analysis problems.
Provides a comprehensive introduction to machine learning with R. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation. It also includes a number of practical examples that show how to use machine learning to solve real-world problems.
Provides a comprehensive introduction to data science from scratch. It covers a wide range of topics, including data collection, data cleaning, data exploration, data visualization, and machine learning. It also includes a number of practical examples that show how to use Python to solve real-world data science problems.
Provides a practical introduction to machine learning for hackers. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation. It also includes a number of practical examples that show how to use machine learning to solve real-world problems.
Provides a comprehensive introduction to machine learning with Python. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation. It also includes a number of practical examples that show how to use Python to solve real-world machine learning problems.
Provides a comprehensive introduction to machine learning with TensorFlow. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation. It also includes a number of practical examples that show how to use TensorFlow to solve real-world machine learning problems.
Provides a comprehensive introduction to deep learning with PyTorch. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It also includes a number of practical examples that show how to use PyTorch to solve real-world deep learning problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/v51c57/build
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Develops real-world applicable skills you can use in job interviews and in your professional life
Builds a strong foundation for beginners looking to explore machine learning
Designed for learners with no web development experience who want to build interactive ML web apps
Taught by experienced instructors who are recognized for their work in machine learning and web development
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