Careertail
About UsCoursesCareer PathsBlogOpportunities
Log In
Courses>log>Building the Frontend of Python Web Applications with Streamlit
Building the Frontend of Python Web Applications with Streamlit
Price:Paid
Length:3 hours 25 minutes
Content type:text
level:beginner
Language:English
Updated:11 October 2022
Published:03 October 2022
Similar courses
Opportunities
Courses>>Building the Frontend of Python Web Applications with Streamlit
Building the Frontend of Python Web Applications with Streamlit
 English
 Rahul Banerjee
DescriptionPython is the premier programming language for data science and machine learning, but its native strengths for back-end development can leave developers scrambling for a front-end solution. Enter Streamlit, a robust open-source library for developing front-end applications with Python. This course is a comprehensive introduction to building Streamlit applications integrated with different Python libraries. You’ll be walked through successive projects to create visualizations, display interactive widgets, and customize layouts. You won’t just display data but work on data transformations using Streamlit interfaces and become familiar with basic functionality for native Python applications like caching and placeholders. You’ll wrap up with deploying your application using the Streamlit cloud. By the end of this course, you’ll be ready to give your Python-based data science and machine learning applications robust user interfaces using Streamlit.
Syllabus

1. Streamlit Overview

1. Prerequisites and Required Libraries

2. Build a Logistic Regression Model

3. Build the DataFrame and Model Performance UI with Streamlit

4. Build User Input UI with Streamlit

5. Exercise 1: Build UI for Candy Bar Prediction

6. Solution: Build UI for Candy Bar Prediction

1. Prerequisites and Required Libraries

2. Classification using SVM, KNN, RandomForestClassifier, and PCA

3. Building UI using Streamlit

4. Exercise 2: Plot Evaluation Metrics using Streamlit

5. Solution: Plot Evaluation Metrics using Streamlit

1. Prerequisites and Required Libraries

2. Web Scraping using Beautiful Soup

3. Streamlit Scraping Application

1. Prerequisites and Required Libraries

2. Transcribing a Local Audio File using AssemblyAI

3. Building the Streamlit UI

1. Prerequisites and Required Libraries

2. Acquiring the Data

3. Creating Word Clouds

4. Streamlit App for Word Clouds

5. Exercise 3: Change the Word Cloud's Properties using Streamlit

6. Solution: Change the Word Cloud's Properties using Streamlit

1. Prerequisites and Required Libraries

2. Data Collection and Understanding the Stream Object

3. Streamlit Videos Downloading Application

1. Deployment using Streamlit Sharing

2. Deployment using Heroku

1. Quiz on Streamlit UI Components

2. Conclusion

Similar courses
Opportunities
Make the most out of your online education
Careertail
Copyright © 2021 Careertail.
All rights reserved
Quick Links
Get StartedLog InAbout UsCourses
Company
BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions
Stay up to date
Trustpilot
Careertail
Courses>log>Building the Frontend of Python Web Applications with Streamlit
Building the Frontend of Python Web Applications with Streamlit
Price:Paid
Length:3 hours 25 minutes
Content type:text
level:beginner
Language:English
Updated:11 October 2022
Published:03 October 2022
Similar courses
Opportunities
Courses>>Building the Frontend of Python Web Applications with Streamlit
Building the Frontend of Python Web Applications with Streamlit
 English
 Rahul Banerjee
DescriptionPython is the premier programming language for data science and machine learning, but its native strengths for back-end development can leave developers scrambling for a front-end solution. Enter Streamlit, a robust open-source library for developing front-end applications with Python. This course is a comprehensive introduction to building Streamlit applications integrated with different Python libraries. You’ll be walked through successive projects to create visualizations, display interactive widgets, and customize layouts. You won’t just display data but work on data transformations using Streamlit interfaces and become familiar with basic functionality for native Python applications like caching and placeholders. You’ll wrap up with deploying your application using the Streamlit cloud. By the end of this course, you’ll be ready to give your Python-based data science and machine learning applications robust user interfaces using Streamlit.
Syllabus

1. Streamlit Overview

1. Prerequisites and Required Libraries

2. Build a Logistic Regression Model

3. Build the DataFrame and Model Performance UI with Streamlit

4. Build User Input UI with Streamlit

5. Exercise 1: Build UI for Candy Bar Prediction

6. Solution: Build UI for Candy Bar Prediction

1. Prerequisites and Required Libraries

2. Classification using SVM, KNN, RandomForestClassifier, and PCA

3. Building UI using Streamlit

4. Exercise 2: Plot Evaluation Metrics using Streamlit

5. Solution: Plot Evaluation Metrics using Streamlit

1. Prerequisites and Required Libraries

2. Web Scraping using Beautiful Soup

3. Streamlit Scraping Application

1. Prerequisites and Required Libraries

2. Transcribing a Local Audio File using AssemblyAI

3. Building the Streamlit UI

1. Prerequisites and Required Libraries

2. Acquiring the Data

3. Creating Word Clouds

4. Streamlit App for Word Clouds

5. Exercise 3: Change the Word Cloud's Properties using Streamlit

6. Solution: Change the Word Cloud's Properties using Streamlit

1. Prerequisites and Required Libraries

2. Data Collection and Understanding the Stream Object

3. Streamlit Videos Downloading Application

1. Deployment using Streamlit Sharing

2. Deployment using Heroku

1. Quiz on Streamlit UI Components

2. Conclusion

Similar courses
Opportunities
Make the most out of your online education
Careertail
Copyright © 2021 Careertail.
All rights reserved
Quick Links
Get StartedLog InAbout UsCourses
Company
BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions
Stay up to date
Trustpilot