Build a Machine Learning Web App with Streamlit and Python

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在此指導項目中,您將:

Build interactive web applications with Streamlit and Python

Train Logistic Regression, Random Forest, and Support Vector Classifiers using scikit-learn

Plot evaluation metrics for binary classification algorithms

Clock1.5 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

Data ScienceMachine LearningPython ProgrammingStreamlitScikit-Learn

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Project Overview and Demo

  2. Turn Simple Python Scripts into Web Apps

  3. Load the Mushrooms Data Set

  4. Creating Training and Test Sets

  5. Plot Evaluation Metrics

  6. Training a Support Vector Classifier

  7. Training a Support Vector Classifier (Part 2)

  8. Train a Logistic Regression Classifier

  9. Training a Random Forest Classifier

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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