Build a Machine Learning Web App with Streamlit and Python

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En este proyecto guiado, tú:

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
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

Data ScienceMachine LearningPython ProgrammingStreamlitScikit-Learn

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

Instructor

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