Building Machine Learning Apps with PyCaret and Streamlit

Want to transition from writing raw machine learning code to deploying functional, interactive web applications? Building and sharing your data science projects is the best way to demonstrate your skills to the world. In this practical, text-based course, you will learn how to build a machine learning model from scratch and turn it into a live web application. Using the low-code PyCaret library, you will quickly train and evaluate an Iris classification model, then build an intuitive user interface using Streamlit so anyone can interact with your model in real time. What you'll learn: - Understand the fundamentals of low-code machine learning workflows using PyCaret - Prepare and analyze data for classification tasks using modern Python practices - Train, compare, and tune multiple machine learning models with minimal code - Build an interactive web interface using Streamlit to accept user inputs - Connect your trained machine learning model to the web application for real-time predictions - Deploy your completed web application to make it accessible online You will start by setting up a clean development environment and learning core machine learning concepts. From there, you will progress step-by-step through data preparation, model training, UI development, and final deployment. This course is designed for beginner data analysts, aspiring data scientists, and Python developers who want to learn how to deploy their models. No prior machine learning or web development experience is required, though a basic understanding of Python is helpful. Start reading today to turn your data science ideas into interactive web applications.