Watch Build & Deploy AI/ML Web Apps: Hands-On Tutorial (Streamlit,GitHub, API) Video Tutorial

👨‍🏫 In this hands-on machine learning session, Dr. Onoja guides students through the complete end-to-end workflow of deploying an ML model using Python — from training in Jupyter Notebook to publishing on Streamlit Cloud. 🧠 What You’ll Learn: 0:00 – 7:00 | How to train a Random Forest classifier on the Iris dataset 8:00 – 24:00 | How to use Generative AI (e.g., ChatGPT/Copilot) to write production-ready Python scripts 31:00 – 47:20 | How to upload your project to a GitHub repository 47:20 – 49:50 | How to deploy a fully functional Streamlit app to the web 🎯 Key Skills Practiced: - Model training & evaluation - Serialization with Joblib - Prompting GenAI for boilerplate code - GitHub repository creation & commit workflow - Model deployment using Streamlit cloud 🧰 Tools Used: Python, Jupyter Notebook, scikit-learn, Streamlit, GitHub, Generative AI (ChatGPT/Copilot) 📦 Dataset: The classic Iris dataset — ideal for demonstrating classification tasks. 📍 Recorded live during a DataEdge Academy class session on model deployment. 📌 Whether you're a data science student or an aspiring ML engineer, this session offers real-world exposure to production-ready workflows. 🌐 Helpful Links: Github repo: https://github.com/Donmaston09/explainer_iris_web_app 🔗 Streamlit: https://streamlit.io/ 🚀 Live App: Deployed Iris Classifier 🔗 Connect: 📧 Email: donmaston09@gmail.com 📺 YouTube: @tonyonoja7880 🌐 Facebook: DataEdge Academy 👍 Don’t forget to Like, Subscribe, and Comment if you found this helpful! #machinelearning #modeldeployment #python #generativeai #streamlit #github #jupyternotebook #datascience #IrisDataset #randomforest #aiapps #mlprojects #DataEdgeAcademy #artificialintelligence #ai #models #researchers #educationalvideo #education #xai #explainableai #innovation #learningprogress #github #pythonprogramming #tutorial