Udemy - Build a Diabetes Dashboard with Python, Streamlit and ML

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size883.5 MB
  • Uploaded Byfreecoursewb
  • Downloads124
  • Last checkedSep. 30th '25
  • Date uploadedSep. 26th '25
  • Seeders 19
  • Leechers1

Infohash : 3076C5387EB48E1E5CC6C1F1268B096928673BF6

Build a Diabetes Dashboard with Python, Streamlit & ML

https://WebToolTip.com

Published 9/2025
Created by Annapoorani Lingeshwaran
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 30 Lectures ( 2h 16m ) | Size: 884 MB

A fast-track, project based course covering data science basics, ML, and visualizations.

What you'll learn
Build an interactive Streamlit dashboard app in Python from scratch, using real-world diabetes data.
Create insightful data visualizations with Pandas, Matplotlib, and Seaborn to explore health datasets.
Develop and integrate machine learning models (e.g., logistic regression, decision tree) into a deployable web app for diabetes prediction.
Deploy a polished, user-friendly data science project that nstrates both coding and applied ML skills — perfect for portfolios or job applications.

Requirements
A computer (Windows, Mac, or Linux) with internet access.
Basic familiarity with Python (variables, functions, simple scripts) is helpful but not required — I’ll guide you step by step.
Willingness to install Anaconda (free, beginner-friendly Python distribution) — I’ll walk you through the setup process in detail.
No prior experience with data visualisation, machine learning, or Streamlit is needed — we’ll build everything from scratch.

Files:

[ WebToolTip.com ] Udemy - Build a Diabetes Dashboard with Python, Streamlit and ML
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction
    • 1 - Introduction - What we'll create in this course!.mp4 (8.5 MB)
    • 2 - Anaconda Download Instructions.mp4 (48.2 MB)
    • 3 - Download Link for Jupyter Files.html (0.3 KB)
    • 3 - Opening the Files in Jupyter.mp4 (24.7 MB)
    • 4 - Explanation of the Dataset.mp4 (19.3 MB)
    • 4 - Python Crash Course.html (0.3 KB)
    2 - Extracting Basic Insights
    • 1 - Coding Basic Commands.mp4 (23.7 MB)
    • 2 - Uploading Basic Commands to App - Part 1.mp4 (34.1 MB)
    • 3 - Uploading Basic Commands to App - Part 2.mp4 (23.6 MB)
    3 - Data Visualization
    • 1 - Histograms and KDEs.mp4 (48.8 MB)
    • 1 - Why Visualize Data.html (1.2 KB)
    • 2 - Adding Histograms to Webapp.mp4 (20.8 MB)
    • 3 - Adding KDEs to Webapp.mp4 (41.7 MB)
    • 4 - Scatterplots.mp4 (33.7 MB)
    • 5 - Subplots.mp4 (33.8 MB)
    • 6 - Heatmaps and Correlations.mp4 (46.6 MB)
    • 7 - Data Cleaning.mp4 (39.9 MB)
    • 8 - Adding Scatterplots, Correlation Heatmap, and Cleaned Data to App.mp4 (31.9 MB)
    4 - Machine Learning - Logistic Regression & Decision Tree Classifiers
    • 1 - Logistic Regression Theory.mp4 (42.1 MB)
    • 2 - Logistic Regression in Jupyter.mp4 (50.3 MB)
    • 3 - Extra! In-Depth, Explained Logistic Regression in Jupyter.mp4 (48.7 MB)
    • 4 - Explanation of Metrics.mp4 (59.1 MB)
    • 5 - Dictionaries Crash Course - Skip if familiar with Python dictionaries.html (2.2 KB)
    • 5 - Logistic Regression in Streamlit.mp4 (68.4 MB)
    • 6 - Decision Tree Classifier Theory.mp4 (43.7 MB)
    • 6 - Supervised vs. Unsupervised Learning.html (2.5 KB)
    • 7 - Decision Trees in Jupyter.mp4 (28.0 MB)
    • 8 - Decision Trees in Streamlit.mp4 (32.0 MB)
    5 - Extra Overfitting & Deploying the App
    • 1 - Extra App Lecture Deploying your app through GitHub.mp4 (31.9 MB)
    • 1 - Extra ML Lecture What is overfitting.html (1.4 KB)
    • Bonus Resources.txt (0.1 KB)

Code:

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