| 1 - Coding Basic Commands.mp4 | 23.7 MB | ||
| 1 - Extra App Lecture Deploying your app through GitHub.mp4 | 31.9 MB | ||
| 1 - Extra ML Lecture What is overfitting.html | 1.4 KB | ||
| 1 - Histograms and KDEs.mp4 | 48.8 MB | ||
| 1 - Introduction - What we'll create in this course!.mp4 | 8.5 MB | ||
| 1 - Logistic Regression Theory.mp4 | 42.1 MB | ||
| 1 - Why Visualize Data.html | 1.2 KB | ||
| 2 - Adding Histograms to Webapp.mp4 | 20.8 MB | ||
| 2 - Anaconda Download Instructions.mp4 | 48.2 MB | ||
| 2 - Logistic Regression in Jupyter.mp4 | 50.3 MB | ||
| 2 - Uploading Basic Commands to App - Part 1.mp4 | 34.1 MB | ||
| 3 - Adding KDEs to Webapp.mp4 | 41.7 MB | ||
| 3 - Download Link for Jupyter Files.html | 307.2 B | ||
| 3 - Extra! In-Depth, Explained Logistic Regression in Jupyter.mp4 | 48.7 MB | ||
| 3 - Opening the Files in Jupyter.mp4 | 24.7 MB | ||
| 3 - Uploading Basic Commands to App - Part 2.mp4 | 23.6 MB | ||
| 4 - Explanation of Metrics.mp4 | 59.1 MB | ||
| 4 - Explanation of the Dataset.mp4 | 19.3 MB | ||
| 4 - Python Crash Course.html | 307.2 B | ||
| 4 - Scatterplots.mp4 | 33.7 MB | ||
| 5 - Dictionaries Crash Course - Skip if familiar with Python dictionaries.html | 2.2 KB | ||
| 5 - Logistic Regression in Streamlit.mp4 | 68.4 MB | ||
| 5 - Subplots.mp4 | 33.8 MB | ||
| 6 - Decision Tree Classifier Theory.mp4 | 43.7 MB | ||
| 6 - Heatmaps and Correlations.mp4 | 46.6 MB | ||
| 6 - Supervised vs. Unsupervised Learning.html | 2.5 KB | ||
| 7 - Data Cleaning.mp4 | 39.9 MB | ||
| 7 - Decision Trees in Jupyter.mp4 | 28 MB | ||
| 8 - Adding Scatterplots, Correlation Heatmap, and Cleaned Data to App.mp4 | 31.9 MB | ||
| 8 - Decision Trees in Streamlit.mp4 | 32 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 32 total files | |||
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.
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