Udemy Build a Diabetes Dashboard with Python Streamlit and ML
Seeders : 34 Leechers : 0
| Torrent Hash : | 3076C5387EB48E1E5CC6C1F1268B096928673BF6 |
| Torrent Added : | at Sept. 25, 2025, 6:36 p.m. in Other |
| Torrent Size : | 883.5 MB |
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Udemy Build a Diabetes Dashboard with Python Streamlit and ML
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Please Update (Trackers Info) Before Start " Udemy Build a Diabetes Dashboard with Python Streamlit and ML" Torrent Downloading to See Updated Seeders And Leechers for Batter Torrent Download Speed.Torrent File Content (3 files)
Udemy Build a Diabetes Dashboard with Python Streamlit and ML
Get Bonus Downloads Here.url -
1 - Introduction - What we'll create in this course!.mp4 -
2 - Anaconda Download Instructions.mp4 -
3 - Download Link for Jupyter Files.html -
3 - Opening the Files in Jupyter.mp4 -
4 - Explanation of the Dataset.mp4 -
4 - Python Crash Course.html -
1 - Coding Basic Commands.mp4 -
2 - Uploading Basic Commands to App - Part 1.mp4 -
3 - Uploading Basic Commands to App - Part 2.mp4 -
1 - Histograms and KDEs.mp4 -
1 - Why Visualize Data.html -
2 - Adding Histograms to Webapp.mp4 -
3 - Adding KDEs to Webapp.mp4 -
4 - Scatterplots.mp4 -
5 - Subplots.mp4 -
6 - Heatmaps and Correlations.mp4 -
7 - Data Cleaning.mp4 -
8 - Adding Scatterplots, Correlation Heatmap, and Cleaned Data to App.mp4 -
1 - Logistic Regression Theory.mp4 -
2 - Logistic Regression in Jupyter.mp4 -
3 - Extra! In-Depth, Explained Logistic Regression in Jupyter.mp4 -
4 - Explanation of Metrics.mp4 -
5 - Dictionaries Crash Course - Skip if familiar with Python dictionaries.html -
5 - Logistic Regression in Streamlit.mp4 -
6 - Decision Tree Classifier Theory.mp4 -
6 - Supervised vs. Unsupervised Learning.html -
7 - Decision Trees in Jupyter.mp4 -
8 - Decision Trees in Streamlit.mp4 -
1 - Extra App Lecture Deploying your app through GitHub.mp4 -
1 - Extra ML Lecture What is overfitting.html -
Bonus Resources.txt -
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Get Bonus Downloads Here.url -
180 bytes
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 -
324 bytes
3 - Opening the Files in Jupyter.mp4 -
24.7 MB
4 - Explanation of the Dataset.mp4 -
19.3 MB
4 - Python Crash Course.html -
340 bytes
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
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
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
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 -
70 bytes
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