Pluralsight Path Data Science with Microsoft Azure 2021 F2 Evaluating Model Effectiveness in Microsoft Azure Tim Warner 2019 playlist m3u

Pluralsight Path Data Science with Microsoft Azure 2021
F2 Evaluating Model Effectiveness in Microsoft Azure Tim Warner 2019 playlist m3u

Name	Pluralsight Path. Data Science with Microsoft Azure (2021)	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	3.4 GB
Total Files	1000
Hash	35B2554B1CE78F11BB2C8CB0A354BB034C77271F

/F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/playlist.m3u	1.3 KB
/F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/exercise.7z	460.6 KB
/F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/~i.txt	1.5 KB
/B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/playlist.m3u	0.8 KB
/E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/playlist.m3u	3.2 KB
/B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/playlist.m3u	1.0 KB
/F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/playlist.m3u	1.1 KB
/E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/playlist.m3u	2.2 KB
/F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/1. Course Overview/1. Course Overview.mp4	2.1 MB
/F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/1. Course Overview/1. Course Overview.vtt	1.3 KB
/E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/playlist.m3u	3.6 KB
/F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/playlist.m3u	1.3 KB
/F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/1. Overview.mp4	1.1 MB
/F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/6. Summary.mp4	1.8 MB
/F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/6. Summary.vtt	2.1 KB
Showing first 15 files of 1000 total files