Download Applied Machine Learning - Algorithms (2024) torrent

Applied Machine Learning - Algorithms (2024)

Torrent:	Applied Machine Learning - Algorithms (2024)
Description:	Applied Machine Learning: Algorithms (2024) https://DevCourseWeb.com Released 4/2024 MP4 \| Video: h264, 1280x720 \| Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate \| Genre: eLearning \| Language: English + srt \| Duration: 1h 58m \| Size: 268 MB With the growing importance of machine learning in almost every sector, professionals need a deeper understanding and practical approach to implementing ML algorithms effectively. This course covers commonly used machine learning algorithms. Instructor Matt Harrison focuses on non-deep learning algorithms, covering PCA, clustering, linear and logistic regression, decision trees, random forests, and gradient boosting. Join Matt in this course to understand common ML algorithms, learn their pros and cons, and develop hands-on skills to leverage them by following along with challenges and solutions in GitHub Codespaces.
Downloads:	135
Category:	Other /Tutorials
Size:	267.9 MB
Show Files »	[ DevCourseWeb.com ] Applied Machine Learning - Algorithms (2024) Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 01 - Introduction 01 - Applied machine learning Algorithms.mp4 (4.5 MB) 01 - Applied machine learning Algorithms.srt (1.5 KB) 02 - What you should know.mp4 (4.9 MB) 02 - What you should know.srt (1.9 KB) 02 - 1. Clustering 01 - K-means.mp4 (18.4 MB) 01 - K-means.srt (14.4 KB) 02 - K evaluation.mp4 (17.8 MB) 02 - K evaluation.srt (13.7 KB) 03 - Understanding clusters.mp4 (19.1 MB) 03 - Understanding clusters.srt (14.9 KB) 04 - Other algorithms.mp4 (5.8 MB) 05 - Challenge Apply KNN.mp4 (1.5 MB) 05 - Challenge Apply KNN.srt (1.5 KB) 06 - Solution Apply KNN.mp4 (15.7 MB) 06 - Solution Apply KNN.srt (12.8 KB) 03 - 2. PCA 01 - PCA.mp4 (8.3 MB) 01 - PCA.srt (6.3 KB) 02 - Structure of components.mp4 (9.0 MB) 02 - Structure of components.srt (6.5 KB) 03 - Components.mp4 (12.0 MB) 03 - Components.srt (8.7 KB) 04 - Scatter plot.mp4 (4.3 MB) 04 - Scatter plot.srt (3.6 KB) 05 - Other algorithms.mp4 (7.6 MB) 05 - Other algorithms.srt (6.5 KB) 06 - Challenge Utilize PCA.mp4 (933.2 KB) 06 - Challenge Utilize PCA.srt (0.9 KB) 07 - Solution Utilize PCA.mp4 (11.2 MB) 07 - Solution Utilize PCA.srt (6.8 KB) 04 - 3. Linear Regression 01 - Linear regression algorithm.mp4 (9.6 MB) 01 - Linear regression algorithm.srt (7.5 KB) 02 - scikit-learn.mp4 (8.1 MB) 02 - scikit-learn.srt (7.3 KB) 03 - Real-world example.mp4 (13.3 MB) 03 - Real-world example.srt (9.8 KB) 04 - Assumptions.mp4 (14.4 MB) 04 - Assumptions.srt (10.0 KB) 05 - Challenge Develop a linear regression model.mp4 (1.0 MB) 05 - Challenge Develop a linear regression model.srt (0.8 KB) 06 - Solution Develop a linear regression model.mp4 (4.2 MB) 06 - Solution Develop a linear regression model.srt (3.1 KB) 05 - 4. Logistic Regression 01 - Logistic regression algorithm.mp4 (2.7 MB) 01 - Logistic regression algorithm.srt (2.2 KB) 02 - Basic example.mp4 (3.6 MB) 02 - Basic example.srt (3.1 KB) 03 - Assumptions.mp4 (9.5 MB) 03 - Assumptions.srt (6.9 KB) 04 - Challenge Construct a logistic regression model.mp4 (360.4 KB) 04 - Challenge Construct a logistic regression model.srt (0.3 KB) 05 - Solution Construct a logistic regression model.mp4 (10.9 MB) 05 - Solution Construct a logistic regression model.srt (7.3 KB) 06 - 5. Decision Trees 01 - Decision tree algorithm.mp4 (9.0 MB) 01 - Decision tree algorithm.srt (7.3 KB) 02 - Real-world example.mp4 (15.5 MB) 02 - Real-world example.srt (10.9 KB) 03 - Random Forest and XGBoost.mp4 (8.2 MB) 03 - Random Forest and XGBoost.srt (7.6 KB) 04 - Challenge Design a decision tree model.mp4 (713.5 KB) 05 - Solution Design a decision tree model.mp4 (9.0 MB) 05 - Solution Design a decision tree model.srt (5.7 KB) 07 - Conclusion 01 - Next steps.mp4 (6.6 MB) 01 - Next steps.srt (2.6 KB) Bonus Resources.txt (0.4 KB) files
Added:	2024-04-26 02:02:59
Language:	English
Peers:	Seeders : 26 , Leechers : 6
Release name:	Applied Machine Learning - Algorithms (2024)
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