[DesireCourse.Net] Udemy - Cluster Analysis and Unsupervised Machine Learning in Python

Creation Time	Sept. 4, 2023, 10:23 a.m.
Last Access Time	Nov. 22, 2024, 5:11 p.m.
File Size	881.5 MB
Keywords	and Unsupervised Python DesireCourse - Analysis Udemy Machine Cluster Learning in Net
Total Requests	55
Total Files	80
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5. Appendix/2. Windows-Focused Environment Setup 2018.mp4 186.3 MB
5. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.vtt 78.3 MB
5. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
5. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
5. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39.0 MB
5. Appendix/11. What order should I take your courses in (part 2).mp4 37.6 MB
3. Hierarchical Clustering/5. Application Donald Trump vs. Hillary Clinton Tweets.mp4 35.3 MB
2. K-Means Clustering/5. Soft K-Means in Python Code.mp4 30.2 MB
4. Gaussian Mixture Models (GMMs)/3. Write a Gaussian Mixture Model in Python Code.mp4 30.1 MB
5. Appendix/10. What order should I take your courses in (part 1).mp4 29.3 MB
3. Hierarchical Clustering/4. Application Evolution.mp4 26.4 MB
2. K-Means Clustering/12. K-Means Application Finding Clusters of Related Words.mp4 26.0 MB
2. K-Means Clustering/3. Soft K-Means.mp4 25.3 MB
5. Appendix/4. How to Code by Yourself (part 1).mp4 24.5 MB
5. Appendix/6. How to Succeed in this Course (Long Version).mp4 18.3 MB
2. K-Means Clustering/7. Examples of where K-Means can fail.mp4 17.0 MB
5. Appendix/5. How to Code by Yourself (part 2).mp4 14.8 MB
2. K-Means Clustering/1. An Easy Introduction to K-Means Clustering.mp4 12.6 MB
3. Hierarchical Clustering/3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.mp4 11.9 MB
2. K-Means Clustering/9. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).mp4 11.4 MB
2. K-Means Clustering/10. Using K-Means on Real Data MNIST.mp4 10.7 MB
2. K-Means Clustering/11. One Way to Choose K.mp4 9.1 MB
5. Appendix/9. Python 2 vs Python 3.mp4 7.8 MB
1. Introduction to Unsupervised Learning/2. What is unsupervised learning used for.mp4 7.6 MB
1. Introduction to Unsupervised Learning/3. Why Use Clustering.mp4 6.6 MB
3. Hierarchical Clustering/2. Agglomerative Clustering Options.mp4 6.2 MB
5. Appendix/1. What is the Appendix.mp4 5.5 MB
2. K-Means Clustering/6. Visualizing Each Step of K-Means.mp4 5.3 MB
4. Gaussian Mixture Models (GMMs)/1. Description of the Gaussian Mixture Model and How to Train a GMM.mp4 5.2 MB
4. Gaussian Mixture Models (GMMs)/4. Practical Issues with GMM Singular Covariance.mp4 5.0 MB
2. K-Means Clustering/2. Visual Walkthrough of the K-Means Clustering Algorithm.mp4 4.9 MB
3. Hierarchical Clustering/1. Visual Walkthrough of Agglomerative Hierarchical Clustering.mp4 4.4 MB
1. Introduction to Unsupervised Learning/1. Introduction and Outline.mp4 4.1 MB
2. K-Means Clustering/8. Disadvantages of K-Means Clustering.mp4 3.9 MB
4. Gaussian Mixture Models (GMMs)/5. Kernel Density Estimation.mp4 3.7 MB
4. Gaussian Mixture Models (GMMs)/6. Expectation-Maximization.mp4 3.5 MB
1. Introduction to Unsupervised Learning/4. How to Succeed in this Course.mp4 3.3 MB
2. K-Means Clustering/4. The K-Means Objective Function.mp4 3.0 MB
4. Gaussian Mixture Models (GMMs)/2. Comparison between GMM and K-Means.mp4 3.0 MB
4. Gaussian Mixture Models (GMMs)/7. Future Unsupervised Learning Algorithms You Will Learn.mp4 2.0 MB
5. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 27.8 KB
5. Appendix/11. What order should I take your courses in (part 2).vtt 20.2 KB
5. Appendix/4. How to Code by Yourself (part 1).vtt 19.8 KB
5. Appendix/2. Windows-Focused Environment Setup 2018.vtt 17.4 KB
3. Hierarchical Clustering/5. Application Donald Trump vs. Hillary Clinton Tweets.vtt 16.9 KB
3. Hierarchical Clustering/4. Application Evolution.vtt 14.3 KB
5. Appendix/10. What order should I take your courses in (part 1).vtt 14.1 KB
5. Appendix/6. How to Succeed in this Course (Long Version).vtt 12.8 KB
5. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.4 KB
5. Appendix/5. How to Code by Yourself (part 2).vtt 11.6 KB
2. K-Means Clustering/1. An Easy Introduction to K-Means Clustering.vtt 8.3 KB
2. K-Means Clustering/9. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).vtt 8.1 KB
2. K-Means Clustering/12. K-Means Application Finding Clusters of Related Words.vtt 7.4 KB
2. K-Means Clustering/5. Soft K-Means in Python Code.vtt 6.9 KB
4. Gaussian Mixture Models (GMMs)/3. Write a Gaussian Mixture Model in Python Code.vtt 6.9 KB
2. K-Means Clustering/10. Using K-Means on Real Data MNIST.vtt 6.3 KB
2. K-Means Clustering/3. Soft K-Means.vtt 6.2 KB
5. Appendix/9. Python 2 vs Python 3.vtt 5.4 KB
1. Introduction to Unsupervised Learning/2. What is unsupervised learning used for.vtt 5.3 KB
1. Introduction to Unsupervised Learning/3. Why Use Clustering.vtt 5.2 KB
3. Hierarchical Clustering/2. Agglomerative Clustering Options.vtt 4.9 KB
2. K-Means Clustering/7. Examples of where K-Means can fail.vtt 4.5 KB
2. K-Means Clustering/11. One Way to Choose K.vtt 4.5 KB
3. Hierarchical Clustering/3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.vtt 3.9 KB
4. Gaussian Mixture Models (GMMs)/4. Practical Issues with GMM Singular Covariance.vtt 3.6 KB
1. Introduction to Unsupervised Learning/4. How to Succeed in this Course.vtt 3.5 KB
4. Gaussian Mixture Models (GMMs)/1. Description of the Gaussian Mixture Model and How to Train a GMM.vtt 3.3 KB
2. K-Means Clustering/2. Visual Walkthrough of the K-Means Clustering Algorithm.vtt 3.3 KB
5. Appendix/1. What is the Appendix.vtt 3.3 KB
1. Introduction to Unsupervised Learning/1. Introduction and Outline.vtt 3.2 KB
3. Hierarchical Clustering/1. Visual Walkthrough of Agglomerative Hierarchical Clustering.vtt 3.2 KB
2. K-Means Clustering/8. Disadvantages of K-Means Clustering.vtt 3.0 KB
4. Gaussian Mixture Models (GMMs)/5. Kernel Density Estimation.vtt 2.9 KB
4. Gaussian Mixture Models (GMMs)/6. Expectation-Maximization.vtt 2.4 KB
2. K-Means Clustering/6. Visualizing Each Step of K-Means.vtt 2.4 KB
4. Gaussian Mixture Models (GMMs)/2. Comparison between GMM and K-Means.vtt 2.0 KB
2. K-Means Clustering/4. The K-Means Objective Function.vtt 1.9 KB
4. Gaussian Mixture Models (GMMs)/7. Future Unsupervised Learning Algorithms You Will Learn.vtt 1.3 KB
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[DesireCourse.Net] Udemy - Cluster Analysis and Unsupervised Machine Learning in Python

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