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[FreeCourseSite.com] Udemy - Cluster Analysis and Unsupervised Machine Learning in Python
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文档列表
5. Appendix/2. Windows-Focused Environment Setup 2018.mp4
195.4 MB
5. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.vtt
82.1 MB
5. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp4
82.1 MB
5. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4
46.1 MB
5. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4
40.8 MB
5. Appendix/11. What order should I take your courses in (part 2).mp4
39.5 MB
3. Hierarchical Clustering/5. Application Donald Trump vs. Hillary Clinton Tweets.mp4
37.0 MB
2. K-Means Clustering/5. Soft K-Means in Python Code.mp4
31.7 MB
4. Gaussian Mixture Models (GMMs)/3. Write a Gaussian Mixture Model in Python Code.mp4
31.6 MB
5. Appendix/10. What order should I take your courses in (part 1).mp4
30.8 MB
3. Hierarchical Clustering/4. Application Evolution.mp4
27.7 MB
2. K-Means Clustering/12. K-Means Application Finding Clusters of Related Words.mp4
27.2 MB
2. K-Means Clustering/3. Soft K-Means.mp4
26.5 MB
5. Appendix/4. How to Code by Yourself (part 1).mp4
25.7 MB
5. Appendix/6. How to Succeed in this Course (Long Version).mp4
19.2 MB
2. K-Means Clustering/7. Examples of where K-Means can fail.mp4
17.8 MB
5. Appendix/5. How to Code by Yourself (part 2).mp4
15.5 MB
2. K-Means Clustering/1. An Easy Introduction to K-Means Clustering.mp4
13.2 MB
3. Hierarchical Clustering/3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.mp4
12.4 MB
2. K-Means Clustering/9. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).mp4
11.9 MB
2. K-Means Clustering/10. Using K-Means on Real Data MNIST.mp4
11.2 MB
2. K-Means Clustering/11. One Way to Choose K.mp4
9.5 MB
5. Appendix/9. Python 2 vs Python 3.mp4
8.2 MB
1. Introduction to Unsupervised Learning/2. What is unsupervised learning used for.mp4
7.9 MB
1. Introduction to Unsupervised Learning/3. Why Use Clustering.mp4
7.0 MB
3. Hierarchical Clustering/2. Agglomerative Clustering Options.mp4
6.5 MB
5. Appendix/1. What is the Appendix.mp4
5.7 MB
2. K-Means Clustering/6. Visualizing Each Step of K-Means.mp4
5.5 MB
4. Gaussian Mixture Models (GMMs)/1. Description of the Gaussian Mixture Model and How to Train a GMM.mp4
5.5 MB
4. Gaussian Mixture Models (GMMs)/4. Practical Issues with GMM Singular Covariance.mp4
5.2 MB
2. K-Means Clustering/2. Visual Walkthrough of the K-Means Clustering Algorithm.mp4
5.1 MB
3. Hierarchical Clustering/1. Visual Walkthrough of Agglomerative Hierarchical Clustering.mp4
4.6 MB
1. Introduction to Unsupervised Learning/1. Introduction and Outline.mp4
4.3 MB
2. K-Means Clustering/8. Disadvantages of K-Means Clustering.mp4
4.1 MB
4. Gaussian Mixture Models (GMMs)/5. Kernel Density Estimation.mp4
3.9 MB
4. Gaussian Mixture Models (GMMs)/6. Expectation-Maximization.mp4
3.7 MB
1. Introduction to Unsupervised Learning/4. How to Succeed in this Course.mp4
3.5 MB
2. K-Means Clustering/4. The K-Means Objective Function.mp4
3.2 MB
4. Gaussian Mixture Models (GMMs)/2. Comparison between GMM and K-Means.mp4
3.2 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
28.4 kB
5. Appendix/11. What order should I take your courses in (part 2).vtt
20.7 kB
5. Appendix/4. How to Code by Yourself (part 1).vtt
20.3 kB
5. Appendix/2. Windows-Focused Environment Setup 2018.vtt
17.8 kB
3. Hierarchical Clustering/5. Application Donald Trump vs. Hillary Clinton Tweets.vtt
17.3 kB
3. Hierarchical Clustering/4. Application Evolution.vtt
14.7 kB
5. Appendix/10. What order should I take your courses in (part 1).vtt
14.4 kB
5. Appendix/6. How to Succeed in this Course (Long Version).vtt
13.1 kB
5. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt
12.7 kB
5. Appendix/5. How to Code by Yourself (part 2).vtt
11.9 kB
2. K-Means Clustering/1. An Easy Introduction to K-Means Clustering.vtt
8.5 kB
2. K-Means Clustering/9. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).vtt
8.3 kB
2. K-Means Clustering/12. K-Means Application Finding Clusters of Related Words.vtt
7.5 kB
2. K-Means Clustering/5. Soft K-Means in Python Code.vtt
7.1 kB
4. Gaussian Mixture Models (GMMs)/3. Write a Gaussian Mixture Model in Python Code.vtt
7.0 kB
2. K-Means Clustering/10. Using K-Means on Real Data MNIST.vtt
6.4 kB
2. K-Means Clustering/3. Soft K-Means.vtt
6.3 kB
5. Appendix/9. Python 2 vs Python 3.vtt
5.5 kB
1. Introduction to Unsupervised Learning/2. What is unsupervised learning used for.vtt
5.4 kB
1. Introduction to Unsupervised Learning/3. Why Use Clustering.vtt
5.3 kB
3. Hierarchical Clustering/2. Agglomerative Clustering Options.vtt
5.0 kB
2. K-Means Clustering/7. Examples of where K-Means can fail.vtt
4.6 kB
2. K-Means Clustering/11. One Way to Choose K.vtt
4.6 kB
3. Hierarchical Clustering/3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.vtt
4.0 kB
4. Gaussian Mixture Models (GMMs)/4. Practical Issues with GMM Singular Covariance.vtt
3.7 kB
1. Introduction to Unsupervised Learning/4. How to Succeed in this Course.vtt
3.6 kB
4. Gaussian Mixture Models (GMMs)/1. Description of the Gaussian Mixture Model and How to Train a GMM.vtt
3.4 kB
2. K-Means Clustering/2. Visual Walkthrough of the K-Means Clustering Algorithm.vtt
3.4 kB
5. Appendix/1. What is the Appendix.vtt
3.4 kB
1. Introduction to Unsupervised Learning/1. Introduction and Outline.vtt
3.3 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
3.0 kB
4. Gaussian Mixture Models (GMMs)/6. Expectation-Maximization.vtt
2.5 kB
2. K-Means Clustering/6. Visualizing Each Step of K-Means.vtt
2.5 kB
4. Gaussian Mixture Models (GMMs)/2. Comparison between GMM and K-Means.vtt
2.1 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
[FCS Forum].url
133 Bytes
[FreeCourseSite.com].url
127 Bytes
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