[FreeCourseSite.com] Udemy - Cluster Analysis and Unsupervised Machine Learning in Python

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[FreeCourseSite.com] Udemy - Cluster Analysis and Unsupervised Machine Learning in Python

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种子哈希：1a8ef507683771cb73c24d933cbc3cea55310e31
文件大小：881.47M
已经下载：886次
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收录时间：2021-03-17
最近下载：2025-07-20

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文件列表

  • 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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