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

1. 1. Introduction to Unsupervised Learning/1. Introduction and Outline.mp44.11MB
2. 1. Introduction to Unsupervised Learning/2. What is unsupervised learning used for.mp47.58MB
3. 1. Introduction to Unsupervised Learning/3. Why Use Clustering.mp46.64MB
4. 1. Introduction to Unsupervised Learning/4. How to Succeed in this Course.mp43.3MB
5. 2. K-Means Clustering/1. An Easy Introduction to K-Means Clustering.mp412.56MB
6. 2. K-Means Clustering/10. Using K-Means on Real Data MNIST.mp410.71MB
7. 2. K-Means Clustering/11. One Way to Choose K.mp49.08MB
8. 2. K-Means Clustering/12. K-Means Application Finding Clusters of Related Words.mp425.99MB
9. 2. K-Means Clustering/2. Visual Walkthrough of the K-Means Clustering Algorithm.mp44.87MB
10. 2. K-Means Clustering/3. Soft K-Means.mp425.25MB
11. 2. K-Means Clustering/4. The K-Means Objective Function.mp43.02MB
12. 2. K-Means Clustering/5. Soft K-Means in Python Code.mp430.21MB
13. 2. K-Means Clustering/6. Visualizing Each Step of K-Means.mp45.26MB
14. 2. K-Means Clustering/7. Examples of where K-Means can fail.mp417MB
15. 2. K-Means Clustering/8. Disadvantages of K-Means Clustering.mp43.87MB
16. 2. K-Means Clustering/9. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).mp411.39MB
17. 3. Hierarchical Clustering/1. Visual Walkthrough of Agglomerative Hierarchical Clustering.mp44.4MB
18. 3. Hierarchical Clustering/2. Agglomerative Clustering Options.mp46.23MB
19. 3. Hierarchical Clustering/3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.mp411.86MB
20. 3. Hierarchical Clustering/4. Application Evolution.mp426.4MB
21. 3. Hierarchical Clustering/5. Application Donald Trump vs. Hillary Clinton Tweets.mp435.28MB
22. 4. Gaussian Mixture Models (GMMs)/1. Description of the Gaussian Mixture Model and How to Train a GMM.mp45.23MB
23. 4. Gaussian Mixture Models (GMMs)/2. Comparison between GMM and K-Means.mp43MB
24. 4. Gaussian Mixture Models (GMMs)/3. Write a Gaussian Mixture Model in Python Code.mp430.12MB
25. 4. Gaussian Mixture Models (GMMs)/4. Practical Issues with GMM Singular Covariance.mp44.96MB
26. 4. Gaussian Mixture Models (GMMs)/5. Kernel Density Estimation.mp43.71MB
27. 4. Gaussian Mixture Models (GMMs)/6. Expectation-Maximization.mp43.5MB
28. 4. Gaussian Mixture Models (GMMs)/7. Future Unsupervised Learning Algorithms You Will Learn.mp41.95MB
29. 5. Appendix/1. What is the Appendix.mp45.45MB
30. 5. Appendix/10. What order should I take your courses in (part 1).mp429.33MB
31. 5. Appendix/11. What order should I take your courses in (part 2).mp437.63MB
32. 5. Appendix/2. Windows-Focused Environment Setup 2018.mp4186.31MB
33. 5. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB
34. 5. Appendix/4. How to Code by Yourself (part 1).mp424.53MB
35. 5. Appendix/5. How to Code by Yourself (part 2).mp414.81MB
36. 5. Appendix/6. How to Succeed in this Course (Long Version).mp418.31MB
37. 5. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95MB
38. 5. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp478.29MB
39. 5. Appendix/9. Python 2 vs Python 3.mp47.84MB