| Packt Mastering Unsupervised Learning with Python/03.Manifold and Deep Learning for High-Dimensional Data/0304.Manifold Learning in Practice – Part 2.mp4 |
78.5 MB |
| Packt Mastering Unsupervised Learning with Python/01.Advanced Clustering Methods Selecting the Best Algorithm/0102.Alternatives to K-Means Clustering – Part 1.mp4 |
34.2 MB |
| Packt Mastering Unsupervised Learning with Python/01.Advanced Clustering Methods Selecting the Best Algorithm/0103.Alternatives to K-Means Clustering – Part 2.mp4 |
29 MB |
| Packt Mastering Unsupervised Learning with Python/01.Advanced Clustering Methods Selecting the Best Algorithm/0104.Agglomerative Clustering Finding Natural Hierarchies – Part 1.mp4 |
49.2 MB |
| Packt Mastering Unsupervised Learning with Python/01.Advanced Clustering Methods Selecting the Best Algorithm/0105.Agglomerative Clustering Finding Natural Hierarchies –Part 2.mp4 |
44.2 MB |
| Packt Mastering Unsupervised Learning with Python/01.Advanced Clustering Methods Selecting the Best Algorithm/0106.Density-Based Clustering DBSCAN and HDBSCAN – Part 1.mp4 |
37.7 MB |
| Packt Mastering Unsupervised Learning with Python/01.Advanced Clustering Methods Selecting the Best Algorithm/0107.Density-Based Clustering DBSCAN and HDBSCAN – Part 2.mp4 |
21.5 MB |
| Packt Mastering Unsupervised Learning with Python/01.Advanced Clustering Methods Selecting the Best Algorithm/0108.Gaussian Mixture Models.mp4 |
42 MB |
| Packt Mastering Unsupervised Learning with Python/02.Topic Modeling Semantic Content Recommendations/0201.Topic Modeling Overview – Part 1.mp4 |
34.4 MB |
| Packt Mastering Unsupervised Learning with Python/02.Topic Modeling Semantic Content Recommendations/0202.Topic Modeling Overview – Part 2.mp4 |
43.4 MB |
| Packt Mastering Unsupervised Learning with Python/02.Topic Modeling Semantic Content Recommendations/0203.Topic Modeling Preparing Your Data – Part 1.mp4 |
26.6 MB |
| Packt Mastering Unsupervised Learning with Python/02.Topic Modeling Semantic Content Recommendations/0204.Topic Modeling Preparing Your Data – Part 2.mp4 |
27 MB |
| Packt Mastering Unsupervised Learning with Python/02.Topic Modeling Semantic Content Recommendations/0205.Topic Modeling Running the Models – Part 1.mp4 |
28.3 MB |
| Packt Mastering Unsupervised Learning with Python/02.Topic Modeling Semantic Content Recommendations/0206.Topic Modeling Running the Models – Part 2.mp4 |
32.3 MB |
| Packt Mastering Unsupervised Learning with Python/02.Topic Modeling Semantic Content Recommendations/0207.Topic Modeling Evaluating and Visualizing Results.mp4 |
30 MB |
| Packt Mastering Unsupervised Learning with Python/03.Manifold and Deep Learning for High-Dimensional Data/0301.Manifold Learning Introduction – Part 1.mp4 |
26 MB |
| Packt Mastering Unsupervised Learning with Python/03.Manifold and Deep Learning for High-Dimensional Data/0302.Manifold Learning Introduction – Part 2.mp4 |
22.5 MB |
| Packt Mastering Unsupervised Learning with Python/03.Manifold and Deep Learning for High-Dimensional Data/0303.Manifold Learning in Practice – Part 1.mp4 |
26.4 MB |
| Packt Mastering Unsupervised Learning with Python/01.Advanced Clustering Methods Selecting the Best Algorithm/0101.The Course Overview.mp4 |
284 B |
| Packt Mastering Unsupervised Learning with Python/03.Manifold and Deep Learning for High-Dimensional Data/0305.Visualize High-Dimensional Data t-SNE and UMAP – Part 1.mp4 |
28.4 MB |
| Packt Mastering Unsupervised Learning with Python/03.Manifold and Deep Learning for High-Dimensional Data/0306.Visualize High-Dimensional Data t-SNE and UMAP – Part 2.mp4 |
32.4 MB |
| Packt Mastering Unsupervised Learning with Python/03.Manifold and Deep Learning for High-Dimensional Data/0307.Deep Learning and Visualization Autoencoders and t-SNE – Part 1.mp4 |
30.9 MB |
| Packt Mastering Unsupervised Learning with Python/03.Manifold and Deep Learning for High-Dimensional Data/0308.Deep Learning and Visualization Autoencoders and t-SNE – Part 2.mp4 |
41.2 MB |
| Packt Mastering Unsupervised Learning with Python/Exercise Files/exercise_files.zip |
68 MB |
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