Python 初級 - 機器學習：scikit-learn - clustering + dimensionality reduction 聚類+降維|AI|人工智能|數據分析|教學|廣東話 Video

Patreon: https://www.patreon.com/kfsoft

Practical Machine Learning with scikit-learn (predictive data analysis)
1) Clustering
- K-means
- DBSCAN
2) Dimensionality Reduction
- PCA, Kernel PCA, t-SNE

Sources:
https://github.com/learn10kYear/learn-pandas/blob/master/sklearn4/sk-clustering.ipynb
https://github.com/learn10kYear/learn-pandas/blob/master/sklearn4/sk-dimensionality-reduction.ipynb

00:00 Introduction
02:11 PART 1a: K-means
06:35 Generate dataset
08:07 K-means training & prediction
14:42 Silhouette score - find out the best k value
19:12 K-means application: color quantization - color space clustering
35:12 PART 1b: DBSCAN
35:23 Generate dataset
36:42 How DBSCAN work
39:41 DBSCAN training & prediction
43:54 Mixed workflow: Use DBSCAN results for further classification of new points
48:25 PART 2: Dimensionality reduction
50:43 Principle component analysis (PCA)
53:18 PCA - explained variance ratio
59:40 Preprocessing using PCA - just keep some components (converted to lower dimensions)
01:07:51 Classification on lower dimensional data
01:10:51 Visualization using PCA, Kernel PCA, t-SNE
01:17:55 Summary & conclusion


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