Unsupervised Machine Learning in Python | Decorrelating Your Data and Dimension Reduction

In this video, we'll learn about the most fundamental of dimension reduction techniques, "Principal Component Analysis" ("PCA"). PCA is often used before supervised learning to improve model performance and generalization. It can also be useful for unsupervised learning

Datasets
https://drive.google.com/drive/folders/1GoJyEr24PLzui_OCVLPITMljlum2v8RD?usp=sharing

Links
Python: https://www.python.org/downloads/
PyCharm: https://www.jetbrains.com/pycharm/download/#section=windows
scikit-learn: https://pypi.org/project/scikit-learn/
pandas: https://pypi.org/project/pandas/
numpy: https://pypi.org/project/numpy/
matplotlib: https://pypi.org/project/matplotlib/

Chapters
0:00 Course Introduction
0:41 Chapter Introduction
00:58 Visualizing the PCA formation
4:12 Intrinsic dimension
7:15 Dimension reduction with PCA

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