Introduction
Previously, we have understood the core concepts of supervised machine learning and how to use most of the common implementations of many preprocessing techniques, models, etc. with scikit-learn. In this lesson, we will be covering most approaches and techniques used for unsupervised learning using the power of scikit-learn.
As a recap, Scikit-learn tries to divide everything related to Machine Learning into the following categories.
Supervised Learning:
a. Classification Problem
b. Regression Problem
Unsupervised Learning
a. Clustering
b. Dimensionality Reduction
c. Density Estimation
Based on these classifications, sklearn has tried a uniform approach for all the data, transformation, preprocessing, and models. In the previous lesson, we have gone through the supervised learning part in this category. In this lesson, we will have a look at unsupervised learning. Let us discuss each of these one by one.
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