Unsupervised learning can benefit greatly from PCA. In this video, learn how to perform PCA using the Python library scikit-learn to speed up machine learning algorithms.
- Do you want to speed up the fitting … of your machine learning algorithm? … Second learn offers quite a few ways to do this. … One way is to train your model in parallel using n_jobs … parameter, which exists for many psychic learn models. … A really simple way is to reduce the number of columns or … rows in your data. … The problem with this approach is it's hard to know which … rows and especially which columns to remove. … Principle Component Analysis, … commonly known as PCA is a technique that you can use to … smartly reduce the dimensionality … in your data while losing the … least amount of information possible. … In this video, … I'll share with you the process of how you can use PCA to … split the fitting of a logistic regression model. … The first step is to import libraries. … The next step is loaded dataset. … The dataset is a modified version of the MNIST dataset … that contains 2000 labeled images … of each digit zero and one, … the images are 28 pixels by 28 pixels. For your convenience, …
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- Why use scikit-learn?
- Supervised vs. unsupervised learning
- Linear and logistic regression
- Decision trees and random forests
- K-means clustering
- Principal component analysis (PCA)